Air Diffusion and Solid Contaminant Behaviour in Room Ventilation a CFD Based Integrated Approach

Transcription

1 Ai Diffusion and Solid Contaminant Behaviou in Room Ventilation a CFD Based Integated Appoach Doctoal Thesis Gey Einbeg June 2005 Kungliga Tekniska Högskolan The Royal Institute of Technology Depatment of Enegy Technology Division of Heat and Powe Technology & Depatment of Constuctional Engineeing and Design, Technology and Health, KTH South

2 TRITA-KRV ISSN 1100/7990 ISRN KTH-KRV-R-05-3-SE ISBN

3 ABSTRACT One of the most fundamental human needs is fesh ai. It has been estimated that people spend compaatively much time in indoo pemises. That ceates an elevated need fo high-quality ventilation systems in buildings. The ventilation aiflow ate is ecognised as the main paamete fo measuing the indoo ai quality. It has been shown that the ventilation aiflow ates have effects on espiatoy diseases, on sick building syndome symptoms, on poductivity and peceived ai quality. Ventilation is necessay to emove indoo-geneated pollutants by diluting these to an acceptable level. The choice of ventilation aiflow ate is often based on noms o standads in which the aiflow ate is detemined based on epidemiological eseach and field o laboatoy measuements. Howeve, the detemination of ventilation flow ate is fa moe complex. Indoo ai quality in the occupied zone can be dependent of many factos such as outdoo ai quality, aiflow ate, indoo geneation of pollutants, moistue content, themal envionment and how the ai is supplied into the human occupied zone. One needs to acknowledge the impotance of ai distibution which clealy affects the comfot of occupants. To design a ventilation system which consides all aspects of oom ventilation can only be achieved by compute modelling. The objective of this thesis is to investigate ai diffusion, indoo ai quality and comfot issues by CFD (computational fluid dynamics) modelling. The cucial pat of the CFD modelling is to adopt BCs (bounday conditions) fo a successful and accuate modelling pocedue. Assessing the CFD simulations by validated BCs enabled constucting the ventilation system vitually and vaious system layouts wee tested to meet given design citeia. In paallel, full-scale measuements wee conducted to validate the diffuse models and the implemented simplified paticle-settling model. Both the simulations and the measuements eveal the full complexity of ai diffusion coupled with solid contaminants. The ai supply method is an impotant facto fo distibution of heat, ai velocity and solid contaminants. The influence of ai supply diffuse location, contaminant souce location and ai supply method was tested both numeically and by measuements to investigate the influence of diffeent paametes on the efficiency of oom ventilation. As example of this, the well-known displacement ventilation is not fully able to evacuate lage 10 µm aibone paticles fom a oom. Ventilation should contol the conditions in the human beathing zone and theefoe the ventilation efficiency is an impotant paamete. A popely designed ventilation system could use less fesh ai to maintain an acceptable level of contaminant concentation in the human beathing zone. That is why complete mixing of ai is not ecommended as the ventilation efficiency is low and the necessay aiflow ate is elatively high compaed to othe ventilation stategies. Especially buoyancy-diven aiflows fom heat souces ae an impotant pat of ventilation and should not be hampeed by supply aiflow fom the diffuses. All the esults evealed that CFD pesently is the only eliable method fo optimising a ventilation system consideing the ai diffusion and contaminant level in all locations of any kind of oom. The last pat of the thesis addesses the possibility to integate the CFD modelling into a building design pocess whee achitectual space geomety, themal simulations and diffuse BCs could be embedded into a nomal building design poject. KEYWORDS: CFD modelling, aiflow ate, ventilation efficiency, diffuses, solid contaminants, IAQ 3

4 4 Ai Diffusion and Solid Contaminant Behaviou in Room Ventilation a CFD Based Integated Appoach

5 PREFACE This thesis is submitted in accodance with the conditions fo attaining the PhD degee at KTH (The Royal Institute of Technology). The wok pesented in the thesis has been caied out as a collaboation wok between KTH South Depatment of Constuctional Engineeing and Design and KTH Depatment of Enegy Technology as well as industy patnes, i.e. fome ABB Ventilation (now Fläkt Woods), Halton OY and Olof Ganlund OY. The following wok was funded by the industial patnes and stated with a poject Impoved Ventilation and Filtation. The main focus of this poject was on aibone paticle contol, themal comfot, enegy use and healthy indoo conditions. The thesis has been completed with the help of many individuals and I wish to expess my sincee gatitude to my supeviso Stue Holmbeg and all collaboation patnes who paticipated in this poject. Above all I want to thank Reijo Hänninen who aanged a way to finance my studies duing the yeas Special thanks goes to my many co-authos Hannu Koskela, Kim Hagstöm, Panu Mustakallio, Tuomas Laine fo making this thesis a fuitful expeience. My fellow doctoal students at KTH Syd need special ecognition and I encouage them to go on with thei eseach. I also want to thank my colleagues at KTH South, R&D staff at Halton OY and Olof Ganlund OY who have helped me moally and financially duing my PhD studies. The language of the summay and the appended papes in the thesis wee mainly checked by Chistina Hönell at KTH who made my English moe undestandable and easie to ead. Finally I want to expess my gatitude to my family and fiends. My wife Silja and siste Iene have helped me vey much duing 3½ yeas of PhD studies. Thee have been some ups and downs duing this time, when sometimes I did not believe myself to be able to finish. Nevetheless my 22 yeas of school time have come to an end and this thesis is a poof of what I have leaned duing that time. Most of the wok done duing the PhD studies is uploaded on my pesonal homepage at Gey Einbeg Apil

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7 TABLE OF CONTENTS ABSTRACT...3 PREFACE...5 TABLE OF CONTENTS...7 TABLE OF FIGURES...9 ABBREVIATIONS...11 NOMENCLATURE...13 LIST OF PUBLICATIONS INTRODUCTION SUMMARY OF PAPER CONTRIBUTIONS OBJECTIVES THE IMPORTANCE OF MODELLING THE INDOOR CLIMATE Ventilation and ai supply pinciples Mixing ventilation Displacement ventilation Piston ventilation Zoning stategy The evaluation of IAQ Detemination of the necessay aiflow ate Ventilation efficiency of contaminant emoval Concentation Heat emoval efficiency Daught Othe comfot evaluation equations IAQ assessment paticles in indoo ai Chaacteistics of paticles Gavitational settling Deposition METHODS Numeical simulation of IAQ paametes by CFD Geneal bounday conditions of the thesis Validations using expeiments and liteatue Numeical simulation of ventilation aiflows coupled with paticles Mass consevation equation Momentum consevation equation Enegy consevation equation Paticle concentation equation Tubulence modelling with the k-ε model DIFFERENT COMPONENTS WITHIN THE MODELLING AND BOUNDARY CONDITIONS D space model Poducts within the modelling Intenal heat and cold souces Numeical modelling of supply openings Tubulence quantities fo k and ε at supply openings

8 6.5 Computational gid Exhaust opening Modelling of solid contaminants and thei souce(s) Modelling of paticle behaviou souces & sinks Finite volume method and discetization Walls and solid boundaies CFD modelling based on integated design pocess RESULTS Liteatue studies Full-scale laboatoy measuements Simulation esults Quality and evaluation of the esults Accuacy of the esults GENERAL DISCUSSION AND CONCLUSIONS REFERENCES...77 APPENDIX A...83 APPENDIX B...85 APPENDIX C...89 APPENDIX D...91 APPENDED PAPERS

9 TABLE OF FIGURES Fig. 1. Data flow in the CFD modelling pocess in connection with eo possibility...24 Fig. 2. Mixing ventilation and modelling paametes investigated in this thesis at steady-state conditions...29 Fig. 3. Displacement ventilation and modelling paametes investigated in this thesis at steady-state conditions...30 Fig. 4. Zoning ventilation configuation with the diffuses and convective heat souces geneating the plumes. It is impotant that the occupied zone and the uppe contaminant zone ae not mixed fo efficient system solutions.31 Fig. 5. Scanning electon micoscopy micogaphs and size fequency histogams fo the two factions (a) PM 2.5 (fine) and (b) PM (coase) (Diociaiuti et al., 2001)...37 Fig. 6. The main chaacteistics of paticles mass, size numbe distibution, settling velocity, aeodynamic diamete and behaviou in ai...38 Fig. 7. Change of paticle size distibution with elative humidity and new modelling concept...38 Fig. 8. Dag coefficient fo diffeent Reynolds numbes...40 Fig. 9. Paticle teminal settling velocity and diffeent types of paticles found in ambient ai, see Pape I...41 Fig. 10. The vaiation of aeosol concentation (paticles 0.26 µm) in the test oom in Pape II. The aveage concentation in the oom was 10 µg/m Fig. 11. Basic CFD modelling set-up vaiables...51 Fig. 12. Low velocity diffuse, used in Papes I & VIII...53 Fig. 13. Industial ai diffuse geomety (A.) and CFD epesentation (B.), Pape V...54 Fig. 14. Ai velocity pofile on the bounday of a multi-cone diffuse...55 Fig. 15. High induction swil diffuse and CFD simplified geomety model to the ight, Pape VI...56 Fig. 16. Gid layout nea an industial ai diffuse...57 Fig. 17. Settling paticles e-enteing convective ai plumes...60 Fig. 18. Ai and paticle behaviou aound a human body. Paticle goups ae indicated by symbols 1, 2 and Fig. 19. CFD simulation pocedue based on an integated design pocess...64 Fig. 20. Full-scale laboatoy oom fo measuements and numeical simulations...68 Fig. 21. The paticle geneato located in the test chambe...69 Fig. 22. Laboatoy test hall in the Finnish Institute of Occupational Health, see also Papes V-VII...69 Fig. 23. Ai diffusion in the laboatoy oom in Fig. 22 ventilated with industial ai diffuses...71 Fig. 24. Tempeatue distibution in the laboatoy oom in Fig. 22 ventilated with industial ai diffuses

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11 ABBREVIATIONS AC ai conditioning ACH ai exchange ate CAD compute aided design IAQ indoo ai quality 3D thee dimensional ATD ai teminal device BC bounday conditions (plual BCs) CFD computational fluid dynamics DNS diect numeical simulation HVAC heating, ventilation and ai conditioning IFC industy foundation classes nd non-dimensional PC pesonal compute PM paticulate matte PM 2.5 paticulate matte < 2.5 µm PM 10 paticulate matte < 10 µm PMV pedicted mean vote PPD pedicted pecentage of dissatisfied RH elative humidity RSP espiable suspended paticles UFP ulta fine paticles TSP total suspended paticles VOC volatile oganic compounds 11

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13 NOMENCLATURE a acceleation (m/s 2 ) A aea (m 2 ) A Achimedes numbe (nd) C concentation (kg/m 3 ) C d dimensionless dag coefficient (nd) d diamete (m) DR daught ate (%) E enegy (W) F momentum (foce) (N) g acceleation due to gavity (m/s 2 ) G Gashof numbe (atio of buoyancy foces to viscous foces, nd) H height (m) h enthalpy (kj/kg) h j enthalpy of species j (kj/kg) J j diffusion flux of species j (kg/m 2 s) K coection facto descibing the paticle gowth in humid ai (nd) k tubulent kinetic enegy (J/kg) l tubulent length scale (m) l k Kolmogoov length scale (m) m& mass flow ate of contaminants (kg/s) m mass of contaminants (kg) N numbe (paticles, cells etc., nd) o coefficient fo discetization equation (nd) P heat tansfe fom heat souces (W) p pessue (N/m 2 o Pa) Q aiflow ate (m 3 /s) Q & mass flow ate (kg/s) R unde-elaxation facto (nd) R φ esidual of scala quantity (nd) Re Reynolds numbe (nd) S c volumetic souce value (N/m 3 ) S 0 souce tem fo discetization equation (by vaiable) S h souce tem in enegy equation (W/m 3 ) S p elative paticle-settling facto (nd) T tempeatue (K) t time (s) Tu tubulence intensity (%) v velocity (m/s) V volume (m 3 ) v + wall non-dimensional velocity (nd) u, v, w thee velocity components (also witten as v x,v y,v z m/s) x,y,z coodinates (x=y, y=y, z=z) also subscipts non-dimensional length paamete fo walls (nd) y + 13

14 Geek symbols α angle ( ) α c local convective heat tansfe coefficient (W/m 2 K) α d deposition value (kg/s) α k volume faction of phase k (nd) β volume expansion coefficient (1/K) δ standad deviation (by vaiable, fo velocity m/s) s displacement vecto (m/s) ε ate of dissipation of tubulent kinetic enegy (J/kgs) ε C contaminant emoval efficiency (nd) ε T heat emoval efficiency (nd) ε l local ventilation efficiency (nd) ε v ventilation efficiency (nd) φ scala quantity (nd) Γ c, Γ φ diffusion coefficients fo C and φ (m 2 /s) λ themal conductivity (W/mK) µ dynamic viscosity (kg/ms) ρ density (kg/m 3 ) τ time constant (s) υ kinematic viscosity (m 2 /s) Empiical non-dimensional constants fo k-ε tubulence model C µ 0.09 C 1ε depends on flow type C 2ε depends on flow type σ k depends on flow type depends on flow type σ ε Subscipts 1,2,3 paticle index a ai value bz beathing zone value c convective cc cell cente value D dag value f face value g gavitation value hi highest allowed (hamful) value i fee index in supply opening (inlet) value l local value m mixtue value 14

15 n nb nd nw old out oz p pa R ad ef Sink Souce t tot tub w v z nomal value neighbouing point value non-dimensional value nomal gid wall value old value exhaust opening value occupied zone value paticle value paticle ai diffeence value oom value adiation value efeence value sink value souce value time-dependent value total value tubulence value wall value ventilation zone value T φ τ themal diffeence value value fo scala quantity tangential value 15

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17 LIST OF PUBLICATIONS The thesis compises the following nine papes which ae efeed to by thei Roman numeals: I. Einbeg G, Holmbeg S, Paticle Filtation in a Ventilated Room, Indoo Ai 2002, Poceeding of the 9 th Intenational Confeence on Indoo Ai Quality and Climate, Vol [2] H. Levin, ed., Indoo Ai 2002, Santa Cuz, Califonia, pp II. Holmbeg S, Einbeg G, Flow Behaviou in a Ventilated Room measuements and simulations, Roomvent 2002, Poceedings of the 8 th Intenational Confeence on Ai Distibution in Rooms, Copenhagen, Denmak Sept., pp III. Einbeg G, Holmbeg S, Chaacteistics of Paticles and Thei Behaviou in Ventilation Ai, The Intenational Jounal of Ventilation, Volume 2, No 1, June 2003, pp IV. Einbeg G, Holmbeg S, Paticle Removal Efficiency in a Numeical Test Room, ISHVAC 2003, The 4 th Intenational Symposium On Heating, Ventilation and Ai Conditioning, Beijing, China 9-11th Octobe, Volume 1 V. Einbeg G, Hagstöm K, Mustakallio P, Koskela H, Holmbeg S, CFD Modelling of an Industial Ai Diffuse Pedicting Velocity and Tempeatue in the Nea Zone, Building and Envionment 40, 2005, VI. VII. Einbeg G, Koskela H, Holmbeg S, CFD Simulation and Measuements in Nea Zone of High Induction Swil Diffuse, Roomvent 2004, Poceedings of the 9 th Intenational Confeence on Ai Distibution in Rooms, Coimba, Potugal 5 8 Sept. Einbeg G, The Influence of Aiflow Pofile and Heat Souce Location on Heat Removal Efficiency, Accepted fo publication in Enegy and Buildings VIII. Einbeg G, Zonal Model fo Evaluating Paticle Mass Tanspot in a Ventilated Room, Roomvent 2004, Poceedings of the 9 th Intenational Confeence on Ai Distibution in Rooms, Coimba, Potugal 5 8 Sept IX. Einbeg G, Laine T, Holmbeg S, CFD Modelling as a Pat of Integated Design Pocess fo Optimized Indoo Envionment, submitted to Automation in Constuction The autho is the pincipal autho of publications Papes I, III-IX. In Pape II the autho was esponsible fo analysing the measuement esults fom the ABB Enköping laboatoy and simulation esults. The CFD simulations in Papes I & II wee caied out by the co-autho Stue Holmbeg. The measuement esults in Papes II & V-VII wee pefomed as a collaboation wok, which means that actual measuements on site wee caied out by co-authos o collaboatos. 17

18 18 Ai Diffusion and Solid Contaminant Behaviou in Room Ventilation a CFD Based Integated Appoach

19 1 INTRODUCTION In this thesis the main focus is the influence of ai diffusion on themal conditions and aibone paticle concentations. The task is to systemise the many aspects of modelling involved in a building poject. Compute modelling is a vital pat of eveyday design in a building poject. CFD modelling gives an oppotunity to assess the indoo climate by vitually constucting a oom and testing diffeent system layouts to meet the pedetemined design citeia. A successful design of a ventilation system points out the need fo a total system view concening ai diffusion, themal conditions and solid contaminant behaviou. Until now these pats have been pefomed sepaately. Simulating only one pat implies the isk that the designed system meets the equiements of, fo instance, ai velocity level in a human occupied zone, but fails to emove hamful pollutants. Typically the IAQ (indoo ai quality) is assessed by means of total aiflow supplied to the oom, which should not be the only consideed paamete in oom ventilation because the ai supply method and oom intenal configuation as well as heat souces have consideable effects on oom ai diffusion. Additionally ai diffusion has effects on themal conditions, moistue, daught and the concentation of solid contaminants in diffeent pats of the oom. That is why the design of ventilation systems and AC (ai conditioning) should be integated consideing all aspects of IAQ poblems. Given that each discipline has a diffeent view and intepetation of its special pat of the modelling, the autho hopes to contibute with integating these disciplines. The eseach on ai diffuse BCs (bounday conditions) and paticle eseach ae classically done sepaately. Nevetheless, the paticle o contaminant concentation is geatly dependent on ai diffusion in the oom. To model the inteaction of ai diffusion and solid contaminant behaviou one needs validated models fom both calculation disciplines. Moeove, the modelling of indoo envionment elies on many input paametes such as diffuse BCs, wall conditions and models of calculating the concentation of solid contaminants. All the input paametes used in CFD should be caefully detemined and the choice of cetain models o BCs should be well justified. To impove the CFD modelling the autho has intoduced a new concept on how the fluid and contaminant modelling can be coupled with a building s design pocess to optimise the indoo envionment. This is a fundamental way to impove the pe-design of vaious ventilation systems by using eliable numeical input paametes geneated fo CFD simulation in vaious stages of any kind of building poject. In paallel many simplified models of diffeent types of ai diffuses and a dift-flux paticle model with an Euleian appoach wee futhe developed duing the poject. The fist goal fo the poject was to test an ai diffuse which could contol aibone paticle concentation in the occupied zone in a nomal office. The othe goal fo this eseach aimed at developing a new type of ventilation system o configuation whee all contaminants (gases as well as diffeent-sized paticles) should be contolled and efficiently evacuated. It is a wellknown fact that most ventilation and ai conditioning systems ae designed without too much concen about how solid contaminants behave in the ventilation aiflow. Fo displacement ventilation systems designes nomally assume all pollutants to be following the buoyant aiflow into an uppe zone whee they ae evacuated. This is, howeve, seldom the case. Studies show that settling RSP (espiable suspended paticles) ae found in inceasing concentations in the beathing zone whee the exposue concentations can be a health hazad to occupants (Mattsson, 19

20 2002). The question on how ventilation systems should be designed to eliminate RSP fom the beathing zone is hee emphasised. The supply and exhaust conditions of the ventilation aiflow ae shown to play an impotant ole in the ai quality contol. Othe impotant paametes include ai velocity, ai tempeatue, heat load in the oom and paticle chaacteistics. It is also impotant to find out which kind of ai distibution method should be used to educe the paticle concentation in a oom. That is the main eason why this thesis consists of many known eseach topics such as paticle modelling, simplified diffuse BCs fo CFD modelling and the integated appoach fo coupling building enegy pogams, diffuse selection pogams fo the CFD modelling to impove the oveall simulation esults. Cuent eseach pactice lacks this kind of expetise. It is undestandable that to pefom a high-quality CFD paticle simulation one needs to have boad knowledge associated to the diffuse BCs, wall BCs and othe BCs in conjunction with opeating a paticle model within the CFD. The esults fom this thesis hopefully contibute to the impoved knowledge concening seveal aspects of CFD modelling. Thee is still no standadisation o fundamental way to pefom CFD simulations concening choice of diffuses fom the manufactue, although thee is a stong effot to ceate guidelines on how to pefom the simulations, how to veify, validate and epot the CFD modelling esults (Casey & Wintegeste, 2000, Chen & Sebic, 2001, At pesent clea instuctions on how to inset the ai diffuse into the CFD simulation case have not been pesented. Fo instance gid layout and the choice of model teatment such as the pescibed velocity method on the bounday of the diffuse o the box method o something else is entiely an open issue fo the CFD use. As CFD modelling has gained moe populaity it is vey necessay that the choice of sub-models in CFD should be well justified and based on eliable input data. Anothe concen in the pesent wok was to study ventilation configuations numeically by efeence examples and heeafte modify system solutions fo impoved function by using CFD. The IAQ assessment included a minimised isk of high exposue levels in the beathing zone as well as a low isk of coss infection in the oom. The simulations consisted of tasks such as space cooling with the ventilation ai by testing vaious locations of ATDs (ai teminal devices) as well the spatial elationships between the heat souces vs. diffuse location. 20

21 2 SUMMARY OF PAPER CONTRIBUTIONS The main aim of the conducted eseach was to develop and link togethe many aspects of CFD modelling. A significant pat of the wok is devoted to the most impotant pollutant indoos, aibone paticles Papes I-IV & VIII. Many ecent studies have shown that inceased concentation of envionmental PM (paticulate matte) is elated to many espiatoy diseases (Omstad, 2000). To model this is a complicated task and needs many numeical input values. In the following objectives of and contibutions fom the included papes ae highlighted. The main task of Pape I was to find out how paticles behave in a oom with multiple heat souces. Paticle concentation in a oom was modelled by using simplified models of humans and low velocity diffuses. The modelling was achieved with a simple Euleian-appoach diftflux paticle model. Special topics investigated in this pape wee paticle spead patten and souce behaviou. Simulations evealed that the floo can be both paticle sink and souce. The modelled 10 µm paticles followed the buoyant aiflows ceated by the heat souces. This was based on modelling paticle concentation in a classoom with multiple heat souces epesenting pupils. The pape used displacement ventilation and peliminay esults showed that lage paticles, 10 µm, do not follow the ai steams exactly. The pape evealed that paticles tend to accumulate in the space and can be found in the cones of the oom athe than following the main ai steam to the exhaust outlet. Both measuements and numeical simulations of paticle behaviou in a efeence oom with displacement ventilation ae evaluated in Pape II. A compaison between measued and CFD simulated esults was pefomed. The esults indicate that the wall function teatment in the numeical simulations is unable to fully deal with the convective heat tansfe fom solid boundaies. The expeimental wok suffeed fom limitations in geneating low paticle mass flows fom the paticle souce. The expeience fom this investigation shows the impotance of woking with measuements and simulations in paallel. Guidelines on how to combine esults ae discussed in this pape. Accuate contol and good undestanding of diffeent paamete vaiations ae emphasised. The behaviou of paticles in aiflow is impotant fo identifying those in vaious locations in a ventilated space. The main aim of Pape III was to popose a new modelling concept and pefom a liteatue eview about paticle eseach. The new poposed model includes diffeentsized paticles, with ealistic distibution. The paticle diamete fo the model is chosen by the specific behaviou in ai. As most paticle models suffe the limitation of non-existing distibution, the new model appoach suggests that paticles can be divided into thee goups by thei behaviou. This makes the paticle model moe systematic and coves all diffeent sizes of paticles found in ambient ai. The poposed modelling appoach is pesented and used late in Pape VIII. Pape IV epots on the diffeences in paticle emoval efficiency fo vaious locations of supply and exhaust devices. Numeical simulations wee caied out in a simple test oom to illustate the paticle concentations with diffeent configuations of oom ventilation. Seveal paticle sizes wee used and the influence of diffeent flow pattens and ai change ates wee investigated. Paticles wee supplied to the oom with the incoming ai. Isothemal conditions 21

22 with vaying ai supply velocities wee used in the pape. Peliminay esults indicated that paticle emoval efficiency is not pedominantly influenced by ai exchange ate; the location of the supply o exhaust device had been undeestimated as an indicato of the paticle elimination pocess. The main objective in ai diffuse eseach is ceating eliable BCs fo CFD modelling. Pape V focused on studying simplified BCs fo a new type of ai diffuse. The model of the diffuse was validated by caeful full-scale laboatoy measuements. Peliminay esults show that simplified BCs pefomed quite well in pedicting velocity field and themal behaviou in the nea zone of the diffuse. Additionally Pape VI used simila methods fo descibing BCs fo a high induction swil diffuse. Tubulence was modelled in this pape by a k-ε RNG model fo swil-dominated flow. The pape confimed that the simplified BCs povided by the manufactue could model diffuse pefomance accuately enough not only vey close to the diffuse, but also in othe pats of the ventilated oom. Pape VII summaises the esults of 10 simulation cases with the two types of ai diffuses used in Papes V & VI. All the 10 simulation cases wee confimed by full-scale laboatoy measuements. This pape focused on how the ai distibution method influences the heat emoval efficiency of the system. Paametes such as heat souce location, heat souce stength, aiflow ate and type of ai diffuse wee studied in connection with the heat emoval efficiency. The pape confimed that if a designe can use eliable diffuse BCs fo the CFD modelling it is possible to optimise the ventilation fo emoving excess heat and contaminants. The citical inteaction between plume aiflow and diffuse supply aiflow can only be modelled in CFD pogams. The pape evealed that the CFD simulation method is supeio to analytical methods in designing cooling with the ventilation ai. Multi-zone methods ae pimaily used fo evaluating the aiflow behaviou between compatments o ooms in a building. Pape VIII uses a multi-zone method to evaluate the paticle mass tanspot within one oom. This new application impoves the evaluation of the esults fom paticle simulations. The paticle model was adopted fom Pape III. In this pape two diffeent paticle sizes wee used to evaluate simulated mixing and displacement ventilation in a small office. The low velocity ATD (ai teminal device) used in the modelling was simila to the one used in Pape II and high velocity diffuse model was adopted fom the manufactue s database. Both diffuses wee modelled by simplified means. Modelling the contaminant souce fom the floo level epesented conditions simila to a dusty floo. The multi-zone model appoach showed that similaly to the study in Pape I most of the paticles follow the convective plumes geneated by the heat souces. The method also evealed the exact locations (in ooms) whee paticle contamination is to be expected. The last pape, Pape IX, deals with impoving the CFD application to optimise the indoo envionment though using an integated design pocess. The integated design pocess is a method to systemise a building poject in such a way that ealie calculations and design pocesses may pomote the quality of the CFD simulation. This pape summaises the eseach done in pevious aticles by an example calculation of an office. 22

23 3 OBJECTIVES The pimay goal of the conducted eseach was to develop and link togethe many aspects of indoo ai modelling. A significant pat of the wok is devoted to the pimay pollutant indoos, aibone paticles. The main poblem of this thesis was to pinpoint the impotance of diffeent vaiables within the modelling. Compaatively many of the aticles in the thesis ae devoted to the modelling of ai diffuses as they ae pimay engines behind the aiflow behaviou in a space. The ai diffuse pefomance is evaluated by how it emoves the excess heat and aibone paticles fom a ventilated oom. All the diffuse aticles contain full-scale laboatoy measuements which confimed the modelling accuacy. Aiflow behaviou of vaious diffuse types was evaluated in the testing facility. This veified that the diffuse type clealy affects the indoo aiflow field. It is a well-known fact that the indoo aiflow patten influences the concentation of hamful contaminants in a oom. That is why the modelling of solid contaminants as pimay pollutants is in focus in this thesis. Futhemoe the aiflow patten evidently influences also the concentation of solid contaminants in the human beathing zone. A majo pat of the thesis is devoted to investigate how the ai distibution design affects the concentation of solid contaminants in vaious locations of a ventilated oom. Diffeent positions of ATDs and many paticle diametes wee investigated to find the easons why some pats of a oom ae moe contaminated than othes. It is expected that the thesis will contibute to a bette knowledge of ventilation system functions. The methods pesented hee may impove futue system designs. The CFD simulation is the main method fo assessing the IAQ poblems though the entie oom space. Thee ae two main topics in this thesis, i.e. ai distibution design and aibone paticle contol using ventilation ai. This thesis intoduces the idea that the CFD modelling should be integated moe with odinay building design to impove the oveall modelling esults. This can only be achieved when all the modelling vaiables ae well justified. Fo instance, if one consides pefoming a CFD simulation of paticle concentation in a oom it is necessay to have a high-quality model of ai diffuse(s) as well as of paticles. If the BCs given to the ai diffuse contain eos the simulation can neve poduce usable esults fom the paticle modelling. That is why feasible esults in eal ooms can be achieved only by integating diffeent modelling disciplines into one coe as diffeent models depend on each othe, Fig

24 Fig. 1. Data flow in the CFD modelling pocess in connection with eo possibility The pocedue fom the use-defined poblem towads late esult evaluation contains many steps. All of the steps in Fig.1 should be pefomed caefully as incoectly detemined space geomety could affect the quality of aiflow o contaminant simulations. Defining the IAQ modelling poblem contains typically many steps, and especially in 3D, poblems should be based on achitectual CAD design which should not be ovesimplified as the geomety of the oom and obstuctions clealy affect the oom aiflow patten (Hagstöm, 2002). The BCs associated with the ai diffuses have the same effect on the aiflow simulation as well as the paticle simulation esults. The eseach is based on commecial CFD platfoms and one need to ecognize some limitations of these pogams as thee is vey little possibility to change the methods and models used within the softwae. Howeve thee ae vey few investigations on how to impove the modelling consideing gatheing of the input paametes fo the simulation. That is why the designe can model a diffuse with simplified means o constuct the diffuse in a CFD pe-pocesso based on actual eal geomety. As thee exists many diffeent methods of how to descibe the BCs fo an ai diffuse, it can be vey confusing to the CFD use. That is why the CFD modelling needs moe systematisation consideing BCs as well as modelling methods and models to impove the end esult. The specific objectives fo the eseach wee: To develop and validate a paticle-settling model including a ealistic distibution of paticles in atmospheic ai To study the influence of ai supply method on o Ai diffusion o Themal behaviou o Solid contaminant behaviou in diffeent pats of the oom To study the efficiency of diffeent ventilation systems concening o Solid contaminants 24

25 o Excess heat o Spatial elationships o Diffeent types of ai diffuses o Vaious locations of ai diffuses To develop and validate diffeent ai diffuse models fo CFD modelling o Low velocity diffuse o High velocity diffuse swil diffuse o Industial ai diffuse To evaluate the oom ventilation by diffeent ventilation configuations o Mixing ventilation o Displacement ventilation o Zoning stategy To develop a new method fo how to evaluate the paticle simulation esults multi-zone appoach. Conducted eseach hopes to answe questions as: o How impove vaious ai diffuse models in CFD? o How does the ai supply method influence oom aiflow velocity, themal and contaminant behaviou? o How impove the paticle modelling in CFD? o How impove the evaluation of paticle modelling esults? o Whee do the paticles eside in a ventilated oom? o Which ai supply method is the best fo emoval of paticles of all sizes? o Is it possible to adopt BCs fom othe calculation disciplines? o Which spaces actually need CFD modelling? o Can CFD modelling be moe integated into the building design pocess? 25

26 26 Ai Diffusion and Solid Contaminant Behaviou in Room Ventilation a CFD Based Integated Appoach

27 4 THE IMPORTANCE OF MODELLING THE INDOOR CLIMATE The indoo ai is something that people ae exposed to duing the majo pat of thei lives. Most people spend most of thei time in atificial climates such as wok/home envionments and tanspot vehicles (Bohus, 1997, Luo, 2003, Matson, 2004). Clean ai in a oom is an essential component fo a healthy indoo envionment. Ai is consideed to be polluted when it contains cetain substances in concentations high enough and lasting long enough to cause ham o undesiable effects. In indoo envionments, paticles ae the main cause of ai pollution and of advese effects on human health (Jones, 1999). It is a well-known fact that indoo pollution oiginates fom indoo pocesses and fom outdoos. This makes the IAQ poblems vey complex. The pesonal exposue to contaminants is pimaily affected by the ventilation aiflow and that is the function of total aiflow ate in a closed compatment (oom) as well as local aiflow behaviou (Bohus, 1997). Essentially, a human is exposed to the contaminants which ae found in the occupied zone. If one consides that the heat souces geneate convective plumes, which ae also pat of the indoo aiflow field then the modelling of indoo pollution without sophisticated compute softwae tools is almost impossible. Cold daughts fom windows, convective plumes fom adiatos, plumes above human heads and aiflow ceated by ai diffuses ae combined to the total indoo aiflow field. Most of the contaminants such as CO 2, SO 2, CO, NO x etc. move in the same manne as the pimay aiflows and do not constitute an exta challenge fo the modelling. But this is not the case with solid aibone paticles, because they have a diffeent behaviou than the pimay aiflows. Paticles in indoo envionments can contain inoganic and oganic constituents such as PAH, VOC, foeign poteins, bacteia, diffeent chemicals, pats of diffeent mateials etc. and they ae also caies of odous. All the paticles found in ambient ai have some distibution fom small size ulta-fine paticles to compaatively lage paticles (Kocifaj & Lukac, 1995). Compaatively lage paticles, 20 µm, have a stong settling behaviou which needs to be consideed when calculating the indoo concentation. The concentation of diffeent-sized paticles indoos is a challenging task, because the concentation is detemined by the kinetic popeties of the fluid (ai) and othe extenal foces that act on the paticles, including the settling velocity (Muakami et al., 1996). Anothe impotant popety of paticles is thei deposition on intenal wall sufaces. Solid contaminants tend to accumulate in a ventilated space with low level of supply ai, and then they become the souces of late indoo contamination. Theefoe it is extemely difficult to detemine the paticle souces fo the modelling as they come fom outdoo ai, ae geneated indoos and sink/souce behaviou is affected entiely by the indoo aiflow field. It can be concluded that paticle concentation in oom ai is an impotant measue of IAQ though envionmental tobacco smoke has been the focus of eseaches fo decades (Hackshaw et al., 1997, Amitage et al., 1997). 4.1 Ventilation and ai supply pinciples Ventilation is the pocess of supplying fesh ai to an enclosed space in ode to efesh/emove/eplace the existing atmosphee. Ventilation is commonly used to emove contaminants such as gases, dust occuing as paticles o vapous and povide a healthy and safe woking envionment; in othe wods, it is an engineeing contol. It can be accomplished by natual means (e.g., opening a window o doo) o mechanical means (e.g., fans o blowes). 27

28 Natual ventilation effects ae uncetain, uneliable and difficult to contol. The oom aiflow motion is entiely ceated by indoo and outdoo tempeatue and/o pessue diffeences though infiltation and ex-filtation (Einbeg, 2001). It may be satisfactoy in some cases, but mechanical ventilation has become an essential pat of good ventilation. Ideally, ventilation povides constant tempeatue, humidity and ai quality within the enclosed space. To be moe specific it is necessay to contol ai quality in the human-occupied zone. This can be achieved by ai diffuses of diffeent kinds and with vaious ventilation methods. Usually devices contolling the oom ventilation ae called ATDs (ai teminal devices), a geneal tem used to descibe supply, exhaust o tansfe diffuses and gilles Mixing ventilation In mixing ventilation, fesh ai, Q in, is supplied at a high momentum to induce oveall eciculation of ai and pomote sufficient mixtue of contaminants and fesh ai, Fig. 2. It is thus aimed at diluting the contamination level, C R, down to an acceptable level in all pats of the oom. In mixing ventilation the ai is typically supplied to the space at ceiling level with a high momentum in ode to ceate a well-mixed flow field without any concentation gadients o tempeatue gadients in the ideal case. In mixing ventilation the supply conditions will mainly detemine the velocity conditions, v R, in the oom (Djuanedy & Cheong, 2002, Zoe, 2001). In mixing ventilation ai jets ae pimay factos affecting oom ai motion. Mixing ventilation has its advantages and disadvantages. The ideal mixing ventilation uses compaatively high supply aiflow ate, Q in, which makes it an inefficient solution concening the enegy aspects. The high initial momentum fom the ai diffuse should be sufficient to mix the ai in the oom. This means that the diffuse has a high pessue loss, high noise pessue level and fans in such a system consume moe electicity as the electical enegy used in the system is a function of the total pessue loss of the system. The significant advantage of mixing ventilation is the easie calculations of the system. The system can be calculated using a simple massbalance model to assess the concentation level in the oom. Howeve, the mixing ventilation system seldom woks ideally. The eal woking systems eveal local concentation gadients which can expose occupants to highe doses of contaminants, m&, than calculated (Rodes et al., 1991, Bohus & Nielsen, 1995). This configuation was assessed by calculating the paticle concentations in Papes IV & VIII which also evealed local concentation gadients. 28

29 Fig. 2. Mixing ventilation and modelling paametes investigated in this thesis at steady-state conditions Displacement ventilation In displacement ventilation, cooled fesh ai, Q in, is supplied at floo level with low momentum by low velocity diffuse(s), Fig. 3. Sometimes this configuation is called statification ventilation, because the flow field is almost entiely ceated by density, ρ a, diffeences. The ventilation ai will natually move fom the human occupied zone to the uppe zone of the oom whee the ai is extacted by the exhaust diffuse(s). Upwad buoyant convection ceated by indoo heat souces, P, caies contaminants and exta heat into the uppe zones of the oom whee the ai is extacted by exhaust teminals. The system is thus deliveing the fesh subtempeatue, T in, ai, Q in, typically 2-4 C lowe than ambient ai to the occupied zone, whee the indoo aiflow field is mostly contolled by the heat souces, as pesented in Papes I-II & VIII. The flow is themally contolled and is dependent of Achimedes numbe, A. The aiflow is dependent on both Reynolds numbe Re and Gashof numbe G (Bohus, 1997). The A numbe in font of the diffuse can be calculated as in Pape V. Displacement ventilation is difficult to calculate numeically (Peng, 1998) as upwad themal plumes fom heat souces ae ceating instabilities in the CFD simulation pocess. Typically in displacement-ventilated ooms linea tempeatue and concentation gadients occu (Mundt, 1996). Displacement ventilation can be used in cooling conditions only. The aiflow is chaacteised by stable themal statification with linea vetical tempeatue distibution in the oom ceated by the heat souces. The most significant advantage of displacement ventilation is the use of smalle aiflow ate compaed to complete mixing ventilation. Displacement ventilation is significantly influenced by the heat souces of the oom and they ae actively displacing the contaminants and heat to the uppe pats of the oom (Skistad et al., 2002). The ai supplied with a low velocity diffuse with sub-tempeatue ai can cause some themal discomfot if the tempeatue diffeences ae too lage along a vetical height axis (Mundt, 1995). 29

30 Fig. 3. Displacement ventilation and modelling paametes investigated in this thesis at steadystate conditions Piston ventilation In the case of piston ventilation which is used only in clean-ooms, a low tubulence and elatively low velocity aiflow is supplied acoss an entie coss-section of the oom, pushing fowad the entie ai volume to an exhaust which is also coss-section-wide (Hagstöm, 2000, Luo, 2003). To emove the contaminants thoughout the oom this method is ultimately the best. Additionally the concentation of contaminants is easy to calculate with simple mass-balance models. Howeve this stategy is inefficient as it uses a lage volume of supply ai and a lot of enegy. This was the main eason why this configuation was not used at all duing the simulation cases Zoning stategy In the zoning stategy fesh ai is supplied at a high momentum in a highe level to the occupied zone. This configuation uses special diffuses which should be chaacteised by high velocity and tempeatue decay, see Pape V. The goal fo this type of ventilation is to contol ai conditions within the selected zone in the oom by the supply ai and allow statification of heat and contaminants in othe oom aeas (Hagstöm, 2000). It can contol the aiflow paametes of a vetical o hoizontal zone in the oom. In most cases the accumulation of heat and contaminants to the uppe zone is desied and utilised as depicted in Fig. 4. This kind of ventilation is a good compomise between mixing and displacement ventilation. The efficiency of emoving contaminants, exta heat and elative humidity fom the contolled zone is vey dependent on ai distibution method and intenal oom configuation, Papes V & VII. Moeove, with a pope design the ventilation efficiency, ε v, of this configuation can be compaatively high. 30

31 Fig. 4. Zoning ventilation configuation with the diffuses and convective heat souces geneating the plumes. It is impotant that the occupied zone and the uppe contaminant zone ae not mixed fo efficient system solutions. The occupied zone is chaacteised by constant tempeatue, T oz, and contaminant level, C oz. Room aiflow patten is contolled patly by supply ai and patly by buoyancy. The esults of testing the zoning stategy of emoving the excess heat fom the occupied zone with two diffeent ai diffuses ae pesented in Pape VII. In conclusion, oom velocity conditions, v R, ae patly a function of supply ai momentum. It is impotant how the ventilation ai is deliveed into the oom, i.e. supply ai diection and the momentum. Fo instance, the aiflow ceated by the heat souces is basically having an effect on themal buoyancy. That is why the distibution of ai should be minimised in the hoizontal diection as it has dumping effects and the ventilation system s efficiency of emoving contaminants and exta heat will decease, see Fig. 4 and Pape VII. Reseaches have fo a long time tied to establish the elationships between incoming ai momentum and oom velocities. Howeve, oom ai movement is to a geate degee thee dimensional without a specific diection, which makes it difficult to descibe in a geneal case using momentum that is a vecto quantity. Additionally, the oom ai velocities ae significantly influenced by the intenal heat souces, Pape VII. That is why it is highly impotant to design the ai distibution because if the oom ai is contolled by the momentum fom the heat souces, this could natually ease the aiflow upwads towads the exhaust outlets (Skistad et al., 2002). 4.2 The evaluation of IAQ The evaluation of the pefomance of a ventilation system is caied though in many papes by estimating the IAQ in the beathing zone. The beathing zone is defined in this eseach as the zone whee a human occupant is taking beathing ai, H = m above the floo level. Additionally, the occupied zone in this thesis is assessed as the whole volume of the oom fom H = m. Occupied zone aveage values ae pimaily assessed fo evaluating the themal comfot which clealy affects the whole human body. That is the main eason why the 31

32 contaminant concentation is pimaily assessed by evaluating the beathing zone value, but othe paametes ae calculated on the whole occupied zone Detemination of the necessay aiflow ate Detemination of ventilation aiflow ate should be based on geneation of pollutant, m&, and the efficiency, ε v, of the ventilation system. Sometimes ventilation aiflow ate is given as ACH (ai exchange ate), it shows how much ai is exchanged in a oom in one hou. If the system is efficient then the necessay aiflow ate could be educed by a facto of 1/ε v. Anothe impotant aspect with solid contaminants is the fallout o deposition on intenal wall sufaces. Additionally it is impotant to conside a pollutant which is tanspoted by the ventilation ai into the oom in the fom of supply ai concentation C in. The necessay aiflow ate needed consideing all the paametes then becomes, accoding to Seppänen & Fisk (2004), Q in m α d 1 C C ε = & bz in v (1) m& contaminant poduction ate (kg/s) C bz limiting (acceptable) contaminant concentation value in the human beathing zone (kg/m 3 ) C in the contaminant concentation supplied to the oom, Figs. 2-3 (kg/m 3 ) α d total ate of the emoval of contaminant, i.e. deposition o fallout of contaminants, filtation, chemical eactions (kg/s) ε v ventilation efficiency (nd, maximum 2 fo ideal piston flow) Fom Eq. (1) it is possible to obseve the complexity of detemination of necessay aiflow ate. Because of this complex elationship typical epidemiological studies have failed to detemine the necessay aiflow ate fo diffeent types of buildings and ooms. Often such factos as outdoo concentation and local ventilation efficiency ae neglected. Inceased incoming concentation, C in, could be caused by impopely maintained ventilation systems, whee paticles could oiginate fom duct sufaces o dumped ai filtes. This is the main eason why the necessay aiflow should be calculated based on allowed concentation in the human beathing zone C bz. It is undestandable that in Eq. (1) the concentation, C bz, is the only fixed vaiable epesenting the maximum limiting value of contaminant in the human beathing zone (should be taken fom noms o egulations), when othe vaiables may change depending on building type, location and ventilation configuation. Basically, satisfactoy ventilation should the fulfil equiements in Eq (4) whee m& 0, C in 0 and ε v max (fo ideal piston flow 2) Ventilation efficiency of contaminant emoval One of the most impotant measues of ventilation system pefomance is its ability emove contaminants. To descibe the efficiency of a ventilation system many diffeent quantities ae 32

33 used to evaluate the system pefomance. The mean ventilation efficiency, ε C, in the oom is defined as C C out in ε C = (2) CR Cin whee C out concentation at exhaust opening (kg/m 3 ) C R mean concentation in the oom, see Figs. 2-3 (kg/m 3 ) C in concentation at supply opening (kg/m 3 ) The ventilation efficiency of contaminant emoval was mainly used fo diffeent-sized paticles. Mainly, contaminant emoval efficiency is expessed fo thee types of paticles simulated in this thesis, see Figs The elative ai quality in the beathing zone can be expessed by the paticle emoval efficiency, ε bz. This efficiency gives a elative compaison between paticle concentations at the exhaust outlet and in the beathing zone, Pape III. The ventilation efficiency in the beathing zone fo diffeent paticles is given by C C out in (ε bz ) 1 3 = (3) Cbz Cin C bz concentation in the beathing zone (kg/m 3 ) 1-3 paticle size index used in this thesis, see Papes I, III, VIII. If the supply ai is uncontaminated then Eq. (3) is tansfomed to C ( ) 1 3 out ε = bz (4) Cbz 1 3 The equation can be used fo gases as well to evaluate the emoval of some hamful contaminant fom the human beathing zone. Paticle emoval efficiency fo an ideal mixing system is unity (1.0). Highe values (>1.0) indicate impoved indoo ai quality conditions in the beathing zone. This may be aanged by popely designed ventilation in combination with CFD and some esults ae pesented in Papes I & VIII. The local ventilation efficiency, ε l, at any point l can be given as C out ε l = (5) Cl Hee, C l (kg/m 3 ), could epesent the concentation a peson in a given location is exposed to. With this paamete it is possible to assess the ventilation efficiency actually expeienced by a human occupant. 33

34 4.2.3 Concentation The concentation in the oom, C R, o given volume, coesponds to the amount of ai which is contaminated by paticles o hamful gases. The concentation is highest close to the souce. Concentation can be expessed in two ways, the fist option is to use the contaminant souce, m&, o it can be expessed by using a oom volume and the amount of hamful contaminant in the same volume. C R πd ρ p = 6 V 3 N = m V dt dt m& = Q out (6) ρ p paticle density (kg/m 3 ) d paticle aeodynamic diamete (m) N numbe of paticles in the oom (nd) m/dt mass of paticles poduced in a oom, late eplaced by m& (kg/s) V/dt volumetic flow ate in a oom (m 3 /s) The concentation is assessed in this thesis at steady-state conditions whee the aveage concentation in a given time ange, t 0, is C t 1 = t t 0 Cdt (7) C t aveage concentation in a given time ange (kg/m 3 ) C concentation (kg/m 3 ) t time (s) hee t = 0 Usually, ventilated ooms contain moe than one type of contaminant. Basically, the effects of diffeent types of hamful contaminants i can be summed, as the allowed concentation of a single substance should fulfil the condition C < Chi. The sum of i numbe of contaminants which is compaed to highest allowed value, C hi, should be less than 1 accoding to Eq. (8) i Ci C hi 1 (8) C i measued o known concentation in a cetain location (kg/m 3 ) C hi hamful concentation of pollutant (kg/m 3 ) The beathing zone concentation is the most impotant value fo human exposue. Human exposue can also be modelled by methods pesented in some ecent studies (Bohus, 1997, Hayashi et al., 2002, Muakami et al., 1996). In this thesis human exposue is assessed by simplified means by aveage concentation ove the whole occupied zone volume, 34

35 C bz 1 = V n CbzdVbz 1 Cbzi Vbzi = = V V i= 1 bz bz C bz dv bz (9) whee V bz (m 3 ) is the beathing zone volume and C bz (kg/m 3 ) stands fo concentation in the beathing zone. Hee i stand fo a given spot in a volume and the concentation in this spot, espectively. The integal is taken fo having a volume-weighted aveage concentation value. Theefoe, the volume-weighted aveage concentation is computed by dividing the sum of the concentation and sub-volume by the total volume of the beathing zone (beathing zone h = m). Similaly Eq. (9) can be used fo the occupied zone as well, then all the vaiables will be alteed to occupied zone values. The elative concentation concept is intoduced hee fo pesenting non-dimensionalised esults. The non-dimensionalised values ae usually pesented by compaing the concentation values to the oom aveage value. Paticle settling in cetain zones o in beathing zone was evaluated by intoducing the dimensionless concentation, C nd, concept, Papes I, IV, VIII. Basically, the concentation, C i (kg/m 3 ), of paticles in a given spot o zone, V i (m 3 ), is compaed to the aveage concentation, C R (kg/m 3 ), in a oom (volume V R, m 3 ). CidVi C nd = (10) C dv R R Heat emoval efficiency The heat emoval efficiency, ε T, equation is defined by measuing o modelling the aveage tempeatue in the occupied zone, T oz (K), as well as in supply, T in (K), and exhaust ai, T out (K), Pape VII. T T out in ε T = (11) Toz Tin The aveage tempeatue in the occupied zone, T oz (K), is vey simila to Eq. (9), as it can be witten as follows T oz 1 = TozdVoz Voz (12) In Eq. (12), T oz (K), is the occupied zone tempeatue (H = m above the floo) and V oz (m 3 ) is the volume of the occupied zone. Geneally this equation shows how lage the potion of heat is in a lowe zone of the oom compaed to the whole volume of the oom. It is evident that the tempeatue in the occupied zone in Eq. (11) is an impotant input paamete fo designing an efficient ventilation system and this is ecognised in some studies of ai distibution (Awbi, 1998, 35

36 Behne, 1999, Mundt, 1995). In the calculations of heat emoval efficiency the occupied zone tempeatue is mainly used as the human occupant senses themal vaiations with the whole body. The effective system solutions focusing on heat emoval efficiency wee tested in Pape VII Daught If one consides themal comfot and people s well-being in a ventilated oom the effect of daught, DR (%), should be taken into account. Anothe good featue of Eq. (13) is the coupling of tempeatue, velocity and tubulence values into one equation. Fange (1988) has suggested DR fo calculating the daught ate, i.e. PPD (pecentage of people dissatisfied) due to daught as follows, ( v Tu )( 34 T )( v 0.05) DR (13) = oz oz oz oz In Eq. (13), T oz is the ai tempeatue, v oz (m/s) is the ai velocity ( 0.05 m/s) and Tu oz (%) is the tubulence intensity in the occupied zone, espectively. Tubulence intensity is defined as (δ/v oz ) x 100%, whee δ is the standad deviation of the ai velocity. Daught ate is intoduced hee fo paamete analysis to evaluate how closely the daught ating is associated to the heat and contaminant emoval efficiency in Pape VII Othe comfot evaluation equations The occupant appeciates the indoo climate by its ai quality and themal conditions. The pediction of themal sensation of indoo climate can be achieved by an index called pedicted mean vote, PMV, developed by Fange (1970). Even if the index is not used in this thesis it eveals the impotance of consideing many paametes in calculation of comfot of human beings indoos. Such vaiables as activity level, clothing, ai tempeatue, mean adiant tempeatue, ai velocity and RH (elative humidity) should be consideed. The thesis mainly focuses on ai velocity and themal pefomance of ventilation systems coupled to paticle concentation in vaious oom locations. 4.3 IAQ assessment paticles in indoo ai Often in ai pollution contol, one can be inteested in sepaating out paticles fom the indoo ai in which they ae suspended. TSP (total suspended paticles) in the aiflow constitutes an impotant component of ai pollution (Omstad, 2000). In this thesis we ae especially inteested in paticles µm. Thee ae seveal tems fo the diffeent size factions; UFPs is used fo vey small paticles less than 0.1 µm (Matson, 2004). In this thesis UFPs ae not modelled as they have been assumed to move in the same manne as the pimay aiflows (settling velocity is vey small) and do not constitute an exta challenge fo modelling. Additionally, PM 2.5 and PM 10 ae commonly used fo paticle sizes less than 2.5 µm and 10 µm, espectively (Diociaiuti et al., 36

37 2001). The oigin and composition of aibone paticles found in indoo ai is quite complex (Owen, 1992). The distibution of paticles in the ai is the most poblematic issue in the modelling. This poblem was assessed aleady in 1955 by Junge who investigated the paticle distibution in atmospheic ai (Junge, 1955). Paticle chaacteistics and distibution in atmospheic ai is a constant eseach topic fo aeosol scientists (Cafoa et al., 1998, Esposito et al., 1995, Fishman et al., 1999, Hais & Maieq, 2001, Hovath et al., 1996, Kocifaj & Lukac, 1995, Lyubovtseva, 1995, Mathiesen, 2000, Mime et al., 1995, Voutilainen & Kaipio, 2001) Chaacteistics of paticles Real paticles ae not spheical in fom, but have diffeent shapes as given in Fig. 5. That is why the aeodynamic paticle diamete is intoduced fo simulations. The aeodynamic diamete used in the simulations, d p, is the diamete of the unit density (ρ p = 1 g/cm 3 ) sphee that has the same settling velocity as the paticle. Paticles can appea as human hai, pats of human skin and pats of diffeent building o othe mateials (Hinds, 1999). Fig. 5. Scanning electon micoscopy micogaphs and size fequency histogams fo the two factions (a) PM 2.5 (fine) and (b) PM (coase) (Diociaiuti et al., 2001) In Fig. 5 one can obseve the fine faction of paticles, often efeed to as PM 2.5. If ultafine paticles less than 0.1 µm ae not consideed then paticles fom µm dominate both by numbe and mass concentation in atmospheic ai. This can be obseved in Fig. 6 and Pape III. Additionally, in Fig. 6 the main behaviou of paticles is pesented. The behaviou is associated with the aeodynamic diamete of the paticles, necessay to know fo the modelling, Papes I- IV & VIII. Futhemoe, the paticles in Fig. 5 have a distibution in the ai by the mass and numbe as indicated in Fig

38 Fig. 6. The main chaacteistics of paticles mass, size numbe distibution, settling velocity, aeodynamic diamete and behaviou in ai To take into account all sizes of paticles in the ai fo modelling is toublesome; theefoe only thee paticle sizes ae used in the simulations. These thee paticle sizes epesent all the paticles found in atmospheic ai. The thee sub-models of paticles ae solved simultaneously and the total mass of monodispese paticles will be the eal mass of the paticles found in atmospheic ai, Eqs.(14-17) and Fig. 7. Fo instance paticles (0.1 1 µm) in the fist goup ae modelled with the diamete 0.5 µm. It epesents the mean paticle diamete which coves a cetain ange of paticles. The numbe N 1 and mass m 1 of paticles hee (0.1 1 µm) is the highest, theefoe the paticle aeodynamic diamete, d 1, selected fo the modelling should be caefully selected, as in Pape VIII. Fig. 7. Change of paticle size distibution with elative humidity and new modelling concept 38

39 Once the aeodynamic diamete, d 1, d 2, d 3 (m), of the paticle is chosen fo calculations it is possible to assess the paticles with thee sub-models as in Pape VIII and Eqs. (14-17). 3 "1" πd m"1" = ρ p ΣN"1" 6 (14) 3 πd"2" m"2" = ρ p ΣN"2" 6 (15) 3 πd"2" m"3" = ρ p ΣN"3" 6 (16) π mp = ρ p ( d" 1" N"1" + d p"2" N"2" + d p"3" N"3" ) 6 (17) Hee the total mass of the paticles, m p (kg), is equal in Eq. (17) to the whole mass of paticles found in ambient ai. In Eq. (17), ρ p, is the paticle density (kg/m 3 ), d 1, d 2, d 3, is the paticle diamete (m) and N 1, N 2, N 3 is the numbe of paticles in a cetain goup. Howeve thee ae seveal limitations of the used paticle model: 1) heat and mass tansfe between ai and paticles is neglected, the ai tubulence influences the paticles but the paticles do not influence ai tubulence back (one-way coupling) 2) no paticle coagulation 3) no paticle e-suspension when it has eached a wall 4) all paticles ae solid sphees 5) the chemical composition of the paticles is ignoed (the paticle is chemically neutal). All additional paticle mechanics ae ignoed, such as Bownian diffusion and nucleation. Paticle deposition on wall sufaces is assessed by simplified means, see Section The pesent model only accounts fo the gavitational settling of paticles at steady-state Gavitational settling The teminal settling velocity of an aibone paticle is an impotant quantity fo chaacteizing the settling behaviou of the paticle. The aeodynamic diamete of a paticle, a key popety fo chaacteizing paticle deposition, is dependent upon the settling velocity. A paticle will each its settling velocity when the dag foce and buoyancy foce balance with the gavitational foce on the paticle. In calculations the consideation of fluid physics is impotant. Paticle motion with a moving fluid is descibed by Newton s second law, which states that the sum of extenal foces exeted on a paticle is equal to the ate of its linea momentum, in this case, 3 πd p dv ρ p = FD + Fg (18) 6 dt 39

40 whee ρ p (kg/m 3 ), d p (m) and acceleation, espectively, and dv FD dt (m/s 2 ) epesent paticle density, aeodynamic diamete and (N) and Fg (N) ae the dag and gavitational foces on the paticle. The ai velocity field and gavitational settling foce pimaily affect the motion of small paticles suspended in a ventilation flow. Once the paticle settles due to the foce of gavity, F g = ρ p (πd p 3 /6)g, a dag foce is ceated due to viscous fiction. In the gavitational foce equation g stands fo acceleation due to gavity. The total esisting foce (ai esistance) on a spheical paticle moving though the ai with a velocity, v p (m/s), is called dag foce F D. In the lamina case Re 0.5 the ai esistance (dag) can also be obtained by integating the nomal foce F n =πµv p d p and the tangential foce, F τ =2πµv p d p, ove the paticle. These two components ae combined to give the dag foce, F D, Eq. (19). F D π 2 2 = 3πµ v pd p Cd ρav pd p (19) 8 whee µ (kg/ms) and C d (nd) epesent the dynamic viscosity of the ai and the dimensionless dag coefficient, espectively. Dag is the foce of esistance on an object (in ou case a paticle) due to a fluid. The dag coefficient, C d, is a value that descibes all of the complex dependencies of shape, inclination, and some flow conditions on dag. The coefficient is a function of Reynolds numbe and it is detemined expeimentally. It is a coefficient, which descibes the shape of the paticle (Hinds, 1999) and can be expessed fo the Stokes egion aiflows, whee Re 0.5 by C d = 24/Re, Fig. 8. Stokes law is a solution of the Navie Stokes equations fo low Reynolds numbe fluid motion. Fig. 8. Dag coefficient fo diffeent Reynolds numbes Fee settling of the paticles in ventilation ai seldom happens, because paticle behaviou is distubed by pimay aiflows caused by ai diffuses and seconday aiflow movements. Seconday aiflow is caused by heat and cold souces which play an impotant pat in oom ventilation, Pape VII. Foces influencing paticles in a ventilation system ae fa moe complex, because of buoyancy, the tem ceated by tempeatue, T (K), density diffeences in the ai, ρ a = f(t). Usually, the buoyancy, ρ a (πd p 3 /6)g, due to diffeences in ai densities, will affect the paticle 40

41 movements. The settling velocity in ai is calculated by Eq. (20). Balancing the dag, the gavitational foce and the buoyancy, esults in the paticle teminal settling velocity v p, Fig. 9. Fig. 9. Paticle teminal settling velocity and diffeent types of paticles found in ambient ai, see Pape I Eq. (20) is the base fo the settling model used. Fo vey small paticles it is sometimes solved by using the paticle aeodynamic diamete d (m) and Cunningham s slip coection facto c (nd). ( ρ ρ ) p 3 ( ρ ρ ) 2 2 πd p g p a d p gρ pd pc a g = 3πµ vpd (20) 6 18µ 18µ The slip coection facto, c, denotes that the ai suounding the paticle is not fully homogeneous, but a mixtue of moving individual molecules. This is significant fo vey small paticles with an aeodynamic diamete less than 0.06 µm at nomal pessue and tempeatue. One can obseve that the behaviou of big paticles (p eplaced by goup) in goup 3 o small paticles, goup 1, is totally diffeent as the paticle aeodynamic diamete is an impotant vaiable. v 2 p > 2 gρ pd" 3" c gρ pd"1" c = (21) 18µ 18µ One can assume that (gρ p c)/18µ is constant and the paticle aeodynamic diamete is the most impotant paamete fo the settling. The constant thus can be eplaced by S p. 41

42 v p p 0.1µ m < p 20 µ m v 2 2 = S ( d ) S ( d ) (22) 2 2 = S ( d ) S ( ) (23) p p " 1" < p d"2" The constant, S p (nd), in Eq. (22) is intoduced as a paticle-flow facto, which shows how many much paticles ae settled fom one location to the othe location. If we conside that H z (m) is the height of the sub-domain in the oom and A z (m 2 ) is the aea of the cubical sub-domain then the time constant, τ z (s), in the sub-domain is, A H z z τ z = (24) Qz whee Q z (m 3 /s) is the flow ate in the sub-domain. Paticle-settling fom one pat of the oom to the modelled pat of the oom (fo instance the occupied zone) can be somewhat chaacteised by the distance τ z v p. A elative expession fo paticle-flow facto, S p, fom zone to zone could be given by the paticle-settling distance divided by the sub-domain height, H z (m), in the unidiectional flow field in Eq. (27). v = τ z p S p = H Z A v z Q z p (25) The settling of paticles only occus when v a <v p. This poves that the paticle-settling facto is a function of velocity conditions and paticle aeodynamic diamete S p = f(q z, d p ). The aeas which ae contaminated with paticles pobably have vey low velocity as esults indicate in Papes I IV & VIII Deposition Deposition of paticles was assessed by simplified means in all papes. Paticle deposition on the walls and intenal wall sufaces is a eseach field wee all the poblems concening paticle deposition on solid sufaces ae not solved (Anand & McFaland, 1989, Lange, 1995). When paticles ae dispesed into the ai, the andom movement will always esult in a net tanspot towads aeas of lowe concentation, and the slip (dift) velocity, v pa, is popotional to the gadient of the pollutant concentation (Lange, 1995). Deposition to solid sufaces can thus, in a simplified appoach, be contolled by giving diffeent BCs fo the paticle concentation at the wall C w (kg/m 3 ). In the pesent thesis it is assumed that paticles deposit by diffusion (Holmbeg & Li, 1998). C w = α C (26) d nw C nw (kg/m 3 ) is the fist nomal gid point concentation. This is an empiical appoach whee appopiate deposition, α d, values could be found fom measuements o liteatue. Unfotunately, this value is gid-dependent. The pesent thesis assumes that α d is equal to 0. It 42

43 should be noted hee that the paticle deposition on wall sufaces was modelled as a pefect deposition situation, i.e. once a paticle goes to a wall it becomes a pat of the wall. This was achieved by putting a no-slip condition at the physical bounday of the wall as zeo concentation at the wall C w = 0 (Holmbeg & Li, 1998). The deposition is also impotant fo dimensioning of necessay flow ate to maintain acceptable beathing zone concentation, as in Eq. (1). 43

44 44 Ai Diffusion and Solid Contaminant Behaviou in Room Ventilation a CFD Based Integated Appoach

45 5 METHODS 5.1 Numeical simulation of IAQ paametes by CFD Numeical methods by means of CFD modelling ae used thoughout the thesis. It has been used fo calculating the indoo aiflow field concening eseach on ai diffuses and investigating the paticle concentation in diffeent pats of ventilated ooms. CFD calculation epesents a supeio method to calculate the indoo aiflow, because semi-empiical and puely analytical methods fail fo diffeent oom layout conditions. The tempeatue o contaminant gadients and convection flows fom heat souces could be calculated by models developed by Mundt (1996) and Kühne (1995), but these models ae not applicable in all oom layout situations. Basically because of these estictions, the use of CFD as a univesal tool to pedict indoo aiflows has gown damatically. Anothe significant advantage of CFD is the possibility to test diffeent system configuations vitually without constucting the eal systems. In this way it is possible to optimise the ventilation system to emove pollutants by numeical methods. That option is widely used in this thesis by validated models of diffuses. The main limitation to the widespead use of CFD is the many model-specific values which depend on the behaviou of the flow in the tubulence model and so on (Casey & Wintegeste, 2000, Chen & Sebic, 2001, Davidson, 2003, Vesteeg & Malalasekea, 1995). 5.2 Geneal bounday conditions of the thesis The CFD simulations thoughout the thesis wee pefomed in a simila way to ease the compaison of diffeent modelling esults. All the simulations pefomed in the thesis wee done in 3D space with steady-state conditions using the commecial code FLUENT, except in last pape whee CFX code was used. Tubulence was modelled in all cases with the standad k-ε model o its modifications. The RNG (mathematical technique called Renomalisation goup theoy) k-ε tubulence model (Vesteeg & Malalasekea, 1995) and the same model with swil modification suggested by the softwae manual wee used as well (Fluent. Inc., 2003). The standad k-ε model was chosen to model tubulence because it epesents the best known model utilized and validated fo ai diffuse pefomance. Wall teatment was achieved by standad wall functions povided by the softwae. Heat tansfe in all simulations was enabled except in Pape IV. The adiation was not included in the simulations because of compute capacity estictions. The compute used in this thesis was a Pentium III 850 MHz with 512 MB cental memoy which esticted utilising all possibilities in the softwae. Fo instance, the gids in the simulations wee esticted to stuctued hexahedal mesh layout and a maximum cell numbe of because of memoy limitation. All the cases used segegated solve, except the last pape, to obtain esults fo flow, tubulence and enegy thoughout the computational domain. In the last pape the coupled solve was utilised. Discetisation of pessue was always second ode accuacy and the pessue-velocity coupling was achieved by SIMPLE o SIMPLEC algoithms. The momentum and enegy wee always discetised in a second ode upwind scheme (Vesteeg & Malalasekea, 1995). Tubulent kinetic enegy, k, and tubulence dissipation ate, ε, wee in most cases solved by fist ode upwind accuacy. Specific unde-elaxation factos fo the segegated solve wee modified to obtain conveged esults. All the BCs fo walls, diffuses and othe elements within the modelling ae given in all papes. 45

46 5.3 Validations using expeiments and liteatue To confim the accuacy of the simulations a full-scale test oom was used. The full-scale oom had 4.0 m x 10 m floo aea and 6.0 m height, Papes V- VII. It was themally insulated fom the suoundings by 50 mm (λ = 0.03 Wm/K) thick polystyene elements to minimize the heat flux though the wall boundaies. Ai velocity was measued using Kaijo Denki WA 390 ultasonic sensos, which have an accuacy of (± 0.02) m/s. The sensos sample the ai velocity vecto components with thee pais of ultasonic tansduces based on the flight time of the ultasonic pulse. The ai tempeatues wee measued by Fenwal themistos with an accuacy of ± 0.1 K. Measuements of ai velocity and tempeatue wee time aveaged ove s (Pape V and Pape VI, espectively) and the values wee ecoded with a data logge. The sensos wee attached to a compute-contolled tavesing system moving them fom point to point to scan the two pedetemined measuement planes. The influence of aiflow pofile fom ai diffuses and heat souce location on heat emoval efficiency was measued in the same oom. Totally 10 diffeent ventilation configuations wee validated by the measuements and pat of the esults ae pesented in APPENDIX D. In the test oom only thee components of velocity v x, v y, v z and ai tempeatue wee measued. In Pape II in addition to velocity and tempeatue also solid paticles wee measued. It is extemely difficult and expensive to make contolled expeimental investigations of paticle movements in ventilated ooms. Real full-scale measuements of solid contaminants wee pefomed in Pape II and mostly failed because of poblems with geneating constant concentation of 0.26, 1.0 and 10 µm paticles, Fig. 10. Fo that eason the paticle model was late validated based on esults in the liteatue (Holmbeg & Li, 1998, Mattsson, 2002, Muakami et al., 1996) in Pape VIII. Fig. 10. The vaiation of aeosol concentation (paticles 0.26 µm) in the test oom in Pape II. The aveage concentation in the oom was 10 µg/m 3. 46

47 5.4 Numeical simulation of ventilation aiflows coupled with paticles Fo aiflows, the CFD pogam solves consevation equations fo mass and momentum. Fo flows involving heat tansfe o compessibility, an additional equation fo enegy consevation is solved. Two additional tanspot equations ae solved when the flow is tubulent, i.e. the tubulent tanspot quantities k and ε in the k-ε model Mass consevation equation The equation fo consevation of mass, o continuity equation at steady state, can be witten as follows: ( vρ) = 0 (27) Eq. (27) is the geneal fom of the mass consevation equation and is valid fo incompessible as well as compessible flows, whee v is the velocity vecto and ρ is the fluid density Momentum consevation equation The momentum equation can be expessed by Navie-Stokes equations by descibing Newton s second law of fluid flow. The momentum equation can be expessed in vecto fom as ( v v) = ( µ tot v) p + Fg + F T ρ (28) whee v, ρ, p (Pa), F T (N) ae the velocity vecto, density, pessue and themal diffeences in the buoyancy tem, espectively. It is woth mentioning that in tubulent flow the viscosity, µ tot (kg/ms), is the sum of molecula and tubulent viscosity. Natual convection is modelled by the Boussinesq appoximation. As we can see fom Eq. (28) it esembles the typical momentum equation with the addition of the gavitational settling foce and buoyancy tems being included in the souce tem. The extenal body foces, F g + F T, contain paametes such as themal diffeences, which give the exta momentum to the flow. The buoyancy tem, F T, was modelled by Boussinesq appoximation in the momentum Eq. (28) and is given as: ( ρ ρ ) g ρ β ( T T g (29) ef ef ef ) whee ρ ef is the efeence density of the flow (kg/m 3 ), T (K) the tempeatue of the ai, T ef (K) is the efeence tempeatue and β (1/K) is the themal expansion coefficient. This appoximation is accuate as long as changes in actual density ae small in the simulated space. Refeence values can be, fo instance, oom aveage values. Natually, gavitational acceleation acts in the diection of the height coodinate and affects the buoyancy foces in all diections of Catesian coodinates and the A numbe in the momentum equations. 47

48 5.4.3 Enegy consevation equation To obtain a desciption of the tempeatue distibution thoughout the non-isothemal flow domain the enegy equation is used. Enegy, E, in the ai is defined as the sum of intenal themal enegy, kinetic enegy of velocity components and the gavitational potential enegy typical in buoyancy-diven flows. Consevation of enegy at steady state is descibed by v E + p = hjj ( ( ρ )) j + S j h (30) whee J j is the diffusion flux of species j (kg/m 2 s), h j is the enthalpy (kj/kg) of species j and S h (W/m 3 ) includes the heat o any othe volumetic heat souces defined in the simulation pocess Paticle concentation equation The geneal concentation equation fo paticles in the paticle-settling model used in most of the papes is pesented below, ( ρc( va + vp ) = ( Γc C ) SC ρ C + + (31) whee C is the volume concentation of paticles, usually vey small (10-8 volume faction). The dimensionless numbe, Γ c (m 2 /s), stands fo the diffusion coefficient. The tem S C includes any othe volumetic souces meant fo concentation, fo instance elative humidity dependence which will be discussed moe closely late. Paticle concentation in the contol volume influenced by convective diffusion in a field with an extenal foce can be descibed by the following geneal concentation Eqs. (32-34) (Jin, 1993). ( C1 ( va + v p K ) = ( Γc C1 ) S 1 ( C2 ( va + v p K ) = ( Γc C 2 ) S 2 ( C3 ( va + v p K ) = ( Γc C 3 ) S 3 C C C C C C C C C whee indices 1-3 epesent the paticle goup, Figs In this case we assume that C 1 is the numbe concentation of paticles µm with simila behaviou. Eqs. (32-35) use coagulation and gowth to detemine the concentation modification, C, which is the change of paticle mass concentation in the contol volume. The settling velocity coection facto, K (nd), descibes the gowth of the aeodynamic diamete. It is especially significant fom RH 70% onwads (Busch et al., 1995). In the thid goup 3 (5-50 µm) the settling foce is significant, but the coagulation and gowth ae elatively insignificant. In the fist goup 1 the situation is the opposite. One of the tickiest things is to detemine the paticle diamete d, C, and the settling velocity coection facto, K, when using tansient calculations. Tansient calculations ae neglected in this thesis and can be the base of futue studies, see Pape III. (32) (33) (34) 48

49 If we eplace the paticle goup numbe by N and neglect all the modification tems the concentation equation at steady-state condition is simila to Eq. (31) N ( ρcn ( va + vp ) = ( Γc C N ) SCN ρ C + + (35) Tubulence modelling with the k-ε model Ai movements in a oom ae usually tubulent and need be modelled in CFD. In tubulent flow we can divide the vaiables into one time-aveaged pat of the velocity v (when the mean flow is steady) and one fluctuating pat v, so that v + v. In the standad k-ε model the modelled tanspot equations fo tubulent kinetic enegy, k, and its dissipation ate, ε, ae solved. The pesent model assumes that the flow is fully tubulent and the effects of molecula viscosity ae small compaed to the tubulent viscosity, µ tot = µ + µ tub. That is why this model is vey suitable fo fully tubulent flows and it calculates the tubulence isotopically. That is why the anisotopic flows such as wall jets should be calculated with othe tubulence models. Tubulent viscosity in this model is computed as µ = k tub Cµρ ε (36) In tanspot equations of k and ε one need values fo 5 unknown constants (Davidson, 2003). Unfotunately these constants ae not univesal fo all types of flows. The pesent thesis used default values, C 1ε 1.44; C 2ε 1.92; C µ 0.09; σ k 1.0; σ ε 1.3. When using k-ε model modifications the default values povided by Fluent Inc. (2003) wee used as well. Moe infomation about the standad k-ε tubulence model can be found in numeous publications (Davidson, 2003, Launde & Spalding, 1974, Vesteeg & Malalasekea, 1995). Othe tubulence models ae not discussed in this thesis, but quite boad knowledge about diffeent tubulent models within CFD is given by Davidson (2003). If the Navie-Stokes equations ae solved without using any appoximations in tubulence modelling, the appoach is called DNS (diect numeical simulation). Successful pediction with DNS needs a vey fine mesh to captue all the smallest eddies in the flow. The smallest eddy size in the tubulent flow is of the ode of the Kolmogoov length scale l k (m) ν l k = (37) ε whee ν is the kinematic viscosity of ai. Fo most of indoo aiflow the Kolmogoov length scale is aound 0.01 to m. To use the DNS method the gid numbe fo the simulation is vey high and not feasible in today s PC. Anothe impotant aspect of the Kolmogoov length scale is the solving of paticles. As the cuent thesis uses Euleian appoach fo the paticle model it is necessay that the paticle size should be significantly smalle than the Kolmogoov mico-scale (Holmbeg & Li, 1998). 49

50 50 Ai Diffusion and Solid Contaminant Behaviou in Room Ventilation a CFD Based Integated Appoach

51 6 DIFFERENT COMPONENTS WITHIN THE MODELLING AND BOUNDARY CONDITIONS It is a well-known fact that the accuacy of the CFD simulation o any kind of modelling geatly depends on its given numeical methods, the chosen models and input values. The CFD modelling is a vey poweful method to solve indoo aiflows, but it is vey sensitive to its given numeical methods and BCs D space model The stating point of a oom aiflow analysis is to ceate a geomety model. By modelling geomety, spatial elationships and objects such as walls and windows should be ceated in a way that these should be elatively simple but at the same time epesenting ealistic conditions so as not to compomise the modelling accuacy. Ove-simplification of intenal obstuctions will lead to eos and alteed aiflow pofile within the space. This will lead aftewads to eoneous pedictions of solid contaminants within the modelled space. Theefoe it is ecommended to constuct the oom geomety based on the achitect model and model even the funitue based on the manufactue s geomety model, Pape IX. A typical aangement of a oom with diffeent modelling vaiables is given in Fig. 11. Fig. 11. Basic CFD modelling set-up vaiables 51

52 In Fig. 11 one can ecognise the complexity of the CFD simulations. That is why the CFD modelling needs moe systematisation to use it as a eliable tool fo optimising indoo climate paametes, Pape IX. 6.2 Poducts within the modelling The modelling of indoo aiflow could include many poducts fom eal manufactues. Fo instance funitue, lamps, adiatos and diffuses ae poduced by some eal poduce, Papes V, VI & IX. Usually the dimensions of these poducts and BCs ae vey specific and sometimes only known by the manufactue. Funitue layout is aleady utilised in the selection pogams, which can be used in the simulations. Fo instance ove-simplification of funitue could lead to the wong oom volume in a small space, which as a esult in CFD pedicts highe/lowe indoo ai velocities compaed to the actual values. 6.3 Intenal heat and cold souces Intenal heat souces such as adiatos, humans and windows geneate aiflows which ae ceated by themal buoyancy and ae a cucial pat of indoo aiflow behaviou. This is especially tue when one deals with displacement ventilation. In this case intenal heat/cold souces dominate the aiflow behaviou. The intenal spatial elationships of heat souces ae impotant fo the efficiency of the ventilation system. This was tested in Papes VII-VIII. A cucial pat of indoo aiflow analysis is the human occupants. The mathematical models and measuements have clealy shown that the plumes above the human body can geneate elatively high velocities, up to 0.26 m/s (Popiolek, 1981). That is why it is impotant to use a eliable mathematical model of the human body (Bjøn, 2001, Nilsson, 2004). Mathematical models of human bodies ae impotant fo modelling pesonal exposue to contaminants o climate (Bohus, 1997) and ae impotant fo affecting the flow field and themal conditions in the oom as the human body geneates heat to the suoundings. 6.4 Numeical modelling of supply openings The aiflow fom a diffuse geatly affects the aiflow patten in a oom. Additionally, the diffuse type and the ai supply paametes dominate the ai diffusion in the oom. In this thesis diffeent kinds of diffuses wee tested to find out how the ai distibution affects the aiflow behaviou, tempeatue distibution and paticle concentation in diffeent pats of a ventilated oom. The pimay goal of this eseach was to veify that ai distibution in a oom is a cucial pat of efficient ventilation. The ai supply fom diffuse(s) patially detemines the efficiency of the ventilation system. It is sometimes much moe impotant to ask how the ai is distibuted into the space than to know the total ventilation flow ate (Awbi, 1998). In complex oom situations the only solution fo designing effective ventilation is to use calculation pogams such as CFD. Still, the limitation of using CFD efficiently is the lack of ai diffuse models within the pogam. The eseach in the field of CFD diffuse models has gained moe attention ecently as it diectly detemines the accuacy of the simulation (Djuanedy, 2000, Djuanedy, 2002, Fan, 1995, 52

53 Fontaine, 2002, Hu, 2003, Huo et al., 2000, Luo, 2003, Nielsen, 2000, Koskela, 2004). In this thesis, fou types of diffuses wee studied: low velocity diffuse, multi-cone diffuse, swil diffuse and industial ai diffuse. All the diffuses in this thesis wee assessed by simplified means. All the diffuses ae possible to model by ceating an exact eal geomety. This method is vey time consuming, but possible to test a single diffuse (Hu, 2003). In this method the BCs ae given to the connection duct whee fo instance the velocity is a known value. The main challenge in the modelling of ai diffuses is to account the gid layout fo a small-scale opening compaed to the scale of oom layout, Papes V & VI. Howeve, ceating an exact geomety is not feasible in nomal calculation cases because the numbe of gid points in a computational domain can incease damatically. Two othe well-known methods fo modelling ai diffuses with simplified BCs (Chen & Sebic, 2000) ae the momentum method and the box method. The momentum method assumes that the aiflow fom a paticula diffuse can be pedicted using the isothemal axisymmetic jet fomula (Zoe, 2001). The box method was developed by Nielsen (1992) to decease the numbe of gid points nea the diffuse, whee the aiflow pofile of the diffuse is specified inside a vitual box (Nagasawa & Kondo, 2002). The box method is not diectly used in this thesis. Basically, velocity specification on the bounday of the model sufaces of the diffuse was used fo high velocity diffuses, Pape VI, and the momentum method fo low velocity diffuses, Papes I & II. A combination of these methods was used in Pape V. The momentum souce specification is a useful method if the diffuse oute aea is not totally fee fo ai to pass; it means that the diffuse effective aea is much smalle than the fee opening aea. This is the typical case with low velocity diffuses, Fig. 12. Fig. 12. Low velocity diffuse, used in Papes I & VIII The momentum souce is also used fo nozzle duct diffuses (Koskela, 2004) and gilles (Huo et al., 2000) and othe wall-mounted diffuses (Fan, 1995). The low velocity diffuse depicted in Fig. 12 was used fo modelling of the ai diffuse in Pape I by using a momentum souce to ovecome the poblem of giving the ight flow ate in font of the pefoated suface of the diffuse. The flow fom the low velocity diffuse is eithe given by the volumetic flow ate, Q in (m 3 /s), o by the face velocity, v in (m/s). In this case one assumes that the velocity is unifom all ove the woking suface of the diffuse. 53

54 Q in v in = (38) Ain whee A in (m 2 ) is the total suface aea of the pefoated pat of the diffuse. The geomety of the pefoated suface (in pecent, 10 %=0.1) and the fee nominal opening aea, 0.1A in, is much less than the whole aea of the diffuse. We should note that A in (m 2 ) is the same as the effective aea of the diffuse. It is impotant that velocity o mass flow is not calculated fom diffuse face aea. To ovecome the poblem with coect flow ate, an exta momentum souce is given in a subdomain fo acceleating the ai velocity at hoizontal x, z diections. The momentum souce should be diected nomal to the inlet bounday to avoid ceating a complex pefoated suface of the diffuse as a method fo modelling ventilation devices, Papes I, II & V. The same BCs wee used fo the low velocity diffuse in CFD simulations. The momentum souce in font of the low velocity diffuse in a sub-domain can be defined geneally as F in 2 = avindain ρ (39) whee ρ a (kg/m 3 ) is the ai density and v in (m/s) is the ai speed in a pefoation hole. If the ight mass flow is maintained and non-isothemal conditions ae used then the cold flow fom the diffuse is influenced by vetical acceleation due to gavity. This is given by the Achimedes numbe, A, fo a flow fom the diffuse, Pape V and Nielsen (2000). Pape V solved the BC specification by poviding mixed BCs fo two diffeent pats of the industial ai diffuse in Fig. 13. Fig. 13. Industial ai diffuse geomety (A.) and CFD epesentation (B.), Pape V An exta momentum souce in font of the cylindical ai diffuse was given in a small subdomain, which geneated a adial aiflow with constant velocity at the supply coveing. The detemination of sub-domain size should be found in such a way that it is wide enough to acceleate the velocity in font of the diffuse to a coect velocity, as the given value in the 54

55 selection pogams. The thickness of the sub-domain in Papes I, V & VIII in all cases was taken to be 0.05 m. The BCs fo the cicula multi-cone ceiling diffuse wee geneated without adding any souce tems in font of the diffuse. The ai diffuse was simply modelled by supplementing the wellspecified bounday pofile to the supply opening, simila to the pescibed velocity method whee velocity values typically ae given in a closed domain (vitual box) in font of the diffuse. In this way, it is possible to avoid giving only a supply aiflow ate, Q & in (kg/s), which geneates a unifom velocity pofile on the bounday of the diffuse and undeestimates the maximum velocity values in the nea zone. The aea of a diffuse suface, A in, is divided by the facets that contain specified velocity values fom the pofile v in = f(h in ). H (m) hee epesents height of the diffuse, but thee ae also othe possibilities to give a pofile which is a function of some geometical paamete. Then the mass flow ate, Q &, can geneally be given as in & Q ρ in = avindain (40) With this kind specification it is also possible to diect velocity vectos downwads o upwads as depicted in Fig. 14 and at the same time the pofile is a function of height co-odinate. Fig. 14. Ai velocity pofile on the bounday of a multi-cone diffuse The pe-descibed velocity on the bounday of the modelled diffuse was a successful method also with a swil diffuse, Pape VI and Fig

56 Fig. 15. High induction swil diffuse and CFD simplified geomety model to the ight, Pape VI The initial ai velocity was calculated fom the diffuse volumetic flow ate, Q in (m 3 /s), and the angle α of the velocity vectos, see also Pape VI, v in Qin = (41) cosαπhd The height of the diffuse, H (m), and the diamete, d (m), of the diffuse model make it possible to calculate the initial ai velocity at the diffuse opening. In this way the aiflow ate will be coect, but if the model is much smalle than the eal diffuse then pobably the velocity in the nea zone will be ove-pedicted. Nevetheless, the esults fom simulations of vaious ai diffuses eveal that the simplified methods descibed hee can be used fo most of the diffuses found on the maket. To conclude, almost any ai diffuse can be modelled with a simple ectangula opening epesenting the ai diffuse, but this kind of modelling pocedue has some limitations as descibed in the liteatue (Heikkinen, 1990, Huo et al., 2000). A cooled beam was used in Pape IX. This model used exactly same techniques as the diffuse models descibed befoe. The numeical paametes wee descibed on the boundaies of the cooled beam CFD model Tubulence quantities fo k and ε at supply openings In the k ε tubulence model the modelled tanspot equations k and ε ae solved. Moe detailed desciptions of the model can be found in the liteatue (Davidson, 2003, Vesteeg & Malalasekea, 1995). As the explicit values of k and ε ae not measued, the tubulent kinetic enegy can be calculated by using the tubulence intensity at the inlet. The elationship between the tubulent kinetic enegy at the supply inlet, k in (J/kg), and tubulence intensity, Tu in (%), is 3 k ( ) 2 in = vintuin (42) 2 The tubulent dissipation ate is obtained fom a length scale. If the tubulent length scale, l in (m), is known, ε in (m 2 /s 3 ) can be detemined fom the elationship: 56

57 3/ 2 in 4 k ε in = C 3/ µ (43) l in whee C µ (nd) is an empiical constant specified in the tubulence model (appoximately 0.09). The length scale, l in = 0.07H, can be appoximated by the elevant dimension of the diffuse H. The facto 0.07 is based on the maximum value of the mixing length in fully developed tubulent pipe flow. 6.5 Computational gid Using a finite volume method the calculation domain in Fig. 16 is divided into a finite numbe of contol volumes and gid points. Each gid point found in the computational domain is suounded by one volume. All the vaiables chosen fo the calculations ae solved in these points. The calculation esults by diffeential equations in these locations ae eplaced by discete values. The cuent eseach used only the stuctued hexahedal mesh depicted in Fig. 16, except in the last pape whee unstuctued mesh was used, Pape IX. Since CFD computes patial diffeential equations into discete fom it necessay that the gid layout nea the simulated ai diffuse be sufficient to avoid unwanted numeical diffusion false diffusion nea the supply opening. The poposed gid layout nea an industial ai diffuse is given in Fig. 16. Fig. 16. Gid layout nea an industial ai diffuse All the numeical vaiables ae discetized in the computational domain and if the gid is not sufficient enough tuncation of equations causes eos simila to eal diffusion. Numeical diffusion is most noticeable when the eal diffusion is small, that is, when the situation is convection-dominated. That is why it is highly impotant to pefom the gid-independence tests by doubling the gid size. If doubling the gid poduces the same esult then the mesh layout used peviously can be used, Pape V. It is athe impotant also to geneate high-quality gid layout nea the solid boundaies and the heat souces (Casey & Wintegeste, 2000, Chen & Sebic, 2001, Vesteeg & Malalasekea, 1995). 57

58 6.6 Exhaust opening Thee is vey little impact on bounday conditions of an exhaust opening to oom aiflow. Howeve it is a athe impotant paamete fo influencing the numeical stability. The last component fo complete ventilation is the specification of the exhaust condition. Zeo gadients fo all flow paametes wee used as a BC fo the exhaust opening in a nomal diection. The aiflow ate is distibuted with a pedetemined atio though the outlets. 6.7 Modelling of solid contaminants and thei souce(s) The modelling of solid contaminants, i.e. paticles in indoo envionments, is a demanding task. The paticles oiginate fom indoo souces and outdoo souces as well. Pobably suface aiflow movements will affect the mateial as well as paticle emissions (Zhang & Haghighat, 1997). In this thesis modelling of solid contaminants is assessed by simplified means. Paticles wee supplied by ATDs into the oom o some sepaate paticle souce was implemented in the simulation, Papes I-II, IV, VIII. The paticle souce was typically modelled usually as a tiny inlet with small inlet velocity and tubulence. Paticles in the ai wee modelled as a vitual seconday phase within the pimay phase ai using the dift-flux model. Sometimes this modelling appoach is called the single-fluid appoach. Paticle velocity, v pa (m/s), is defined as the velocity of the paticle phase, v p (m/s): (m/s), compaed to the velocity of pimay (ai) phase va v pa = v v (44) p a The concentation of paticles is solved calculating the slip velocity, i.e. paticle elative velocity in the ai, v pa (m/s). The mixtue model makes use of an algebaic slip fomulation. The basic assumption of the algebaic slip mixtue model is to pescibe an algebaic elation fo the elative velocity, a local equilibium between the phases should be eached ove a shot spatial length scale. Following Manninen et al., (1996) the fom of the elative velocity is given similaly to Eq. (20) fo paticle settling velocity v pa ( ρ ρ ) = 2 d p m p a 18µ F (45) tub D hee ρ m (kg/m 3 dv ) is the mixtue density and a = (m/s 2 ) is the seconday-phase paticles dt acceleation. The mixtue density in the dift-flux model can be expessed by n ρ = α ρ (46) m k= 1 k k 58

59 whee α k (nd) is the volume faction of phase k, simila to concentation. One should still keep in mind that the mixtue model uses the so-called single fluid appoach (Fluent Inc, 2003). The mixtue model in the pogam solves the continuity equation fo the mixtue, the momentum equation fo the mixtue, the enegy equation fo the mixtue and the volume faction equation fo the seconday phases, as well as algebaic expessions fo the elative velocities (if the phases ae moving at diffeent velocities). The mixtue model allows accommodating k diffeent paticle sizes within in a single pimay ai phase, in ou case 3 diffeent sub-models Modelling of paticle behaviou souces & sinks The modelling of paticle souces is toublesome, because paticles oiginate fom many diffeent places, fom indoos and outdoo souces. In mathematical modelling, one question always aises, whee to put the souce. In this thesis the point souce is neglected and paticles ae modelled though ai inlets. As mentioned befoe, the cuent model uses Euleian appoach to teat paticles evenly distibuted in the contol volume. The continuum citeion is valid when thee ae enough paticles in the computational element (one finite volume) so that statistically aveage popeties can be assumed, see Eq. (37). In this sense the model is vey diffeent compaed to Langangian discete phase modelling as the Langangian model solves paticle equations fo each paticle individually. If one assumes that outdoo ai contains a cetain amount of paticles, C in (kg/m 3 ), then the paticle souce could be given at the supply opening with an aiflow pofile. Paticles ae assumed to be fully mixed with the ambient ai when cetain aveage popeties ae assumed fo the contol volume. m& in πd ρ p = CinQin = 6 V 3 p N v da in in (47) if the velocity is constant ove the supply opening bounday, dain, then integation is not necessay. In geneal paticle souces wee modelled by tiny inlets as in Pape VIII. Paticles wee supplied into the oom togethe with incoming ai fom two small ectangula slots, 0.05 m x 0.05 m, nea the floo. Simulations in Pape VIII showed that such a souce simulates well a floo-type paticle souce such as a capet o a dusty floo. This souce could be also combined with a souce oiginating fom the ATD. In CFD softwae often the volume faction, α k, of contaminant is used when inseting the souce. The paticle souce, m& in (kg/s), with supply opening, A in (m 2 ), and with an unifom velocity, v in (m/s), can be calculated as follows, m& = ρ α v A (48) in p k in in The contaminant in the ai will be calculated by its momentum flow ate, Fpa (N), ceated by the mass geneation, m& (kg/s), and elative velocity, v pa (m/s), of contaminant mixtue. If the velocity conditions ae not favouable then settling will occu, 59

60 F = m (49) & pa v pa In Eq. (49) it is possible to obseve that the gavitational settling only occus when the elative velocity, v pa (m/s), is highe than the ai velocity, va (m/s). Additionally paticle mass tanspot in diffeent pats of the oom is vey much dependent on velocity conditions in the oom. The paticle loss though deposition is dependent on wall gid, because when the paticle eaches the fist gid point in the wall, deposition will occu. Theefoe, the velocity conditions nea walls ae vey impotant as they influence the paticle e-suspension into the ambient ai. Fo instance, convective heat souces can bing paticles back to the aiflow, this means that the floo in Fig. 17 can be consideed to be eithe a paticle sink o souce, Pape I. Because of this dynamic behaviou it is sometimes vey had to detemine whee the paticle souces and sinks ae. Additionally paticle behaviou is geneally influenced by the aiflow conditions in the indoo envionment, Pape III. Fig. 17. Settling paticles e-enteing convective ai plumes That is why the modelling of paticles in thee sub-models is justified, as the behaviou of paticles clealy is a function of thei mass. The paticles ae behaving in the geneal aiflows in a simila way as was tested in Pape IV fo an isothemal case. Some expected paticle dispesion pattens nea humans ae shown in Fig. 18. Lage paticles, goup 3, have a stong settling behaviou. Measuements demonstate that supe micon paticles, goup 2, will settle in low velocity aiflows and, at the same time, they may be bought up to highe elevations with local convective heat souces. Small, sub micon paticles, goup 1, follow the aiflow almost exactly as depicted in Fig

61 Fig. 18. Ai and paticle behaviou aound a human body. Paticle goups ae indicated by symbols 1, 2 and Finite volume method and discetization Discetization in a oom (computational space) equies the flow field to be divided into small contol volumes. As mentioned befoe the cuent thesis used mainly one type of gid topology: hexahedal mesh. The softwae used a contol volume-based technique to convet the govening equations to algebaic equations that wee solved numeically. This contol volume technique consists of integating the govening equations about each contol volume, yielding discete equations that conseve each quantity on a contol-volume basis. Discetization of the govening equations can be illustated most easily by consideing the steady-state consevation equation fo tanspot of a scala quantity φ (nd). This is demonstated by the following equation witten in integal fom fo an abitay contol volume, V (m 3 ), as follows: ρφv da = Γφ φ da + V S φ dv (50) Hee the scala quantity epesents discetization of each of the thee velocity components u,v,w (m/s), the kinetic enegy of tubulence, k (J/kg), the dissipation ate, ε (m 2 /s 3 ), and ai enthalpy, h a (kj/kg). φ is the gadient of scala quantity in all thee dimensions, ρ (kg/m 3 ) is the fluid element density, Γ φ (m 2 /s) is the diffusion coefficient fo φ and S φ (nd) souce of φ pe unit of volume. A fist-ode scheme accoding to the Taylo expansion seies is used in computing the tubulence quantities k and ε. All quantities at cell faces ae detemined by assuming that the cellcente values of any field vaiable epesent a cell-aveage value and hold thoughout the entie cell; the face quantities ae identical to the cell quantities (Vesteeg & Malalasekea, 1995). Thus when the fist-ode upwind scheme is selected, the face value, φ f, is set equal to the cell-cente value of φ in the upsteam cell. Howeve, most of the vaiables calculated in CFD wee discetized with second-ode accuacy (convection + diffusion tems) and quantities at cell faces 61

62 wee computed using a multidimensional linea econstuction appoach (Pantaka, 1980). In this appoach, highe-ode accuacy is achieved at cell faces though a Taylo seies expansion of the cell-cented solution about the cell centoid. Thus, when a second-ode upwind scheme is selected, the face value, φ f, is computed using the following expession, φ = φ + φ s (51) f whee φ and φ ae the cell-cented value and its gadient in the upsteam cell, and s is the displacement vecto fom the upsteam cell centoid to the face centoid. This fomulation equies the detemination of the gadient φ in each cell. This gadient is computed using the divegence theoem, which in discete fom is witten as 1 φ = V N faces f ~ φ f A (52) Hee the face values, ~ φ f, ae computed by aveaging φ fom the two cells adjacent to the face. Discetization of pessue was typically pefomed using a body-foce weighted scheme povided by the softwae and pessue-velocity coupling was achieved by SIMPLE o SIMPLEC algoithms (Pantaka, 1980, Vesteeg & Malalasekea, 1995). A lineaised fom of the discetization equation of paticle concentation is obtained fom Pantaka s SIMPLE appoach (1980): o cc C cc = N nb= 1 o nb C nb + S0 (53) whee the subscipt cc epesents cell cente and nb the neighbouing points. The numbe of neighbouing points, N, the coefficients o cc, o nb and the souce tem, S 0, of the discetization equation depend on the used discetization schemes. The computational stability and the coectness of the CFD solution depend vey much on used discetization schemes, gid layout and unde-elaxation factos given to the segegated solve. Because the CFD calculation contain non-lineaities of the equation sets solved, it is necessay to contol the change of φ. This is achieved by unde-elaxation, which educes the change of φ poduced in each iteation. In a simple fom, the new value of the vaiable φ within a cell depends upon the old value, φ old, the computed change in φ, φ, and the unde-elaxation facto, R (nd), as follows: φ = φold + R φ (54) Iteation numbe and the convegence citeia ae inteelated. Because of these non-lineaities in the poblem the solution pocess is contolled via unde-elaxation factos and all the govening equations ae solved sequentially (i.e., segegated fom one anothe). Typically iteations wee used to obtain conveged solutions fo all solved equations. Convegence citeia 62

63 wee typically set to 10-4 fo fluid as well as paticle concentation equations and 10-7 fo the enegy equation. 6.9 Walls and solid boundaies All the sufaces in an indoo space, such as walls, ceilings, floos and the funitue sufaces, ae consideed as walls. In the close egion of the wall, the aiflow is lamina and convective heat tansfe occus between the flow and the wall sufaces. Fo instance, in Pape II the wall function teatment in the numeical simulations was unable to fully deal with the convective heat tansfe fom the solid boundaies. In the egion vey close to the wall, the aiflow is lamina, and often the convective heat tansfe occus between the flow and the sufaces in this egion. This is still a poblem fo many tubulence models, such as the standad k-ε model. In this thesis many of the investigations focused on fully tubulent flow motion, theefoe one can undestand that the cuent model pedicted aiflow behaviou nea the tested diffuses well, but could have some limitations in coectly pedicting the heat tansfe fom the walls, Pape II. The nea-wall gid is also impotant fo handling paticle calculations as deposition teatment on solid boundaies is still gid-dependent in ou calculations (Holmbeg & Li, 1998). Additionally, it is necessay to specify wall functions fo the desciption of fiction and heat tansfe. In most of the CFD calculations the wall tempeatues ae pescibed. The BCs ae applied to wall sufaces by supplying the suface tempeatue. One can undestand that the local heat tansfe coefficient is an impotant paamete fo expessing the heat flow between the wall and the fist gid node. The heat flow, P c A w, between the wall and the fist gid node is P c c ( T T ) = α (55) nw w whee P c (W/m 2 ) is the local convective heat tansfe fom the walls when A w (m 2 ) is set to 1, α c (W/m 2 K) is the local convective heat tansfe coefficient fom the fluid side, T nw (K) is the ai tempeatue at the fist gid node and T w (K) is the suface tempeatue at the wall. Only in Pape IV the tempeatue was not specified on oom wall sufaces and adiabatic oom conditions wee used. Radiation heat tansfe, P ad (W/m 2 ), is consideed indiectly by pescibing the suface tempeatues of floo, ceiling and walls. In this way the tempeatue at the wall suface tempeatue adjacent to a fluid cell can be defined by using a heat flux, P c P ad, at the wall suface as in the calculation example, Pape IX. P P c ad T w = + α c T nw (56) Anothe impotant featue in wall teatment is choice of wall functions. Wall functions ae a collection of semi-empiical fomulas and functions that in effect link the solution vaiables in the nea-wall cells and the coesponding quantities on the wall. The wall functions contain laws of the wall fo mean velocity, tempeatue and tubulence quantities. The standad wall functions wee used thoughout the thesis (Vesteeg & Malalasekea, 1995). Moe infomation about wall 63

64 dimensionless values such as non-dimensional velocity, v +, and dimensionless length nea wall, y +, and wall functions can be found in seveal publications (Bohus, 1997, Chen & Sebic, 2001, Vesteeg & Malalasekea, 1995). Howeve, one has to undestand the limitations of wall functions unde cetain flow conditions which depat too much fom the ideal conditions. The softwae then povides enhanced wall teatment fo such situations (Fluent Inc, 2003) CFD modelling based on integated design pocess Due to a lack of standadization of CFD simulation pocedues the autho poposes a static way to link diffeent softwae packages to assist CFD simulations and to impove the quality of the final esult. Though inteopeability, diffeent softwae tools used in the design pocess of buildings can povide some new featues to the CFD simulation pocess. The amount of manual modelling and numeical input values can be deceased using computation esults fom othe pogams, as shown in Pape IX. Using aleady existing data geneated in vaious stages of a building poject can educe both the time consumption and the cost. The integated design pocess is a new method to systemise building pojects in a way that ealie calculations and design pocesses could pomote the quality of the CFD simulation. The ealie moe pimitive computations could help to identify whee the athe complicated CFD simulation is equied. Pefoming the aiflow simulations based on eliable numeical BCs (bounday conditions) ceates a unique possibility to optimise the indoo envionment by compaing diffeent system configuations. This new method helps the enginee to choose the best altenative ensuing cetain design citeia and taget levels concening themal o contamination aspects, Pape IX. The integated CFD modelling appoach is pesented in Fig. 19. Fig. 19. CFD simulation pocedue based on an integated design pocess 64

65 The integated design pocess can be used fo optimising the ventilation system s layout by compaing the effect of distibution solutions, system layout constuctions and souce locations on contaminant concentations. 65

66 66 Ai Diffusion and Solid Contaminant Behaviou in Room Ventilation a CFD Based Integated Appoach

67 7 RESULTS The esults fom this thesis have evealed the full complexity of CFD simulations in buildings. The gatheing of input data fo modelling and geneation of BCs fo CFD is vey complex and not fully undestood. The cuent thesis used many diffeent methods to pomote CFD modelling. They ae listed below: Liteatue studies of ai diffuse BCs and paticles Intoducing simplified models of ai diffuses o Low velocity devices o Swil diffuse o Industial ai diffuse Validation of simplified models by laboatoy measuements Full-scale measuements in the laboatoies concening o Paticle behaviou o Aiflow behaviou o Themal behaviou New infomation on how the intenal heat souce location can influence the heat emoval efficiency by using validated simplified diffuse models o Full-scale measuements of 10 diffeent cases o CFD simulations of these cases o Compaison of simulation and measuement esults Impoved method fo evaluating the ventilation efficiency in emoving paticles o By compaing diffeent oom ventilation configuations Mixing ventilation Displacement ventilation Diffeent positions of ATDs o Using combined multi-zone and CFD methods to evaluate paticle concentations in diffeent pats of the oom o Using a new type of sub-models fo diffeent-sized paticles Pesenting a new method fo pefoming the CFD simulations integated design pocess 7.1 Liteatue studies A quite extensive liteatue study on paticles is pesented in Pape III. Futhemoe, pat of the validation of the cuent paticle model is based on measuements found in liteatue. The esults fom liteatue wee used when developing the simplified models of ai diffuses and compaing the esults fom diffeent simulations to liteatue esults and measuements, Papes V-VII. The esults fom liteatue eveal that the whole CFD modelling pocedue needs moe systematisation. CFD modelling needs a moe systematic way of impoving the modelling pocedue and geneation of BCs. This impovement could patially be achieved though an integated design pocess whee fo instance design citeia and input values, as given in Pape IX, could be adopted fom othe building design disciplines. Futhemoe, CFD-specific 67

68 modelling citeia discussed in Pape IX should be followed by the standad pocedues given in CFD guidebooks (Casey & Wintegeste 2000, Chen & Sebic, 2001). 7.2 Full-scale laboatoy measuements An impotant goal fo this wok was to investigate how the ai distibution influences the paticle concentation in diffeent pats of a ventilated oom. The fist goal was to develop an ai diffuse o ventilation configuation which could efficiently contol the aibone paticle distibution in the occupied zone to an acceptable level. The tested diffuse was a low velocity diffuse called Floomaste. Fig. 20. Full-scale laboatoy oom fo measuements and numeical simulations All the tests and CFD simulations wee compaed in full-scale assuming: Equality in geomety and co-odinate system Equality in used bounday conditions. This means: o Equal ai supply/exhaust teminal locations and souce conditions o Equal oom conditions, including suface tempeatues in the oom Equal flow conditions in the oom. This means: o Identical tempeatue and velocity conditions 68

69 o Identical paticle concentation conditions The main esults fom the tests in Fig. 20 ae pesented in Pape II. This pape evealed poblems with geneating constant paticle concentation in the oom, Fig. 21. The paticle concentation vaied consideably with time as shown in Fig. 10. Fig. 21. The paticle geneato located in the test chambe The low velocity diffuse Floomaste was tested in the ABB laboatoy in Enköping. Additionally, diffeent-sized paticles wee measued in the same laboatoy hall. Some of the esults of the paticle measuements ae pesented in APPENDIX C. The measuements in test ooms veifying the CFD simulations ae descibed in Papes II, V-VII. The swil diffuse and the industial ai diffuse simplified CFD models wee validated by fullscale measuements done in a laboatoy oom of the Finnish Institute of Occupational Health. Fig. 22. Laboatoy test hall in the Finnish Institute of Occupational Health, see also Papes V- VII 69

70 In the test facility shown in Fig. 22 two types of ai diffuses wee used fo testing the heat emoval efficiency. Totally 10 diffeent configuations wee utilised to test the aiflow and themal behaviou in the laboatoy test oom, Pape VII. It is expected that contaminants behave in the same way as excess heat and theefoe also contaminant emoval efficiency was measued in paallel, Pape VII. The esults evealed that the ai distibution is athe impotant fo an efficient design as to the heat and contaminant emoval. Most of the measuements wee pefomed as a collaboation wok and one can acknowledge uncetainties duing the computecontolled measuements. Due to huge amounts of measuement data geneated by the computecontolled measuement equipment, the study focused moe on handling data popely. Fo instance, nea zone validation of the industial ai diffuse model in APPENDIX A used (17 x 4 = 68) measuement points fo one measuement line. Thee velocity components and tempeatue wee measued simultaneously in one measuement point. Measuements of ai velocity and tempeatue wee time aveaged ove 60 s and the values wee ecoded with a data logge (in the swil diffuse case 180 s), Pape VI. Totally 680 measuements points wee used to validate the industial ai diffuse pefomance in the nea zone, Pape V. In the swil diffuse case, 34 x 4 = 136 points wee used and totally 1360 points wee utilised in the validation pocess in Pape VI and APPENDIX B. Totally 10 diffeent ventilation configuations wee tested in the same test facility, 5 cases fo the industial ai diffuse and 5 cases fo the swil diffuse, Pape VII. 10 diffeent test conditions used 28 x 24 x 4 = 2688 points fo vetical plane measuements of thee velocity components and the ai tempeatue. Hoizontal plane measuements used 11 x 28 x 4 = 1232 points. Fo that eason each tested case poduced 3920 measuement points and totally points wee used fo visualisation and validation of all the cases. The time spent, fo instance, measuing the industial ai diffuse cases was 327 hous, appoximately two weeks aound the clock. The cases with the swil diffuse took thee times longe, because of the extended measuement time of 180 seconds fo one measuement point. This kind of measuements cannot be pefomed by humans, but was achieved by a compute-contolled tavesing system and with a data logge. An example of the measuement esults is pesented in APPENDIX D; it was used fo validating the swil diffuse model in Pape VI. 7.3 Simulation esults Oiginal simulation esults ae pesented in all papes except in Pape III which was almost entiely focused on liteatue eseach. All the esults indicated that the ai diffusion in a oom is a complex combination of many factos such as supply aiflow pofile, diffuse type, location of heat souces, heat souce stength and intenal obstuctions. Room ventilation is caused patly by the aiflow supplied fom ai diffuses and additionally by the buoyancy-diven flows fom heat o cold sufaces, Figs and Papes V & VII. That is why it is essential to model the ai diffuses fo the CFD simulation case as accuately as possible, Papes V-VII. Contaminant o paticle behaviou within a ventilated space is entiely a function of ai diffusion which is discussed in Papes I-II, IV & VIII. 70

71 Fig. 23. Ai diffusion in the laboatoy oom in Fig. 22 ventilated with industial ai diffuses Fig. 24. Tempeatue distibution in the laboatoy oom in Fig. 22 ventilated with industial ai diffuses Lage paticles settle in the egions whee low velocity conditions occu. This is poven both by measuements and simulations. Due to the complex natue of the CFD modelling even the evaluation of the esults is difficult. The esults fom simulation ae given in all cells in the computational domain. The usual suface cuts ae not always the best method to pesent the esults. In this thesis a new type of evaluation method fo paticle behaviou is pesented in 71

72 Pape VIII. Paticle simulation esults in CFD combined with multi-zone modelling appoach epesent a poweful method to undestand how paticles actually behave in aiflow and whee they ae to be found in a oom. This method can assist in finding the best ventilation configuation by pefoming compaative simulations. Fo instance, the position of the diffuse can be tested to find out how it influences the beathing zone concentation, C bz. The simulation esults of testing the influence of diffuse location at isothemal conditions fo paticle concentation in the occupied zone ae pesented in Pape IV. The compaison of diffeent simulations is a way to optimise the system layout to ensue cetain design citeia and taget levels. The design citeia pesented in Fig. 11 ae impotant fom the achitect s point of view concening the oom layout and spatial elationships of intenal obstuctions. The design of a ventilated oom is impotant fo oom aiflow distibution as well as paticle behaviou. That is why it is necessay to use sophisticated methods and pocedues to optimise the ventilation system s pefomance, as in Pape IX. In Pape IX the ventilation pefomance in an office was optimised by using cooled beam ai distibution. 7.4 Quality and evaluation of the esults The quality of the simulation esults was assued by vaious means. When compaing the esults eithe a single point value o a whole set of points was assessed. When validating, pincipally measuement value and simulation value wee compaed, as in Pape V. The quality of the simulation esults was achieved by: Compaing a single point measuement value to the simulation value, Papes V-VI Compaing a whole set of values in the gaph in a given compaison aea, Pape V-VI Visualisation of measuement esults and simulation esults, Papes V-VI Smoke visualisation in the full-scale laboatoy hall, Pape V The paticle simulation esults wee epoted and assessed in numeous aticles. Additionally, fo typical gaphs and suface cuts the simulation esults wee calculated by: Integating ove the supply and exhaust openings to calculate the paticle flux, Papes I-II, IV, VII-VIII Volume integation ove cetain volumes such as the beathing zone, H = m, and the occupied zone, H = m, above the floo height, Pape IV Repoting esults in iso-sufaces with cetain concentation, as in Pape I. Dividing the oom into diffeent zones and epoting the concentation in these zones. This was a way to evaluate the paticle mass tansfe in diffeent pats of a oom, Pape VIII. Compaing the oom aveage concentation to the concentation in the investigated location, Papes II, IV, VIII. 7.5 Accuacy of the esults The deficiencies o inaccuacies of CFD simulations can be elated to a vaiety of eos and uncetainties. These can be geneation of BCs, CFD-elated models and methods, human lack of 72

73 knowledge and so on. This thesis mainly focuses on integating simulation-elated pocedues and to ecognise and ovecome the poblems duing the CFD modelling. Thee ae two main poblem categoies when pefoming the simulations and they can be divided into: Eos: Recognizable deficiency that is not due to lack of knowledge Uncetainly: A potential deficiency that is due to lack of knowledge (Casey & Wintegeste, 2000). In othe wods, unknown poblems appeaing duing the simulation. It is assumed that the cuent thesis can contibute to impoved knowledge so as to decease the eos in the CFD pocess. Uncetainty issues ae left out as thee ae no staightfowad methods on how to assess uncetainties in CFD simulation. One needs to be awae of the limitations and capabilities of CFD in modelling an indoo envionment. The end esult of any CFD simulation is dependent on Choice of BCs o Numeical values fo physical wall boundaies o Numeical values fo supply/exhaust openings Geometical uncetainties o Geometical epesentation of objects within the modelling o Ovesimplification is not ecommended as the geometical objects alte the indoo aiflow field. This concens all the objects embedded in the modelling such as ATDs, funitue, walls, humans and othe objects Mesh quality in the computational domain o Numeical diffusion o Gid design close to ATDs o Gid dependence tests should be pefomed Choice of models o Tubulence modelling o Code eos o Specific model eos known by solving a specific poblem with an unsuitable model Choice of discetization technique o Numeical eo epesented in each cell because of discetization of govening equations o Fist ode vs. highe ode schemes Numeical values as unde-elaxation factos fo segegated solve and othes Convegence citeia o Caused by iteative solution technique o Scaled esiduals should each pe-given limits. In the nomal case 10-4 fo flow equations. The esidual R φ computed by segegated solve in Eq. (53) is the imbalance in the concentation equation summed ove all the computational cells. This is efeed to as the unscaled esidual. The scaled esiduals ae computed by compaing a scaling facto epesentative of scala quantity, φ, though the computational domain. Use eos o Depends vey much on the peson who actually pefoms the simulation 73

74 In indoo envionment, the aiflow diffusion is typically diven by natual convection, foced convection o mixed convection. The calculation of aiflow field coupled with solid contaminants makes the indoo poblem vey complex. Conclusively, paticle calculation in indoo ai is dependent on all the factos mentioned above and the quality of the paticle model itself. That was the main eason why the cuent thesis used simila numeical methods thoughout all the papes, because fo instance the limitations of the k-ε tubulence model ae known, but still it seemed to be the most suitable model fo simulating indoo ai tubulence. Accuacy of the esults was assessed by simplified means: Compaing simulated esults to esults found in the liteatue, Pape III Compaing simulated esults to the measuement esults, Papes II & V-VII Compaing diffeent simulation esults with each othe, Pape IX If the simulation esults ae compaed to each othe it is necessay to ecognise the limitations of the simulated esults. Still, when an acceptable quality of CFD simulation esults is maintained CFD can be used fo optimizing the ventilation system s pefomance, as in Pape IX. This is the main eason why the accuacy of the simulation should always be maintained at an acceptable level. If one consides using the paticle model pesented in this thesis it needs to be ecognised that the model only accounts fo the paticle settling and the deposition at physical boundaies by simplified means. To captue the dynamic behaviou of paticles in indoo ai one needs to use a moe sophisticated model. At the moment thee ae vey few CFD models which can account fo paticle coagulation, nucleation, Bownian diffusion. Even if such a model exists it is always a poblem to evaluate the esults in a pope way, because of poblems of 3D space-time epesentation. In geneal, the accuacy of simulated esults was assessed by compaing them to measuement esults, as in Papes V-VII. The esults quantitatively showed some deficiencies in modelling ai diffuses. The gid layout was a vey impotant facto fo the accuacy as the BCs wee epesented on the bounday of the simplified model. If the gid was too coase numeical diffusion took place and the simulated velocity in the nea zone of the diffuse was unacceptably low compaed to the measued values. Additionally, the gid layout close to heat souces was impotant because it had a stong impact on the plume geneated above the heat souce, Pape VIII. The accuacy of paticle simulations was assessed mainly based on esults found in the liteatue and pevious validations. 74

75 8 GENERAL DISCUSSION AND CONCLUSIONS The esults fom this thesis eveal the full complexity of pefoming CFD simulations of an indoo aiflow field coupled with solid paticles. The thesis emphasises the impotance of the many aspects of modelling. The quality of paticle simulation esults is patly detemined by the quality of the indoo aiflow field simulation. As the indoo aiflow field is geatly dependent on ai supply conditions it is necessay to have high quality models of ai diffuses. The cuent thesis suffeed limitations in the full-scale laboatoy measuements fo aibone paticles. The laboatoy measuements patly failed and the simulation esults wee patly validated by the esults found in the liteatue. Nevetheless, measuements, liteatue and the simulation esults evealed that the locations contaminated by paticles in a oom ae whee the low ai velocity conditions occu. That was the main eason why aiflow simulations wee extensively pefomed to numeically test diffeent design altenatives. As the heat souces geneate plumes which ae a cucial pat of the indoo aiflow field, mainly non-isothemal conditions wee simulated except in one pape. All the simulations evealed that the ai distibution to the space is impotant as it detemines the occupied zone tempeatue, the concentation of contaminants and is a vaiable fo the enegy efficiency via use of the aiflow ate. In this thesis CFD simulation is intoduced as a design tool to impove the system layouts in pe-design. It is expected that with validated numeical tools, system designs may be significantly impoved befoe the actual constuction phase. At the same time it is impotant to follow the guidelines to assue a cetain quality of these simulations. Still, the liteatue study has evealed, fo instance, that thee is no standad way to geneate the BCs fo ai diffuses, because of thei geometical complexity. This is why the CFD simulations still ae extensively used by univesities, but have gained athe low populaity among engineeing companies. The amount of paametes given to simulations and methods should be moe standadised. Lately, some guidebooks on how to follow cetain quality citeia in pefoming the simulations have been published (Casey & Wintegeste, 2000, Chen & Sebic, 2001). The gatheing of input data and geneation of BCs is still given small attention which pevents CFD usage becoming widespead. Futue studies should impove the modelling, consideing all aspects of CFD modelling to make it a moe feasible tool in the nomal building design pocess. That is why all the design disciplines should be integated into one coe to impove the data exchange and to educe the manual data input. Futue studies should integate and standadise most of the pocedues fo CFD modelling. This will enable the use of CFD modelling moe widely among people who do not have a deep knowledge in fluid and mathematical modelling, without compomising the modelling accuacy. 75

76 76 Ai Diffusion and Solid Contaminant Behaviou in Room Ventilation a CFD Based Integated Appoach

77 9 REFERENCES Anand, N. K. and A. R. McFaland, 1989: Paticle Deposition in Aeosol Sampling Lines Caused by Tubulent Diffusion and Gavitational Settling. Am. Ind. Hyg. Assoc. Jounal, 50, Amitage, A. K., J. R. Ashfod and J. W. Good, 1997: Foum. Envionmental Tobacco Smoke - Is it eally a cacinogen? Med. Sci. Res., 25, 3-7. Awbi, H. B., 1998: Enegy Efficient Room Ai Distibution. Renewable Enegy, 15, Behne, M., 1999: Indoo ai quality in ooms with cooled ceilings. Mixing ventilation o athe displacement ventilation? Enegy and Buildings, 30, Bjøn, E., 2001: Mechanical and anthopometic consideations fo physical and vitual modelling of human beings. Poceedings of the fouth intenational meeting on themal manikins, 4IMM, at the Swiss Fedeal Laboatoies fo Mateials Testing and Reseach (EPMA), St Gallen, Switzeland, 27-28, Septembe Bohus, H., 1997: Pesonal Exposue to Contaminant Souces in Ventilated Rooms, Engineeing and Science, Aalbog Univesity, PhD thesis, 264 pp. Bohus, H. and P. V. Nielsen, 1995: Pesonal Exposue to Contaminant Souces in a Unifom Velocity Field. Poceedings of Healthy Buildings '95, Fouth Intenational Confeence on Healthy Buildings, Milan, Italy, Septembe, pp Busch, B., G. Feon, E. Kag, A. Silbeg and J. Heyde, 1995: The Gowth of Atmospheic Paticles in Moist Ai. Jounal of Aeosol Science, 26, S435-S436. Cafoa, M. F., F. Esposito and C. Seio, 1998: Numeical methods fo etieving aeosol size distibutions fom optical measuements of sola adiation. Jounal of Aeosol Science, 29, Casey, M. and T. Wintegeste, 2000: ERCOFTAC Special Inteest Goup on "Quality and Tust in Industial CFD -Best Pactise Guidelines. Fluid Dynamics Laboatoy, Sulze Innotec, EU, 94 pp. Chen, Q. and J. Sebic, 2001: How to Veify, Validate, and Repot Indoo Envionment Modeling CFD Analyses. ASHRAE, Atlanta, 58 pp. Davidson, L., 2003: An Intoduction to Tubulence Models. Depatment of Themo and Fluid Dynamics, Chalmes Univesity of Technology, Götebog. Diociaiuti, M., M. Balduzzi, B. De Beadis, G. Cattani, G. Stacchini, G. Ziemacki, A. Maconi and L. Paoletti, 2001: The Two PM 2,5 (Fine) and PM 2,5-10 (Coase) Factions: Evidence of Diffeent Biological Activity. Envionmental Reseach Section, A86,

78 Djuanedy, E., 2000: Numeical modelling of supply openings fo oom ai distibution, Building Science, National Univesity of Singapoe, M.Sc. Thesis, 91 pp. Djuanedy, E. and K. W. D. Cheong, 2002: Development of a simplified technique of modelling fou-way ceiling ai supply diffuse. Building and Envionment, 37, Einbeg, G., 2001: Ventilation and the stable climate - a facto of animal well-being and poduction, Institutionen fö Enegiteknik. Avdelningen fö Uppvämnings- och ventilationsteknik, Kungl. Tekniska Högskolan, Stockholm, Licentiate thesis, 177 pp. Esposito, F., G. Pavese, F. Romano and C. Seio, 1995: Daily vaiation of the Aeosol Size Distibution at a Rual Location in Southen Italy. Jounal of Aeosol Science, 26, S75-S76. Fan, Y., 1995: CFD modelling of the ai and contaminant distibution in ooms. Enegy and Buildings, 23, Fange, P. O., 1970: Themal Comfot, Technical Univesity of Denmak. Fange, P. O., A. K. Melikov, H. Hanzawa and J. Ring, 1988: Ai Tubulence and Sensation of Daught. Enegy and Buildings, 12, FLUENT Inc, 2003: FLUENT 6.1 Use's Guide, Digital Copy Fontaine, J. R., R. Rapp, H. Koskela and R. Niemelä, 2002: Evaluation of CFD-Modelling Methods fo Ai Difffuses. RoomVent 2002: 8th Intenational Confeence on Ai Distibution in Rooms, Copenhagen, Denmak, pp Fishman, F., M. Hussainov, A. Katushinsky and Ü. Rudi, 1999: Distibution chaacteistics of the mass concentation of coase solid paticles in a two-phase tubulent jet. Jounal of Aeosol Science, 30, Hackshaw, A. K., M. Law and N. J. Wald, 1997: The accumulated evidence on lung cance and envionmental tobacco smoke. Bitish Medical Jounal, 315, Hagstöm, K., 2002: Influence of Kinetic Enegy Souces and Intenal Obstuctions on Room Ai Conditioning Stategy, Efficiency of Ventilation and Room Velocity Conditions, Depatment of Mechanical Engineeing, Laboatoy of Heating, Ventilating and Ai Conditioning, Helsinki Univesity of Technology, PhD thesis, 43 pp. Hais, S. J. and M. M. Maieq, 2001: Signatue size distibutions fo diesel and gasoline engine exhaust paticulate matte. Jounal of Aeosol Science, 32, Hayashi, T., Y. Ishizu, S. Kato and S. Muakami, 2002: CFD analysis on chaacteistics of contaminated indoo ai ventilation and its application in the evaluation of the effects of contaminant inhalation by a human occupant. Building and Envionment, 37,

79 Heikkinen, J., 1990: Numeical pediction of oom ai flows. In Finnish. Vol. 705, Reseach Repots, Espoo, 83 pp. Hinds, C. M., 1999: Aeosol technology: popeties, behaviou, and measuement of aibone paticles. Wiley-Intescience, UK, 504 pp. Holmbeg, S. and Y. Li, 1998: Modelling of the Indoo Envionment - Paticle Dispesion and Deposition. Indoo Ai, 8, Hovath, H., M. Kasahaa and P. Pesava, 1996: The Size Distibution and Composition of the Atmospheic Aeosol at a Rual and Neaby Uban Location. Jounal of Aeosol Science, 27, Hu, S. C., 2003: Aiflow chaacteistics in the outlet egion of a votex oom ai diffuse. Building and Envionment, 38, Huo, Y., F. Haghighat, J. S. Zhang and C. Y. Shaw, 2000: A systematic appoach to descibe the ai teminal device in CFD simulation fo oom ai distibution analysis. Building and Envionment, 35, Jones, A. P., 1999: Indoo ai quality and health. Atmospheic Envionment, 33, Junge, C., 1955: The size distibution and aging of natual aeosols as detemined fom electical and optical data on the atmosphee. Jounal of Meteoology, 12, Kocifaj, M. and J. Lukac, 1995: Size Distibution of Submicon Paticles. Jounal of Aeosol Science, 26, S253-S254. Koskela, H., 2004: Momentum souce model fo CFD - simulation of nozzle duct ai diffuse. Enegy and Buildings, 36, Kühne, 1995: Enegy- and Mass Tansfe in Rooms with Displacement Ventilation. Poceedings of Healthy Buildings '95, Fouth Intenational Confeence on Healthy Buildings, Milan, Italy, Septembe Lange, C., 1995: Indoo Deposition and the Potective Effect of Houses against Aibone Pollution, National Laboatoy Risø, Denmak, PhD thesis. Launde, B. E. and D. B. Spalding, 1974: The numeical computation of tubulent flow. Comput. Methods Mech. Eng., 3, Luo, S., 2003: Numeical Study of Thee Dimensional Tubulent Flows in a Habitat With Coupled Heat and Mass Tansfe, Mechanics, Univesity of the Mediteanean (Univesity of Aix-Maseille II). Lyubovtseva, S., 1995: On Composition Distibution of Atmospheic Aeosols. Jounal Aeosol Science, 26, S

80 Manninen, M., V. Taivassalo and S. Kallio, 1996: On the mixtue model fo multiphase flow. Technical Reseach Cente of Finland, Espoo, VTT Publications 288. Mathiesen, V., T. Solbeg and B. H. Hjetage, 2000: Pedictions of gas/paticle flow with an Euleian model including a ealistic paticle size distibution. Powde Technology, 112, Matson, U., 2004: Ultafine paticles in indoo ai - measuements and modelling, Building Sevices Engineeing, Chalmes Univesity of Technology, PhD thesis, Götebog, 76 pp. Mattsson, M., 2002: Vetical Distibution of Occupant Geneated Paticles in a Room with Displacement Ventilation. Indoo Ai 2002: Poceedings of the 9th Intenational Confeence on Indoo Ai Quality and Climate, Santa Cuz, Califonia, Mime, A., E. Tamm and M. Fische, 1995: Vaiability of Aeosol Concentation and the Fine Stuctue of Aeosol Size Distibution. Jounal of Aeosol Science, 26, S73-S74. Mundt, E., 1995: Displacement Ventilation Systems - Convection Flows and Tempeatue Gadients. Building and Envionment, 30, Mundt, E., 1996: The Pefomance of Displacement Ventilation Systems - Expeimental and Theoetical Studies, Building Sevices Engineeing, KTH (Royal Institute of Technology, Stockholm, Ph.D thesis, 155 pp. Muakami, S., S. Kato and J. Zeng, 1996: CFD Analysis Aound Human Body. Poceedings of INDOOR AIR '96. The 7th Intenational Confeence of Indoo Ai Quality and Climate, Nagoya, Japan, July 21-26, pp Muakami, S., S. Kato, S. Nagano and Y. Tanaka, 1996: Diffusion chaacteistics of aibone paticles with gavitational settling in a convection-dominant indoo flow field. ASHRAE Tansactions, 98, Nagasawa, Y. and Y. Kondo, 2002: Modeling of Complex Ai Diffuse fo CFD simulation Pat I and II. RoomVent 2002: 8th Intenational Confeence on Ai Distibution in Rooms, Copenhagen, Denmak, pp Nielsen, P. V., 1992: Desciption of Supply Openings in Numeical Models fo Room Ai Distibution. ASHRAE Tansactions, 98, Nielsen, P., 2000: Velocity distibution in a oom ventilated by displacement ventilation and wall-mounted ai teminal devices. Enegy and Buildings, 31, Omstad, H., 2000: Suspended paticulate matte in indoo ai: adjuvants and allegen caies. Toxicology, 152, Owen, M. K., D. S. Enso and L. E. Spaks, 1992: Aibone Paticle Sizes and Souces Found in Indoo Ai. Atmospheic Envionment, 26A,

81 Pantaka, S. V., 1980: Numeical Heat Tansfe and Fluid Flow. McGaw-Hill Book Company, London, 197 pp. Peng, S.-H., 1998: Modelling of Tubulent Flow and Heat Tansfe fo Building Ventilation, Chalmes Univesity of Technology, Götebog, PhD thesis, 83 pp. Popiolek, Z., 1981: Poblems of Testing and Mathematical Modelling of Plumes Above Human Body and Othe Extensive Heat Souces. KTH. Institutionen fö uppvämning- och ventilationsteknik, A4-seien Rodes, C. E., R. M. Kamens and R. W. Wiene, 1991: The Significance and Chaacteistics of the Pesonal Activity Cloud on Exposue Assessment Measuement fo Indoo Contaminants. Indoo Ai, 2, Seppänen, O. A. and W. J. Fisk, 2004: Effect of ventilation on health and othe human esponses in office envionment. Roomvent 2004 Poceedings of 9th Intenational Confeence on Ai Distibution in Rooms, Coimba, Potugal. Skistad, H., E. Mundt, P. V. Nielsen, K. Hagstöm and J. Railio, 2002: Displacement Ventilation in Non - Industial Pemises, Guidebook n. 1. REHVA, EU, 50 pp. Vesteeg, H. K. and W. Malalasekea, 1995: An Intoduction to Computational Fluid Dynamics - The Finite Volume Method. Pentice Hall, London, 272 pp. Voutilainen, A. and J. P. Kaipio, 2001: Estimation of non-stationay aeosol size distibutions using the state-space appoach. Jounal of Aeosol Science, 32, Zhang, G. and F. Haghighat, 1997: The Impact of Suface Ai Movement on Mateial Emissions. Building and Envionment, 32, Zoe, Y., 2001: Ai jets in ventilation applications, Building Sevices Engineeing, Royal Institute of Technology (KTH), Stockholm, PhD Thesis, 63 pp. 81

82 82 Ai Diffusion and Solid Contaminant Behaviou in Room Ventilation a CFD Based Integated Appoach

83 APPENDIX A Bounday conditions fo industial ai diffuse 83

84 Measuement and simulation esults in one compaison point Compaison of measuement and simulation esults with industial ai diffuse 84

85 APPENDIX B Bounday conditions fo swil diffuse 85

86 Measuement and simulation esults in one compaison point 86

87 Compaison of measuement and simulation esults with swil diffuse 87

88 88 Ai Diffusion and Solid Contaminant Behaviou in Room Ventilation a CFD Based Integated Appoach

89 APPENDIX C Paticle measuements in displacement ventilated oom at 2.0 metes Paticle measuements in displacement ventilated oom at 1.3 metes Paticle measuements in displacement ventilated oom at 0.4 metes 89

90 Paticle measuements in mixing ventilation at 2.0 metes Paticle measuements in mixing ventilation at 0.4 metes Paticle measuements at exhaust outlet 90

91 APPENDIX D Measuement esults fo swil diffuse case in a vetical plane Q in = 380 l/s. All the 10 measuement cases wee measued in a simila way. See also Pape VII. Velocity vectos in vetical plane Velocity vectos in hoizontal plane 91

92 Velocity conditions V a, m/s Velocity conditions v x, m/s 92

93 Velocity conditions v y, m/s Velocity conditions v z, m/s 93

94 Tempeatue conditions T, C 94