Uncertain data assimilation for urban wind flow simulations with OpenLB-UQ

TL;DR

This paper introduces OpenLB-UQ, a modular framework combining lattice Boltzmann methods and stochastic collocation for efficient, uncertainty-aware urban wind flow simulations, enabling real-time analysis despite uncertain boundary conditions.

Contribution

It presents a novel integration of LBM with gPC-based stochastic collocation within OpenLB-UQ for urban wind flow, demonstrating efficiency and accuracy in real urban scenarios.

Findings

Uncertainty localizes in wakes and shear layers.

SC LBM provides accurate, uncertainty-aware predictions.

Framework enables real-time urban wind analysis.

Abstract

Accurate prediction of urban wind flow is essential for urban planning, pedestrian safety, and environmental management. Yet, it remains challenging due to uncertain boundary conditions and the high cost of conventional CFD simulations. This paper presents the use of the modular and efficient uncertainty quantification (UQ) framework OpenLB-UQ for urban wind flow simulations. We specifically use the lattice Boltzmann method (LBM) coupled with a stochastic collocation (SC) approach based on generalized polynomial chaos (gPC). The framework introduces a relative-error noise model for inflow wind speeds based on real measurements. The model is propagated through a non-intrusive SC LBM pipeline using sparse-grid quadrature. Key quantities of interest, including mean flow fields, standard deviations, and vertical profiles with confidence intervals, are efficiently computed without altering the underlying deterministic solver. We demonstrate this on a real urban scenario, highlighting how uncertainty localizes in complex flow regions such as wakes and shear layers. The results show that the SC LBM approach provides accurate, uncertainty-aware predictions with significant computational efficiency, making OpenLB-UQ a practical tool for real-time urban wind analysis.