Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Source term estimation in complex urban environments based on Bayesian inference and unsteady adjoint equations simulated via large eddy simulation
https://orcid.org/0000-0002-1025-9839
Abstract Establishing an accurate source–receptor relationship is essential for identifying unknown sources of air pollution in complex urban environments. Existing source term estimation (STE) methods determine this relationship via steady simulations of adjoint equations with ensembled-averaged flow fields. However, the effect of turbulent diffusion on the dispersion of pollutants is simplified according to the gradient dispersion hypothesis, which may result in considerable STE errors, especially in the complex urban areas. Therefore, we developed a new STE method by embedding unsteady adjoint equations modeled via large eddy simulation (LES) into a Bayesian inference framework. The tremendous storage requirement of the unsteady simulation was mitigated using a wavelet-based compression method. The performance of the proposed method was then evaluated based on dispersion measurements collected in a wind tunnel experiment for a regular, block-arrayed building group model. The estimation results were compared with those derived from an existing method, in which the steady simulation of adjoint equations was employed based on the mean LES flow field. Our results suggest that the LES-based approach significantly improved the modeling accuracy of the adjoint equations and the resulting STEs. If the 50th percentile of the posterior probability was regarded as the point estimate, the absolute errors in source location and strength were reduced by 89% and 99%, respectively, when compared to the existing method.
Highlights An LES-based unsteady adjoint equation was coupled with Bayesian inference. Source term estimation was compared between LES and steady RANS model. Data storage for backward LES was realized by wavelet-based compression. The model was validated via continuous point release in a block-arrayed group. More accurate estimates were achieved using LES than with RANS.
Source term estimation in complex urban environments based on Bayesian inference and unsteady adjoint equations simulated via large eddy simulation
https://orcid.org/0000-0002-1025-9839
Abstract Establishing an accurate source–receptor relationship is essential for identifying unknown sources of air pollution in complex urban environments. Existing source term estimation (STE) methods determine this relationship via steady simulations of adjoint equations with ensembled-averaged flow fields. However, the effect of turbulent diffusion on the dispersion of pollutants is simplified according to the gradient dispersion hypothesis, which may result in considerable STE errors, especially in the complex urban areas. Therefore, we developed a new STE method by embedding unsteady adjoint equations modeled via large eddy simulation (LES) into a Bayesian inference framework. The tremendous storage requirement of the unsteady simulation was mitigated using a wavelet-based compression method. The performance of the proposed method was then evaluated based on dispersion measurements collected in a wind tunnel experiment for a regular, block-arrayed building group model. The estimation results were compared with those derived from an existing method, in which the steady simulation of adjoint equations was employed based on the mean LES flow field. Our results suggest that the LES-based approach significantly improved the modeling accuracy of the adjoint equations and the resulting STEs. If the 50th percentile of the posterior probability was regarded as the point estimate, the absolute errors in source location and strength were reduced by 89% and 99%, respectively, when compared to the existing method.
Highlights An LES-based unsteady adjoint equation was coupled with Bayesian inference. Source term estimation was compared between LES and steady RANS model. Data storage for backward LES was realized by wavelet-based compression. The model was validated via continuous point release in a block-arrayed group. More accurate estimates were achieved using LES than with RANS.
Source term estimation in complex urban environments based on Bayesian inference and unsteady adjoint equations simulated via large eddy simulation
https://orcid.org/0000-0002-1025-9839
Abstract Establishing an accurate source–receptor relationship is essential for identifying unknown sources of air pollution in complex urban environments. Existing source term estimation (STE) methods determine this relationship via steady simulations of adjoint equations with ensembled-averaged flow fields. However, the effect of turbulent diffusion on the dispersion of pollutants is simplified according to the gradient dispersion hypothesis, which may result in considerable STE errors, especially in the complex urban areas. Therefore, we developed a new STE method by embedding unsteady adjoint equations modeled via large eddy simulation (LES) into a Bayesian inference framework. The tremendous storage requirement of the unsteady simulation was mitigated using a wavelet-based compression method. The performance of the proposed method was then evaluated based on dispersion measurements collected in a wind tunnel experiment for a regular, block-arrayed building group model. The estimation results were compared with those derived from an existing method, in which the steady simulation of adjoint equations was employed based on the mean LES flow field. Our results suggest that the LES-based approach significantly improved the modeling accuracy of the adjoint equations and the resulting STEs. If the 50th percentile of the posterior probability was regarded as the point estimate, the absolute errors in source location and strength were reduced by 89% and 99%, respectively, when compared to the existing method.
Highlights An LES-based unsteady adjoint equation was coupled with Bayesian inference. Source term estimation was compared between LES and steady RANS model. Data storage for backward LES was realized by wavelet-based compression. The model was validated via continuous point release in a block-arrayed group. More accurate estimates were achieved using LES than with RANS.
Source term estimation in complex urban environments based on Bayesian inference and unsteady adjoint equations simulated via large eddy simulation
Jia, Hongyuan (Autor:in) / Kikumoto, Hideki (Autor:in)
Building and Environment ; 193
01.02.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
A lightweight adjoint method for gaseous pollution source term estimation in urban environments
| Springer Verlag | 2025