OpenLB-UQ: An Uncertainty Quantification Framework for Incompressible Fluid Flow Simulations

TL;DR

OpenLB-UQ is a scalable, integrated framework that enhances the OpenLB library with uncertainty quantification methods, enabling efficient and accurate large-scale fluid flow simulations under uncertain conditions.

Contribution

It introduces a novel UQ module for OpenLB, combining stochastic collocation and Monte Carlo methods for large-scale incompressible flow simulations.

Findings

Validated convergence rates with benchmark cases

Demonstrated promising scalability and efficiency

Confirmed robustness of statistical accuracy

Abstract

Uncertainty quantification (UQ) is crucial in computational fluid dynamics to assess the reliability and robustness of simulations, given the uncertainties in input parameters. OpenLB is an open-source lattice Boltzmann method library designed for efficient and extensible simulations of complex fluid dynamics on high-performance computers. In this work, we leverage the efficiency of OpenLB for large-scale flow sampling with a dedicated and integrated UQ module. To this end, we focus on non-intrusive stochastic collocation methods based on generalized polynomial chaos and Monte Carlo sampling. The OpenLB-UQ framework is extensively validated in convergence tests with respect to statistical metrics and sample efficiency using selected benchmark cases, including two-dimensional Taylor--Green vortex flows with up to four-dimensional uncertainty and a flow past a cylinder. Our results confirm the expected convergence rates and show promising scalability, demonstrating robust statistical accuracy as well as computational efficiency. OpenLB-UQ enhances the capability of the OpenLB library, offering researchers a scalable framework for UQ in incompressible fluid flow simulations and beyond.