A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
An enhanced k- SST model to predict airflows around isolated and urban buildings
https://orcid.org/0000-0002-8989-7357
https://orcid.org/0000-0003-4560-8100
Abstract The goal of this research is to improve and validate a Reynolds Averaged Navier–Stokes (RANS) turbulence model to perform accurate Computational Fluid Dynamics (CFD) simulations of the urban wind flow. The k- SST model is selected for calibration since its blended formulation holds remarkable optimization potential and has increased relevancy in recent studies in the field. A simulation-based optimization approach recalibrates the model closure constants by minimizing the prediction error of wind pressure coefficients on an isolated cubical building because this scenario contains many salient features observed in the flow in actual urban areas. The optimization procedure ensures both the coherence of calibrated model constants involved in the wall function formulations and the relationship between them to satisfy the flow horizontal homogeneity of the atmospheric boundary layer. The tuned closure coefficients increase momentum diffusion in the wake, resulting in shorter and more accurate predictions of the reattachment lengths. Validation case studies with wind tunnel measurement data from various urban scenarios were addressed to comprehensively assess the adaptability of the optimal set of coefficients reached. The results confirm that CFD predictions with the optimized model are consistently in closer agreement with experimental data than the standard version of k- SST. The root mean square errors are reduced by about 75% in pressure, 40% in velocity, and 20% in turbulent kinetic energy.
Graphical abstract Display Omitted
Highlights An enhanced k- SST model to predict urban airflow is developed. The RANS closure coefficients are optimized to fit wind tunnel-measured data. The flow in the wake is better predicted due to an increased momentum diffusion. Validation case studies use wind tunnel data from various urban configurations. The new model reduces the prediction error up to 75% (pressures) & 40% (velocities).
An enhanced k- SST model to predict airflows around isolated and urban buildings
https://orcid.org/0000-0002-8989-7357
https://orcid.org/0000-0003-4560-8100
Abstract The goal of this research is to improve and validate a Reynolds Averaged Navier–Stokes (RANS) turbulence model to perform accurate Computational Fluid Dynamics (CFD) simulations of the urban wind flow. The k- SST model is selected for calibration since its blended formulation holds remarkable optimization potential and has increased relevancy in recent studies in the field. A simulation-based optimization approach recalibrates the model closure constants by minimizing the prediction error of wind pressure coefficients on an isolated cubical building because this scenario contains many salient features observed in the flow in actual urban areas. The optimization procedure ensures both the coherence of calibrated model constants involved in the wall function formulations and the relationship between them to satisfy the flow horizontal homogeneity of the atmospheric boundary layer. The tuned closure coefficients increase momentum diffusion in the wake, resulting in shorter and more accurate predictions of the reattachment lengths. Validation case studies with wind tunnel measurement data from various urban scenarios were addressed to comprehensively assess the adaptability of the optimal set of coefficients reached. The results confirm that CFD predictions with the optimized model are consistently in closer agreement with experimental data than the standard version of k- SST. The root mean square errors are reduced by about 75% in pressure, 40% in velocity, and 20% in turbulent kinetic energy.
Graphical abstract Display Omitted
Highlights An enhanced k- SST model to predict urban airflow is developed. The RANS closure coefficients are optimized to fit wind tunnel-measured data. The flow in the wake is better predicted due to an increased momentum diffusion. Validation case studies use wind tunnel data from various urban configurations. The new model reduces the prediction error up to 75% (pressures) & 40% (velocities).
An enhanced k- SST model to predict airflows around isolated and urban buildings
https://orcid.org/0000-0002-8989-7357
https://orcid.org/0000-0003-4560-8100
Abstract The goal of this research is to improve and validate a Reynolds Averaged Navier–Stokes (RANS) turbulence model to perform accurate Computational Fluid Dynamics (CFD) simulations of the urban wind flow. The k- SST model is selected for calibration since its blended formulation holds remarkable optimization potential and has increased relevancy in recent studies in the field. A simulation-based optimization approach recalibrates the model closure constants by minimizing the prediction error of wind pressure coefficients on an isolated cubical building because this scenario contains many salient features observed in the flow in actual urban areas. The optimization procedure ensures both the coherence of calibrated model constants involved in the wall function formulations and the relationship between them to satisfy the flow horizontal homogeneity of the atmospheric boundary layer. The tuned closure coefficients increase momentum diffusion in the wake, resulting in shorter and more accurate predictions of the reattachment lengths. Validation case studies with wind tunnel measurement data from various urban scenarios were addressed to comprehensively assess the adaptability of the optimal set of coefficients reached. The results confirm that CFD predictions with the optimized model are consistently in closer agreement with experimental data than the standard version of k- SST. The root mean square errors are reduced by about 75% in pressure, 40% in velocity, and 20% in turbulent kinetic energy.
Graphical abstract Display Omitted
Highlights An enhanced k- SST model to predict urban airflow is developed. The RANS closure coefficients are optimized to fit wind tunnel-measured data. The flow in the wake is better predicted due to an increased momentum diffusion. Validation case studies use wind tunnel data from various urban configurations. The new model reduces the prediction error up to 75% (pressures) & 40% (velocities).
An enhanced k- SST model to predict airflows around isolated and urban buildings
Gimenez, Juan M. (author) / Bre, Facundo (author)
Building and Environment ; 237
2023-04-15
Article (Journal)
Electronic Resource
English
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