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Evaluation and comparison of various fast fluid dynamics modeling methods for predicting airflow around buildings

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Abstract

Computational fluid dynamics (CFD) methods are being increasingly used for predicting airflow fields around buildings, but personal computers can still take tens of hours to create a single design using traditional computing models. Considering both accuracy and efficiency, this study compared the performances of the conventional algorithm PIMPLE, fast fluid dynamics (FFD), semi-Lagrangian PISO (SLPISO), and implicit fast fluid dynamics (IFFD) in OpenFOAM for simulating wind flow around buildings. The effects of calculation parameters, including grid resolution, discrete-time step, and calculation time for these methods are analyzed. The results of the simulations are compared with wind tunnel tests. It is found that IFFD and FFD have the fastest calculation speeds, but also have the largest discrepancies with test data. The PIMPLE algorithm has the highest accuracy, but with the slowest calculation speed. The calculation speeds of the FFD, SLPISO, and IFFD models are 6.3, 3 and 13.3 times faster than the PIMPLE model, respectively. The calculation accuracy and speed of the SLPISO model are in between those of the IFFD, FFD and PIMPLE models. An appropriate algorithm for a project may be chosen based on the requirements of the project.

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Abbreviations

C 1ε, C 2ε :

constants related to k-ε model

e(i):

error caused by test equipment instrument, 10%

F s :

safety factor, 1.25

G k, G b :

turbulence kinetic energy due to the mean velocity gradients and buoyancy, respectively

P :

static pressure

p*, p**:

pressure at the intermediate time steps

p n :

pressure at the previous time steps

p n+1 i :

pressure at the current time steps

S(i):

simulation result

t :

current time

Δt :

discrete-time step

T(i):

test data

T* :

computation time

u*, u**:

air velocity at the intermediate time steps

U i :

the ith component of the velocity vector, i=1,2,3

u n :

air velocity at the previous time steps

u n+1 i :

air velocity at the current time steps

ε G :

relative error between the solution of two grids

ν :

kinematic viscosity

μ :

dynamic viscosity

μ t :

turbulent viscosity

ρ :

density

σ k, σ ε :

turbulent Prandtl number for k and ε, respectively

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Acknowledgements

This study was financially supported by the National Key R&D Project “Research on key technologies for environmental protection and energy saving of industrial buildings with high pollution emission” (No. 2018YFC0705300), the National Natural Science Foundation of China Youth Fund Project “Fast reverse identification of indoor multiple gaseous pollutant sources” (No. 51708084), and the joint research project of the Wind Engineering Research Center, Tokyo Polytechnic University (MEXT (Japan) Promotion of Distinctive Joint Research Center Program grant number: JPMXP0619217840, JURC grant number: 192013).

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Authors and Affiliations

  1. School of Civil Engineering, Dalian University of Technology, Dalian, 116024, China

    Shu Zheng, Yu Xue & Lin Duanmu

  2. Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, CO, 80309, USA

    Zhiqiang John Zhai

  3. Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA

    Yi Wang

  4. KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden

    Wei Liu

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  1. Shu Zheng
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  2. Zhiqiang John Zhai
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  3. Yi Wang
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  4. Yu Xue
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  5. Lin Duanmu
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  6. Wei Liu
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Correspondence to Zhiqiang John Zhai.

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Zheng, S., Zhai, Z.J., Wang, Y. et al. Evaluation and comparison of various fast fluid dynamics modeling methods for predicting airflow around buildings. Build. Simul. 15, 1083–1095 (2022). https://doi.org/10.1007/s12273-021-0860-1

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  • DOI: https://doi.org/10.1007/s12273-021-0860-1

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