Bayesian-Enhanced Galerkin-Based Reduced Order Modelling for Unsteady Compressible Flows

TL;DR

This paper introduces a Bayesian-enhanced Galerkin-POD reduced-order modeling framework that improves stability and predictive accuracy for unsteady compressible flows by systematically incorporating uncertainty and model correction.

Contribution

It reformulates Galerkin-POD model correction as a Bayesian inverse problem, enabling robust, uncertainty-aware reduced-order models for complex fluid flows.

Findings

Successfully validated on oscillating flow over a dimpled surface at Re=3000.

Accurately captured unsteady structures in a centrifugal compressor at Re=100000.

Enhanced model stability and predictive fidelity through Bayesian inference.

Abstract

This work proposes a statistically enhanced framework to address the instability and limited predictive capability of conventional Galerkin-Proper Orthogonal Decomposition (Galerkin-POD) models. The method reformulates the correction of the Galerkin-projected ODE system as a statistical inverse problem, in which the coefficients are inferred through Bayesian inference. By accounting for model uncertainty arising from POD mode truncation and data uncertainty introduced by data noise and numerical postprocessing, the framework systematically updates the ODE system coefficients using an analytical, sampling-free solution based on Gaussian likelihood and inverse-Gamma priors. The approach is first validated using a self-sustained oscillating flow over a dimpled surface at a moderate Reynolds number (Re=3000), demonstrating stable and accurate reproduction of the temporal dynamics and phase trajectories of coherent structures when compared with direct numerical simulation (DNS). It is then applied to a centrifugal compressor featuring strong tip-leakage vortex breakdown and impeller-diffuser interactions at Re=100000, where the model successfully captures dominant unsteady structures and frequency characteristics despite limited mode retention. Overall, the results show that Bayesian inference substantially enhances the robustness, stability, and predictive fidelity of Galerkin-POD models for compressible flow systems. The proposed methodology combines the physical interpretability of Galerkin projection with the statistical rigour of Bayesian inference, offering a general, computationally efficient, and uncertainty-aware reduced-order modelling framework for complex fluid dynamic applications.