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

TL;DR

This paper introduces a non-intrusive reduced-order modeling approach combining POD and GPR to efficiently predict LES-based pollutant dispersion statistics in atmospheric boundary-layer flows, reducing computational costs for multi-query scenarios.

Contribution

The paper presents a novel reduced-order model that integrates POD and GPR with hyperparameter optimization for efficient LES prediction in complex atmospheric flows.

Findings

The model captures key spatial scales with sufficient POD modes.

Component-wise GPR hyperparameter tuning improves accuracy.

At least 50-100 LES snapshots are needed for reliable predictions.

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.