Application of coarse-grid computational fluid dynamics on indoor environment modeling: Optimizing the trade-off between grid resolution and simulation accuracy

Computational fluid dynamics has been playing an important role in building design and indoor environment study for decades. However, the computing cost of grid-independent computational fluid dynamics prevents its application from real-time simulation in many areas, such as building emergency evacuation and hourly-based energy simulation. Although coarse-grid computational fluid dynamics has the potential of being as fast as or faster than real time and can provide valuable information for decision making, the credibility of coarse-grid computational fluid dynamics results is questioned due to the unknown error scale it brings. This study investigates the grid-induced error, evaluates the potential computing cost saving by using a coarse grid, and provides a guideline for optimizing the trade-off between grid resolution and computing cost. The numerical error caused by coarse grid can be minimized by appropriately adapting the distribution of grid size. Following the guideline of coarse-grid specifications, coarse-grid computational fluid dynamics can provide informative prediction that is comparable to a grid-independent result on building environment modeling. The computing cost of computational fluid dynamics with an optimized coarse grid is usually orders of magnitude less than that with uniform fine grid.

[1]  Zhao Zhang,et al.  Evaluation of Various Turbulence Models in Predicting Airflow and 1 Turbulence in Enclosed Environments by CFD : Part-1 : 2 Summary of Prevalent Turbulence Models 3 4 , 2007 .

[2]  Jonathan Cohen,et al.  Title: A Fast Double Precision CFD Code using CUDA , 2009 .

[3]  M. Berger,et al.  Adaptive mesh refinement for hyperbolic partial differential equations , 1982 .

[4]  Rui Zhang,et al.  A prototype mesh generation tool for CFD simulations in architecture domain , 2010 .

[5]  F. Schmitt About Boussinesq's turbulent viscosity hypothesis: historical remarks and a direct evaluation of its validity , 2007 .

[6]  B. P. Leonard,et al.  A stable and accurate convective modelling procedure based on quadratic upstream interpolation , 1990 .

[7]  Zhiqiang John Zhai,et al.  Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature , 2007 .

[8]  Tassos G. Karayiannis,et al.  Experimental benchmark data for turbulent natural convection in an air filled square cavity , 2003 .

[9]  Peter V. Nielsen,et al.  Flow in Air Conditioned Rooms , 1974 .

[10]  Qingyan Chen,et al.  On approaches to couple energy simulation and computational fluid dynamics programs , 2002 .

[11]  Qingyan Chen,et al.  Buoyancy-driven single-sided natural ventilation in buildings with large openings , 2003 .

[12]  Peter V. Nielsen,et al.  Specification of a Two-Dimensional Test Case: (IEA) , 1990 .

[13]  Q Chen,et al.  Real-time or faster-than-real-time simulation of airflow in buildings. , 2009, Indoor air.

[14]  Leslie M. Smith,et al.  Renormalization group analysis of turbulence , 2003 .

[15]  X Liu,et al.  Location identification for indoor instantaneous point contaminant source by probability-based inverse Computational Fluid Dynamics modeling. , 2007, Indoor air.

[16]  Hukam C. Mongia,et al.  An unconditionally-stable central differencing scheme for high Reynolds number flows , 1987 .

[17]  Johan Meyers,et al.  CFD for model-based controller development , 2004 .

[18]  Qingyan Chen,et al.  A zero-equation turbulence model for indoor airflow simulation , 1998 .

[19]  Sankaran Sundaresan,et al.  Coarse-Grid Simulation of Gas-Particle Flows in Vertical Risers , 2005 .