Mechanism validation and optimization with Optima++

Transcription

1 Mechansm valdaton and optmzaton wth Optma++ Introducton and general workflow, post-processng Carsten Olm Insttute of Chemstry, Eötvös Unversty (ELTE), Budapest, Hungary MTA-ELTE Research Group on Complex Chemcal Systems, Budapest, Hungary COST Tranng School on the Analyss of Combuston Mechansms July 4 7, 206 Budapest, Hungary DAY 3, Practcal Sesson 3-2

2 The ReSpecTh nformaton system Webste: Reacton knetcs Hgh-resoluton molecular Spectroscopy Thermochemstry data 2

3 Reacton knetcs data n ReSpecTh Webste: The content of the Reacton knetcs branch. Database of combuston experments Collecton of combuston mechansms Utlty codes 3

4 Motvaton Why do we compare and optmze mechansms? Comparson of exstng mechansms Investgate the sutablty of publshed mechansms Provde a gudelne for users of reacton mechansms Identfy a canddate for further mprovement through optmzaton Fnd explanatons for potental shortcomngs of the mechansms n ther actual chemstry Mechansm optmzaton Develop a mechansm that descrbes a large number of combuston experments well Approach real physcal values of the reacton rate coeffcents rather than just fttng them Descrbe the remanng uncertantes of the obtaned model 4

5 Optmzaton methodology of Turány et al. T. Turány et al., Int. J. Chem. Knet. 44 (202) Collecton of ndrect measurements Senstvty analyss Collecton of drect measurements of reacton rate coeffcents for the selected reactons Selecton of reacton rate parameters to be optmzed Estmaton of the pror uncertanty doman of the Arrhenus parameters Determnaton of the optmal set of parameters - Mnmzaton of the error functon by usng a global optmzaton method - Calculaton of the posteror covarance matrx of all parameters Better rate parameters + better estmaton of ther accuracy! 5

6 General workflow Collecton of lterature data - References n revew artcles/mechansm papers - Searchng for ctatons (e.g. n Web of Scence) - Databases (e.g. IST Chemcal Knetcs Database) - etworkng wth expermentalsts Encodng data nto a standard format - st choce: tabulated values n paper/supp. Mat. - 2 nd choce: requestng raw data from authors - 3 rd choce: manual dgtzaton of plots - Standardzed (e.g. XML-based) formats recommended also useful for data storage ew project Mechansm optmzaton - Assemblng an ntal mechansm - Decdng on parameters to be optmzed - Estmatng pror rate coeffcent uncertantes - Selecton of optmzaton targets (ndrect measurements, drect and theoretcal rate determnatons) - Creatng restart fles and/or response surfaces - Montorng the progress of optmzaton Mechansm valdaton - Collectng reference mechansms from lterature - Smulatons at the condtons of the collected data - (sem-)automatc comparson va the evaluaton of a least-square error functon - Identfyng structural dfferences, comparng senstvtes at varous condtons, pathway analyss 6

7 General workflow Collecton of lterature data - References n revew artcles/mechansm papers - Searchng for ctatons (e.g. n Web of Scence) - Databases (e.g. IST Chemcal Knetcs Database) - etworkng wth expermentalsts Encodng data nto Practcal sesson 4- a standard format (Thursday, 2pm 3pm) - st choce: tabulated values n paper/supp. Mat. - 2 nd choce: requestng raw data from authors - 3 rd choce: manual Tamás dgtzaton Varga of plots - Standardzed (e.g. XML-based) formats recommended also useful for data storage ew project Mechansm optmzaton Practcal sesson Assemblng an ntal mechansm - Decdng (Thursday, on parameters 3:30pm to be optmzed 4:30pm) - Estmatng pror rate coeffcent uncertantes - Selecton of optmzaton targets (ndrect measurements, drect and Tamás theoretcal Varga rate determnatons) - Creatng restart fles and/or response surfaces - Montorng the progress of optmzaton Mechansm valdaton Practcal sesson Collectng reference mechansms (now) from lterature - Smulatons at the condtons of the collected data - (sem-)automatc comparson va the evaluaton of a least-square Carsten error functon Olm - Identfyng structural dfferences, comparng senstvtes at varous condtons, pathway analyss 7

8 Overvew: Optma++ framework TXT_TO_XML (XML_TO_TXT) Actons not requrng smulatons XML_TO_CKII XML_TO_FM Data and mechansm manpulaton MECHMOD Optma++ Actons requrng smulatons MECHTEST SESITIVITY OPTIMIZATIO Model valdaton and mprovement FlameMaster (FM) only! 8

9 Smulaton framework In-house C++ based framework Optma++ used for multple smulaton runs n parallel mode, senstvty analyss and optmzaton FlameMaster (and CHEMKI-II) packages utlzed All experments were converted nto the ReSpecTh Knetcs Data (RKD) XML format 9

10 Optma++ MECHTEST Performs smulatons at the condtons of experments usng a detaled chemcal mechansm Ignton delay measurement Lamnar flame speed measurement (.e. lamnar burnng veloctes) Outlet concentraton measurement Concentraton tme profle measurement Jet strred reactor measurement (or perfectly strred reactor data) Drect rate coeffcent determnaton Flame smulatons requre pre-exstng solutons from a flame database (FLAME_DATABASE) 0

11 Optma++ MECHTEST: Inputs - RKD format XML fle(s) contanng expermental data - Mechansm nput fles to be compled/modfed (also transport data fle, f D smulatons are carred out) - Optma++ nput fle for the mechansm test to be performed

12 Optma++ MECHTEST: Outputs - Compled mechansm - mechtestresults: contans all smulaton results - Several fles contanng addtonal nformaton (e.g. on faled runs) - Screen output (n Release verson) or debug fle (Debug) - ext step: plots comparng smulatons and experments 2

13 Comparng smulaton results Case study: Syngas combuston Correspondng artcle appeared n 205: Smlar artcle for hydrogen combuston: Olm et al., Combust. Flame 6 (204), Further artcles are work-n-progress. 3

14 Comparng smulaton results Case study: Syngas combuston (Olm et al., CF 205) 4

15 Comparng smulaton results Case study: Syngas combuston (Olm et al., CF 205) 5

16 Comparng smulaton results Case study: Syngas combuston (Olm et al., CF 205) How dd we calculate all these numbers? 6

17 outgen: a smulaton result post-processor Expermental condtons, results and smulaton results are read from plan text fles Multple mechansms can be handled smultaneously Error functon and the absolute devaton functon are automatcally calculated for a subset of ponts selected Selecton of data to be ncluded n the comparson by applyng certan flterng crtera (e.g. by measurement type, + addtonal evaluatons (e.g. correlatons, weghtng) experment type, condtons) + analyss of senstvty data 7

18 outgen: a smulaton result post-processor A manual s avalable: 8

19 outgen: a smulaton result post-processor control_fle mech_data raw_data sen_data data selecton, post-processng optons smulaton results and expermental data expermental condtons senstvty analyss data (optonal) outgen (optonal) (optonal) general nfo about the selecton data results by data pont, dataset and overall weghted results, correlatons Average senstvtes mn/max, rankngs, hstograms 9

20 The error functon Utlzaton for the comparson of mechansms E = umber of data seres mod exp Y Y = = j j j exp σ ( Y ) j 2 Dfference of modeled and expermental value (characterzes the predcton of one measured value) umber of ponts (dvson makes data seres dfferng n sze equally weghted) Estmated standard devaton / scatter (makes dfferent types of experments comparable, accounts for dfferent relablty of data) Y j = yj ln y j f σ ( y f σ (ln exp j ) exp yj ) constant constant Transformaton (comparson of experments wth absolute and relatve errors) The overall agreement between smulatons and measurements can be well characterzed quanttatvely by ths error functon 20

21 How are my expermental data represented? What we would lke to have: - Even dstrbuton of measurements wthn feasble range of operatng condtons - o duplcates 2

22 How are my expermental data represented? What we often observe: - Uneven dstrbuton of measurements - Multple dentcal or very smlar measurements Some of these condtons are overrepresented! 22

23 Determnng weghtng factors A smple approach (I). Defne physcally meanngful thresholds for all relevant operatng condtons When are data ponts stll smlar? - Statc thresholds for matchng speces concentratons, temperature and pressure - Optonally, dynamc thresholds for pressure, e.g. lamnar burnng veloctes measured at p = 5 atm or p = 5 bar attempt the same target - threshold s (.0325 ) p/atm ±2% (roundng errors) 2. Assess database entres, dentfy n duplcates and double gangers for each data pont 3. Recalculate weghts: w j = ( n + ) 23

24 Determnng weghtng factors A smple approach (II) Example: 5 data ponts n a p-t parameter space; dynamc p threshold, statc T threshold ( K) Unnormalzed weghts / Unque data pont Unque data pont / /2 /3 /2 smlar data pont 2 smlar data ponts smlar data pont 24

25 Modfcaton of the objectve functon Unweghted vs. weghted defnton Tradtonal, unweghted defnton of the objectve functon: E( p) = Y j yj = ln y = j= j f σ ( y f σ (ln y Y ) exp j exp j mod j ( p) Y σ ( Y exp j constant ) constant ) exp j 2 E(p) p Y exp Y mod σ objectve functon for a gven p vector of parameters from a mechansm number of datasets number of data ponts n dataset expermental value modeled value for a gven p estmated standard devaton / scatter Unform weghtng factors (unty, not shown n equaton) ew, weghted objectve functon: E( p) = * 2 mod exp w Yj ( p) Yj w wj = exp = j= σ ( Yj ) = j= *,n w j,n Y mod j ( p) Y σ ( Y exp j ) exp j 2 Data set weght = w wj Effectve * = = j= w Data pont weght to be determned! Effectve * = j= w j = w ormalzed dataset weghts (all values are n average) w w = w w =,n = = = w ormalzed data pont weghts (all values are n average) w j,n = w j j= j= w j = w j j= w j 25

26 Thank you for your attenton! Fnancal support: Hungaran Scentfc Research Fund OTKA (ERA Chemstry grant 00523) Saxon Mnstry of Scence and Fne Arts and the EU (project BoRedKat ) German Federal Mnstry of Food and Agrculture (project Entwcklung von chemschen Mechansmen zur energetschen utzung von Bokraftstoffen ) German Academc Exchange Servce (DAAD), Balass Intézet/ HSB Sandra Hartl, Chrstan Hasse Specal thanks to: István Gy. Zsély Tbor agy 26