SET Proceedings 2978-4387 Caravel Press 10.65582/set.2008.128 2008.128 Understanding the Impact of Parametric Uncertainty in Validating Building Thermal Behaviour in Computational Fluid Dynamics (CFD) SmithStefán Thor laxsts@nottingham.ac.uk WuShenyi RiffatSaffa School of the Built Environment, University of Nottingham, Nottingham, NG7 2RD, UK 24082008 7 1 978-1-995850-08-5 966 972 24 August 2008 © 2008 Copyright WSSET 2008

Understanding the uncertainty in modelling procedures is important to provide confidence in model predictions, but is rarely carried out. Uncertainty in CFD model parameters resulting from empirical investigation and applying accepted standards are assessed to validate model behaviour. The results show that a basic structured approach to uncertainty considerations (i.e. sensitivity analysis) in a steady-state CFD model result in different conclusions of model behaviour than otherwise achieved by a standard deterministic evaluation.

 validation uncertainty sensitivity CFD 24 to 27 August 2008 7th International Conference on Sustainable Energy Technologies SET2008 7 Seoul, Korea Z. J. Zhai, Developing an integrated building design tool by coupling building energy simulation and computational fluid dynamics programs. thesis, MIT (2003). I. Beausoleil-Morrison, The adaptive coupling of heat and air flow modeling within dynamic whole-building simulation. thesis, Strathclyde University (2000). Z. J. Zhai, Q. Y. Chen, P. Haves, J. H. Klems. 2003. On approaches to couple energy simulation and computational fluid dynamics programs. Energy and Buildings. 35. 493. Z. J. Zhai, Q. Y. Chen. 2006. Sensitivity Analysis and application guides for integrated building energy and CFD simulation. Energy and Buildings. 38. 1060. I. MacDonald, Quantifying the effects of Uncertainty in Building Simulation. thesis, Strathclyde (2002). M. S. d. Wit, Uncertainty inpredictions of thermal comfort in Buildings. thesis, TUD (2001). P. Aude, L. Tabary, P. Depecker. 2000. Sensitivity analysis and validation of buildings' thermal models using adjoint-code method. Energy and Buildings. 31. 267. J. Pfafferott, S. Herkel. 2007. Statistical simulation of user behaviour in low-energy office buildings. Solar Energy. 81. 676. S. T. Smith, S. Wu, S. Riffat. 2007. Case Study: Method of Investigation of a Commercial Building's Comfort Levels. Highlighting Energy Efficiency Concerns. Sustainable Energy Technology International Conference. 6.