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Site-specific prediction for energy simulation by integrating computational fluid dynamics

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Abstract

Currently, energy simulations (ES) utilize various outdoor variables such as outside surface, ground surface, sky, and air temperature, using coefficients or simplified equations. These variablesuse empirical correlations that are sometimes insufficient in certain location. Variables such as air temperature at the base of the layer are informed from weather data that may not accurately represent the physical microclimate of the site, and may therefore reduce the accuracy of simulation results. This research investigates utilizing computational fluid dynamics (CFD) with Monte Carlo stochastic model to predict site specific temperature parameters for energy simulation. This will allow more realistic and robust energy simulation results for specific site conditions.

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Abbreviations

f(x):

probability density function (PDF)

F(x):

cumulative distribution function (CDF)

Pr :

probability

t :

time

T :

temperature (°C)

x :

variate

X :

random variable

ϕ :

normal distribution PDF

μ :

mean

σ :

deviation

σ 2 :

variance

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Authors and Affiliations

  1. Building Simulation Group, School of Design, University of Pennsylvania, 207 Meyerson Hall, 210 South 34th Street, Philadelphia, PA, 19104-6311, USA

    Yun Kyu Yi & Ali M. Malkawi

Authors
  1. Yun Kyu Yi
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  2. Ali M. Malkawi
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Correspondence to Yun Kyu Yi.

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Yi, Y.K., Malkawi, A.M. Site-specific prediction for energy simulation by integrating computational fluid dynamics. Build. Simul. 1, 270–277 (2008). https://doi.org/10.1007/s12273-008-8422-3

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  • DOI: https://doi.org/10.1007/s12273-008-8422-3

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