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

• URL: http://arxiv.org/abs/2208.01518v1
• Date: Tue, 2 Aug 2022 15:06:22 GMT
• Title: Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion
• Authors: Bastien X Nony, M\'elanie Rochoux, Thomas Jaravel (CERFACS), Didier Lucor (LISN)
• Abstract summary: Large-eddy simulations (LES) have the potential to accurately represent pollutant concentration spatial variability. LES become prohibitively costly to deploy to understand how plume flow and tracer dispersion change with various atmospheric and source parameters. We propose a non-intrusive reduced-order model combining proper decomposition (POD) and Gaussian process regression (GPR) to predict LES field statistics of interest associated with tracer concentrations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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