urban context Darren Robinson Group Leader: Sustainable Urban Development / EPFL Visiting Professor: Technical Research Centre of Finland / VTT

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1 Radiation modelling in the urban context Darren Robinson Group Leader: Sustainable Urban Development / EPFL Visiting Professor: Technical Research Centre of Finland / VTT

2 Structure Simulation using cumulative skies: Principles and applications Image processing: morphological indicators Principles and applications Validity Simplified radiosity algorithm Shortwave radiation exchange Longwave radiation exchange and daylighting Applications Conclusions

3 Cumulative sky modelling

4 l i = f ( Z,θ ) χ = Ri = I dh l χ i p i = 1 l i Φ i sin γ i n T R i = Ri, j= 1 j We can also add to this the cumulative solar radiance. Robinson and Stone, PLEA 2004.

5 Winter irradiation: BedZed

6 Winter irradiation: Vauban

7 Winter irradiation: Hammarby

8 rface area, % Viable su Irradiation histograms: Solurban Matthaeus, Basle Meyrin, Geneva Bellevaux, Lausanne Energy threshold, kwh/m 2 Matthaeus facade Bellevaux facade Meyrin facade Matthaeus roof Bellevaux roof Meyrin roof pdf

9 Image Processing: morphological indicators

10 Image procesing of DEMs

11 Indicators of irradiation availability [ 1+ cos( u) ] 2 I d β = I dh β + [ 1+ cos( u) ] 2 V s = β + u = cos 1 ( 2V 1) β s h w = tan u So V s, u and h/w are related. Are they good indicators of urban solar irradiation / illumination availability? For this we need only examine one, say V s.

12 Morphological indicators: accuracy

13 Mean SVF / Irradiation roses E E E E E E E E E E E E E E E E E E E E E E E E E Irradiation SVF, % x Irradiation SVF, % x Irradiation SVF, % x Basel Lausanne Geneva

14 Cumulative SVF / Irradiation roses E E E E E E E E E E E E E E E E E E E E Irradiation Irradiation Irradiation SVF, % x SVF, % x SVF, % x Basel Lausanne Geneva

15 Aggregate performance y = 1.105x 105x R 2 = Meyrin Bellevaux irradiation Simulated Matthaeus Calculated irradiation T T I gβ = I ghvs

16 Simplified Radiosity Algorithm (SRA)

17 Basis of the Simplified Radiosity Algorithm (SRA) 1. Calculate a sky radiance distribution for a discretised sky vault ( Z,θ ) l v = f R i = f ( Z,θ )χ Annual Mean Diffuse Radiance W/m 2 Sr 60 χ p ( Φl sinγ ) I dh v i = 1 p I = d RΦ β i= 1 ( σ cosξ ) Φσ cosξ = cosξ. dφ i i p = 145 (we use the Tregenza discretisation scheme)

18 Radiance distribution v s isotropic max=18.2%, min=-26.3% Radiance distribution v s Perez max=5.2%, min=-4.4%

19 2. Combine obstructions (angular neighbours) within each patch, representing the radiance by the dominant obstructing surface The dominant obstruction is that which provides the largest contribution to: Φ ω cos ξ = cos ξ. d Φ

20 3. Calculate irradiance due to obstructions I ρβ = p ( * R Φω cosξ ) i i= 1 But we also have obstructions below the horizontal plane. For this we can simply define another discretised vault and invert it (upside-down sky), so that: I ρβ = 2 p ( * R Φω cosξ ) i i= 1 For each ith patch we need the radiance of the dominant occlusion: R I p 2 p = ξ + b i i= 1 j = 1 ( ) ( * RΦσ cosξ + R Φω cosξ ) ρ π j

21 SRA Implementation

Render the scene (+/- 45 horizontal, 0-45 vertical). Derive a visibility factor (0-1).

22 Shortwave radiation exchange Step 1 Surface splitting: split into regions of similar sky view factor (a first approximation). Define a surface sampling grid (e.g. 5m spacing). Render the scene (+/- 45 horizontal, 0-45 vertical). Derive a visibility factor (0-1). Split hoz/vert according to some min/max visibility threshold. Recursively split until all regions are within this variation threshold

23 Step 2 Patch view factors: determine patch view factors and identify dominant obstruction. Colour each surface uniquely (blue red; ) Render four wide angle perspective views from each surface s s centroid to capture the entire 180 visible range Calculate the coordinates and solid angle of each pixel Determine sky / obstruction view factors from the surface centroid and the dominant obstructing surface

24 Step 3 Solar view factors: determine the proportion of each surface that t is directly insolated at each hour. For each sun position, render the scene from the sun s view point (using parallel projection). Calculate l the area of each pixel. Sun View Factor = Number of pixels occupied by surface * area of each pixel / area of surface projected perpendicular to sun direction Incident beam irradiance is then simply: I = bξ I bn ψ cosξ t

25 Step 4 Build and solve the matrices: determine energy contribution ti from sun, sky and reflections. I = AI BR I g = I d + I b d g + B Φ Φ = Φ 1,1 2,1 n σ σ 1,1 2,1 M cosξ cosξ 1,1 2,1 Φ 1,2 σ 1,2 O cosξ 1,2 L Φ 1, p σ 1, p M cosξ, 1σ n,1 cosξn,1 Φn,2σ n,2 cosξn,2 L Φn, pσ n, p cosξn O M 1, p, p 145 x n ρ1k π ρ1k A = π M ρ1kn π m 1,1 2,1,1 ρ2k π O ρ k 2 π 1,2 n,2 K O L ρ k n π M M ρ k n π 1, n n, n ( σ σ ) k i, j = Φi, x 1 i, x self, x cosξi, k = 1 k k k x k n 2

26 Comparis sons

27 Current Practice Iso otropic Model Irradian nce (W/m^2) Radiance Raytracer Irradiance (W/m^2) radiance Current Practic ce Isotropic Model Irr (W/m^2) Radiance Raytracer Irradiance (W/m^2) Current practice (Perez tilted surface) versus RADIANCE (gendaylit) Rad diance Distribution Mo odel Irradiance (W/m^2 2) Rad diance Distribution Mo odel Irradiance (W/m^2 2) North Radiance Raytracer Irradiance (W/m^2) South SRA versus RADIANCE (gendaylit) Radiance Raytracer Irradiance (W/m^2)

28 Annual irradiation, MWh/m 2 Radiance SRA for 10mx10m facade discretisation: 6500 surfaces Napier-Shaw Medal, CIBSE 2007 Difference plot: green is within 10%

29 Longwave radiation exchange Calculate an approximate sky temperature ε T00062T d T 4 sky T 4 a ε n o 5 2 (accounting for cloud cover in the sky emittance) Take surface temperatures from a thermal model (for the previous time step) Define a solid angle weighted equivalent temperature. T * 4 1 π = ( Φ ) + ( 4 T cos Φ cos ) sky σ ξ i ω ξt j i= 1 Calculate the longwave exchange: ( 4 4 I = εaσ T T ) 290 j= 1 L * s

30 Daylight yg H 2H 3H Robinson and Stone (2006), Solar Energy 80(3)

31 Applications

32 CitySim: A decision i support tool to support sustainable urban planning and design, based on urban energy flow modelling. Accounting for: Occupants behaviour Urban climate Synergetic energy exchanges Applicable at the range of scales.

34 What questions can we ask? - How should I position my buildings, what form should they take? - What should be the characteristics of my buildings envelopes to best exploit available passive solar resources? - what is the most energy efficient way of satisfying my energy demands? - What are the most best energy conversion systems to integrate? Productivity aids: - Importing and cloning 3D models - idefault datasets - Parallelising the solver - Exporting results pour analyse 34

35 Neuchâtel 440 buildings 4,700 residents Annual A day in winter - Airborne LIDAR + Cadastral = 2.5D model - CENSUS data - Field observations - Energy use: calibration

36 Conclusions Simple morphological indicators are inaccurate RADIANCE is accurate but computationally expensive for time-varying urban-scale applications The SRA is a good compromise It has been applied to good effect for: Urban resource flow modelling Urban climate modelling It could also be incorporated into standard DSPs

37 Thank you!

38 References Robinson, D., Haldi, F., Kämpf, J., Leroux, P., Perez, D., Rasheed, A., Wilke, U., City-Sim: Comprehensive micro-simulation of resource flows for sustainable urban planning, Proc. Eleventh Int. IBPSA Conf: Building Simulation 2009, Glasgow, UK. Robinson, D., Urban morphology and indicators of radiation availability, Solar Energy, 80(12) 2006, p Robinson, D., Stone, A., Internal illumination prediction based on a simplified radiosity algorithm, Solar Energy, 80(3) 2006, p Robinson, D., Stone, A., A simplified radiosity algorithm for general urban radiation exchange, Building Services Engineering Research and Technology, 26(4) 2005, p Robinson, D., Stone, A., Solar radiation modelling in the urban context, Solar Energy, (77)3 2004, p Robinson, D., Stone, A., Irradiation modeling made simple the cumulative sky approach and its applications, Proc. PLEA 2004, Eindhoven 2004.

39 1. Introduction (DR) Part 3: Measuring and optimising urban sustainability Part 1: Climate and comfort 7. Measures of urban sustainability 2. Radiation exchange (DR) (FF, DR) 3. The urban climate (AR, DR) 8. Optimisation of urban 4. Pedestrian comfort (DR, MB) sustainability (JK, DR) Part 2: Metabolism Part 4: An eye to the future 5. Building modelling (DR) 9. Sustainability and land-use 6. Transport modelling (KA) Transport dynamics (MB) 10. Conclusions (DR) Book: Computer modelling for sustainable urban design

AIR-CONDITIONING ENERGY REDUCTION THROUGH A BUILDING SHAPE OPTIMIZATION

AIR-CONDITIONING ENERGY REDUCTION THROUGH A BUILDING SHAPE OPTIMIZATION G. Caruso 1 ; J. H. Kämpf 2 1 Dipartimento di Ingegneria dell Energia e dei Sistemi, Università di Pisa, Largo Lucio Lazzarino, I-56126