Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion - DAta science, TrAnsition, Fluid instabiLity, contrOl, Turbulence Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2022

Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion

Résumé

Mapping near-field pollutant concentration is essential to track accidental toxic plume dispersion in urban areas. By solving a large part of the turbulence spectrum, large-eddy simulations (LES) have the potential to accurately represent pollutant concentration spatial variability. Finding a way to synthesize this large amount of information to improve the accuracy of lower-fidelity operational models (e.g. providing better turbulence closure terms) is particularly appealing. This is a challenge in multi-query contexts, where LES become prohibitively costly to deploy to understand how plume flow and tracer dispersion change with various atmospheric and source parameters. To overcome this issue, we propose a non-intrusive reduced-order model combining proper orthogonal decomposition (POD) and Gaussian process regression (GPR) to predict LES field statistics of interest associated with tracer concentrations. GPR hyperpararameters are optimized component-by-component through a maximum a posteriori (MAP) procedure informed by POD. We provide a detailed analysis of the reducedorder model performance on a two-dimensional case study corresponding to a turbulent atmospheric boundary-layer flow over a surface-mounted obstacle. We show that near-source concentration heterogeneities upstream of the obstacle require a large number of POD modes to be well captured. We also show that the component-by-component optimization allows to capture the range of spatial scales in the POD modes, especially the shorter concentration patterns in the high-order modes. The reduced-order model predictions remain acceptable if the learning database is made of at least fifty to hundred LES snapshot providing a first estimation of the required budget to move towards more realistic atmospheric dispersion applications.
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Dates et versions

hal-03736838 , version 1 (26-07-2022)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

Citer

Bastien X Nony, Mélanie Rochoux, Thomas Jaravel, Didier Lucor. Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion. 2022. ⟨hal-03736838⟩
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