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Evaluation and comparison of various fast fluid dynamics modeling methods for predicting airflow around buildings
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2022 (English)In: Building Simulation, ISSN 1996-3599, E-ISSN 1996-8744, Vol. 15, no 6, p. 1083-1095Article in journal (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
Springer Nature , 2022. Vol. 15, no 6, p. 1083-1095
Keywords [en]
calculation accuracy, calculation efficiency, calculation parameters, fast fluid dynamics, Air, Computational fluid dynamics, Efficiency, Wind tunnels, Air-flow fields, Calculation speed, Computational fluid dynamics methods, Fluid-dynamic models, Model method, Semi-Lagrangian, Personal computers
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URN: urn:nbn:se:kth:diva-313608DOI: 10.1007/s12273-021-0860-1ISI: 000722135800003Scopus ID: 2-s2.0-85120644055OAI: oai:DiVA.org:kth-313608DiVA, id: diva2:1667206
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QC 20220610

Available from: 2022-06-10 Created: 2022-06-10 Last updated: 2022-06-25Bibliographically approved

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Liu, Wei

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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