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Building Simulation

, Volume 12, Issue 2, pp 231–246 | Cite as

Importance-driven approach for reducing urban radiative exchange computations

  • José Pedro AguerreEmail author
  • Eduardo Fernández
  • Benoit Beckers
Research Article Building Thermal, Lighting, and Acoustics Modeling
  • 25 Downloads

Abstract

In the context of large scale urban heat transfer simulation, the prediction of radiative flux at short and long wave spectra is a step necessary to obtain accurate results. From a computational perspective, this task is expensive because realistic conditions require calculations in many sensors, considering multiple radiation bounces, and evaluating many hundred daylighting conditions. Radiosity-based approaches are adequate methods for processing the large number of diffuse surfaces that are usually present in city models. However, the high memory consumption of these algorithms turns them inefficient for handling big geometries, and therefore ray tracing techniques are commonly used. In this article we present a study on using the importance concept to improve the performance of radiosity calculations at the urban scale. The algorithm is able to consider diffuse and specular materials, and it proves to be a viable alternative to ray tracing. Since most of the information contained in big city models is not needed for simulating a selected zone of interest, the computational requirements can be reduced drastically. Several experiments are conducted to test the approach, and promising results are reported.

Keywords

urban physics radiative exchange radiosity importance 

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Notes

Acknowledgements

The work was partially supported by project FSE_1_2017_1_144731 from Agencia Nacional de Investigación e Innovación (ANII, Uruguay). The authors would like to express their gratitude to Per Christensen for his kind cooperation.

References

  1. Aguerre JP, Fernández E, Besuievsky G, Beckers B (2017). Computing urban radiation: A sparse matrix approach. Graphical Models, 91: 1–11.MathSciNetCrossRefGoogle Scholar
  2. Ashdown I, Jackson C, Spahn J, Saemisch T (2017). Licaso and daysim. Technical report, Lighting Analysts Inc.Google Scholar
  3. Beckers B (2013a). Solar Energy at Urban Scale. Hoboken, NJ, USA: John Wiley & Sons.CrossRefGoogle Scholar
  4. Beckers B (2013b). Taking advantage of low radiative coupling in 3D urban models. In: Proceedings of the Eurographics Workshop on Urban Data Modelling and Visualisation.Google Scholar
  5. Bekaert P, Willems YD (1995). Importance-driven progressive refinement radiosity. In: Hanrahan PM, Purgathofer W (eds), Rendering Techniques’ 95. Eurographics. Vienna: Springer, pp. 316–325.Google Scholar
  6. Blocken B (2015). Computational fluid dynamics for urban physics: Importance, scales, possibilities, limitations and ten tips and tricks towards accurate and reliable simulations. Building and Environment, 91: 219–245.CrossRefGoogle Scholar
  7. Bourgeois D, Reinhart CF, Ward G (2008). Standard daylight coefficient model for dynamic daylighting simulations. Building Research & Information, 36: 68–82.CrossRefGoogle Scholar
  8. Chen SE, Rushmeier HE, Miller G, Turner D (1991). A progressive multi-pass method for global illumination. In: Proceedings of the 18th Annual Conference on Computer Graphics and Interactive Techniques, pp. 165–174.Google Scholar
  9. Christensen PH, Stollnitz EJ, Salesin DH, DeRose TD (1996). Global illumination of glossy environments using wavelets and importance. ACM Transactions on Graphics, 15: 37–71.CrossRefGoogle Scholar
  10. Christensen PH (2003). Adjoints and importance in rendering: An overview. IEEE Transactions on Visualization and Computer Graphics, 9: 329–340.CrossRefGoogle Scholar
  11. Cohen MF, Greenberg DP (1985). The hemi-cube: A radiosity solution for complex environments. In: Proceedings of the 12th Annual Conference on Computer Graphics and Interactive Techniques, pp. 31–40.Google Scholar
  12. Cohen MF, Wallace J (1993). Radiosity and Realistic Image Synthesis. San Diego, CA, USA: Academic Press.zbMATHGoogle Scholar
  13. Crawley DB, Lawrie LK, Pedersen CO, Winkelmann FC, Witte MJ, et al. (2004). EnergyPlus: New, capable, and linked. Journal of Architectural and Planning Research, 21: 292–302.Google Scholar
  14. Devabhaktuni V, Alam M, Depuru SSSR, Green II RC, Nims D, Near C (2013). Solar energy: Trends and enabling technologies. Renewable and Sustainable Energy Reviews, 19: 555–564.CrossRefGoogle Scholar
  15. Döllner J, Buchholz H (2005). Continuous level-of-detail modeling of buildings in 3D city models. In: Proceedings of the 13th Annual ACM International Workshop on Geographic Information Systems, pp. 173–181.Google Scholar
  16. Ebrahimpour A, Maerefat M (2010). A method for generation of typical meteorological year. Energy Conversion and Management, 51: 410–417.CrossRefGoogle Scholar
  17. Fernández E, Aguerre JP, Beckers B, Besuievsky G (2016). Optimizing window shape for daylighting: An urban context approach. In: Proceedings of Eurographics Workshop on Urban Data Modelling and Visualisation.Google Scholar
  18. Geisler-Moroder D, Lee ES, Ward GJ (2017). Validation of the five-phase method for simulating complex fenestration systems with radiance against field measurements. In: Proceedings of the 15th International IBPSA Building Simulation Conference, San Francisco, USA.Google Scholar
  19. Gibson S, Hubbold RJ (1996). Efficient hierarchical refinement and clustering for radiosity in complex environments. Computer Graphics Forum, 15: 297–310.CrossRefGoogle Scholar
  20. Ho CK, Ghanbari CM, Diver RB (2011). Methodology to assess potential glint and glare hazards from concentrating solar power plants: Analytical models and experimental validation. Journal of Solar Energy Engineering, 133: 031021.CrossRefGoogle Scholar
  21. Huttner S, Bruse M (2009). Numerical modeling of the urban climate—A preview on ENVI-met 4.0. In: Proceedings of the 7th International Conference on Urban Climate, Yokohama, Japan.Google Scholar
  22. Immel DS, Cohen MF, Greenberg DP (1986). A radiosity method for non-diffuse environments. In: In: Proceedings of the 13th Annual Conference on Computer Graphics and Interactive Techniques, pp. 133–142.Google Scholar
  23. Jones NL (2017). Validated interactive daylighting analysis for architectural design. PhD Thesis, Massachusetts Institute of Technology, USA.Google Scholar
  24. Jones NL, Reinhart CF (2014). Irradiance caching for global illumination calculation on graphics hardware. In: Proceedings of ASHRAE/IBPSA-USA Building Simulation Conference, pp. 111–120.Google Scholar
  25. Kajiya JT (1986). The rendering equation. In: Proceedings of the 13th Annual Conference on Computer Graphics and Interactive Techniques, pp. 133–142.Google Scholar
  26. Lewis RW, Nithiarasu P, Seetharamu KN (2004). Fundamentals of the Finite Element Method for Heat and Fluid Flow. Chichester, UK: John Wiley & Sons.CrossRefGoogle Scholar
  27. Luebke D, Reddy M, Cohen JD, Varshney A, Watson B, Huebner R (2003). Level of Detail for 3D Graphics. San Francisco, USA: Morgan Kaufmann.Google Scholar
  28. Marschner SR (1998). Inverse rendering for computer graphics. PhD Thesis, Cornell University, USA.Google Scholar
  29. MATLAB (2010). MATLAB Version 7.10. The MathWorks Inc.Google Scholar
  30. McNeil A, Lee ES (2013). A validation of the radiance three-phase simulation method for modelling annual daylight performance of optically complex fenestration systems. Journal of Building Performance Simulation, 6: 24–37.CrossRefGoogle Scholar
  31. Meyer RX (1999). Elements of Space Technology. San Diego, CA, USA: Academic Press.Google Scholar
  32. Muñoz D, Beckers B, Besuievsky G, Patow G (2018). A technique for massive sky view factor calculations in large cities. International Journal of Remote Sensing, 39: 4040–4058.CrossRefGoogle Scholar
  33. Musialski P, Wonka P, Aliaga DG, Wimmer M, van Gool L, Purgathofer W (2013). A survey of urban reconstruction. Computer Graphics Forum, 32: 146–177.CrossRefGoogle Scholar
  34. Neumann A, Neumann L, Bekaert P, Willems YD, Purgathofer W (1996). Importance-driven stochastic ray radiosity. In: Pueyo X, Schröder P (eds), Rendering Techniques’ 96. Eurographics. Vienna: Springer, pp. 111–121.Google Scholar
  35. Overby M, Willemsen P, Bailey BN, Halverson S, Pardyjak ER (2016). A rapid and scalable radiation transfer model for complex urban domains. Urban Climate, 15: 25–44.CrossRefGoogle Scholar
  36. Parish YIH, Müller P (2001). Procedural modeling of cities. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 301–308.Google Scholar
  37. Pattanaik SN, Mudur SP (1993). Efficient potential equation solutions for global illumination computation. Computers & Graphics, 17: 387–396.CrossRefGoogle Scholar
  38. Perez R, Seals R, Michalsky J (1993). All-weather model for sky luminance distribution—Preliminary configuration and validation. Solar Energy, 50: 235–245.CrossRefGoogle Scholar
  39. Pharr M, Jakob W, Humphreys G (2016). Physically Based Rendering: From Theory to Implementation, 3rd edn. Cambridge, MA, USA: Morgan Kaufmann.Google Scholar
  40. Prikryl J, Bekaert P, Purgathofer W (2000). Importance-driven hierarchical stochastic ray radiosity. In: Proceedings of WSCG.Google Scholar
  41. Reinhart CF, Herkel S (2000). The simulation of annual daylight illuminance distributions—A state-of-the-art comparison of six RADIANCE-based methods. Energy and Buildings, 32: 167–187.CrossRefGoogle Scholar
  42. Reinhart CF, Mardaljevic J, Rogers Z (2006). Dynamic daylight performance metrics for sustainable building design. LEUKOS, 3: 7–31.Google Scholar
  43. Robinson D, Haldi F, Kämpf J, Leroux P, Perez D, Rasheed A, Wilke U (2009). CitySim: Comprehensive micro-simulation of resource flows for sustainable urban planning. In: Proceedings of the 11th International IBPSA Building Simulation Conference, Glasgow, UK, pp. 1614–1627.Google Scholar
  44. Robinson D, Stone A (2005). A simplified radiosity algorithm for general urban radiation exchange. Building Services Engineering Research and Technology, 26: 271–284.CrossRefGoogle Scholar
  45. Shao M-Z, Peng Q-S, Liang Y-D (1988). A new radiosity approach by procedural refinements for realistic image sythesis. In: Proceedings of the 15th Annual Conference on Computer Graphics and Interactive Techniques, pp. 93–99.Google Scholar
  46. Sillion F, Puech C (1989). A general two-pass method integrating specular and diffuse reflection. In: Proceedings of the 16th Annual Conference on Computer Graphics and Interactive Techniques, pp. 335–344.Google Scholar
  47. Sillion FX, Arvo JR, Westin SH, Greenberg DP (1991). A global illumination solution for general reflectance distributions. In: Proceedings of the 18th Annual Conference on Computer Graphics and Interactive Techniques, pp. 187–196.Google Scholar
  48. Smits B, Arvo J, Greenberg D (1994). A clustering algorithm for radiosity in complex environments. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, pp. 435–442.Google Scholar
  49. Smits B, Arvo JR, Salesin DH (1992). An importance-driven radiosity algorithm. In: Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques, pp. 273–282.Google Scholar
  50. Strømann-Andersen J, Sattrup PA (2011). The urban canyon and building energy use: Urban density versus daylight and passive solar gains. Energy and Buildings, 43: 2011–2020.CrossRefGoogle Scholar
  51. Suykens F, Willems YD (2000). Density control for photon maps. In: Péroche B, Rushmeier H (eds) Rendering Techniques 2000. Eurographics. Vienna: Springer, pp. 23–34.CrossRefGoogle Scholar
  52. Tregenza PR, Waters IM (1983). Daylight coefficients. Lighting Research & Technology, 15 65–71.CrossRefGoogle Scholar
  53. Ward GJ, Rubinstein FM, Clear RD (1988). A ray tracing solution for diffuse interreflection. In: Proceedings of the 15th Annual Conference on Computer Graphics and Interactive Techniques, pp. 85–92.Google Scholar
  54. Ward GJ (1994). The RADIANCE lighting simulation and rendering system. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, pp. 459–472.Google Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • José Pedro Aguerre
    • 1
    Email author
  • Eduardo Fernández
    • 1
  • Benoit Beckers
    • 2
  1. 1.Centro de Cálculo, Facultad de IngenieríaUniversidad de la RepúblicaMontevideoUruguay
  2. 2.Urban Physics Joint LaboratoryUniversité de Pau et des Pays de l’Adour, I2S UPPAAngletFrance

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