Dr Marta Camps Santasmasas M.CampsSantasmasas@salford.ac.uk
Lecturer
Dr Marta Camps Santasmasas M.CampsSantasmasas@salford.ac.uk
Lecturer
Xutong Zhang
Ben Parslew
Gregory F. Lane-Serff
Joshua Millar
Alistair Revell
In modelling turbulent flow around buildings, the computational domain needs to be much larger than the immediate neighbourhood of the building, resulting in computational costs that are excessive for many engineering applications. Two nested models are presented to solve this problem, with an outer domain calculated using a Reynolds Averaged Navier Stokes (RANS) solver in both cases. The inner region is calculated using large eddy simulation (LES) from both a lattice Boltzmann (LB) and a Navier Stokes (NS) based solver. The inner domains use the mean RANS velocity as boundary conditions for the top and the side boundaries and incorporate the RANS turbulence using a synthetic eddy method (SEM) at the inner domain inlet. Both models are tested using an atmospheric boundary layer flow around a rectangular building at 𝑅𝑒𝐻 = 47,893, comparing the computational resources spent and validating the results with experimental measurements. The effect of the inlet turbulence, the size of the domain and the cell size are also investigated. Both LB and NS based simulations are able to capture the physics of the flow correctly and show good agreement with the experimental results. Both simulation frameworks were configured to run in a similar computational time, so as to compare the computational resources used. Due to the use of GPU programming, the approach based on LB was estimated to be 25 times cheaper than the NS simulation. Thus these results show that a nested LB-LES solver can run accurate wind flow calculations with consumer level/cloud based computational resources.
Camps Santasmasas, M., Zhang, X., Parslew, B., Lane-Serff, G. F., Millar, J., & Revell, A. (2022). Comparison of Lattice Boltzmann and Navier-Stokes for Zonal Turbulence Simulation of Urban Wind Flows. Fluids, 7(6), 181. https://doi.org/10.3390/fluids7060181
Journal Article Type | Article |
---|---|
Acceptance Date | May 13, 2022 |
Online Publication Date | May 24, 2022 |
Publication Date | May 24, 2022 |
Deposit Date | Mar 21, 2024 |
Publicly Available Date | Mar 22, 2024 |
Journal | Fluids |
Print ISSN | 2311-5521 |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 7 |
Issue | 6 |
Pages | 181 |
DOI | https://doi.org/10.3390/fluids7060181 |
Keywords | Hybrid RANS LES; Embedded LES; turbulence; urban wind flow; industrial CFD; lattice Boltzmann Method; GPU |
Published Version
(25.7 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Baler -- Machine Learning Based Compression of Scientific Data
(2024)
Preprint / Working Paper
About USIR
Administrator e-mail: library-research@salford.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search