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

2025-08-25

AI Summary

Accurate urban wind field prediction is critical for urban planning, pedestrian safety, and environmental management, yet hindered by boundary condition uncertainties and the high computational cost of conventional CFD simulations. To address this, we propose an efficient uncertainty quantification (UQ) framework that integrates the lattice Boltzmann method (LBM) with a non-intrusive sparse grid stochastic collocation method, implemented in the OpenLB-UQ platform. Our approach introduces a novel relative-error-based noise model calibrated against field measurements and couples generalized polynomial chaos expansion with sparse grid Gaussian quadrature—enabling uncertainty propagation without modifying the underlying LBM solver. Applied to realistic urban geometries, the framework efficiently generates mean flow fields, standard deviation maps, and confidence-interval vertical cross-sections. It achieves substantial computational speedup while maintaining high-fidelity uncertainty-aware predictions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Addresses uncertain boundary conditions in urban wind flow simulations
• Develops efficient uncertainty quantification for real-time urban wind analysis
• Propagates measurement-based noise model through non-intrusive computational framework

Innovation

• Lattice Boltzmann method with stochastic collocation
• Non-intrusive sparse-grid quadrature propagation
• Relative-error noise model from measurements