Reduced-Order Modeling of Parameterized Visco-Plastic Shallow Flows

TL;DR

This paper introduces a tensor-based, non-intrusive reduced-order modeling framework for complex visco-plastic shallow flows, enabling fast and accurate simulations of nonlinear free-surface flows with yield surfaces.

Contribution

The work develops a novel tensorial reduced-order model that efficiently captures nonlinear visco-plastic flow features without time integration, improving computational speed and accuracy.

Findings

Accurately captures flow features like front propagation and yield surfaces.

Achieves substantial speedups over full-order simulations.

Demonstrates effectiveness on parametrized visco-plastic flow problems.

Abstract

We propose a non-intrusive reduced-order modeling framework for parametrized visco-plastic free-surface flows governed by a shallow-water formulation of Herschel--Bulkley fluids. These flows exhibit strong nonlinearities, non-smooth rheology, moving fronts, and yield surfaces, making efficient surrogate modeling particularly challenging. To address this challenge, we employ a tensor-based approach in which the solution manifold is approximated using a low-rank representation obtained via higher-order singular value decomposition of snapshot data over a structured parameter space. The resulting tensorial reduced-order model (TROM) enables rapid online evaluation by directly reconstructing solution trajectories from the compressed representation, thereby avoiding the need to perform time integration of a reduced dynamical system. The proposed non-intrusive framework can be interpreted as an encoder--decoder architecture with a compressed latent representation and efficient multilinear decoding. Numerical experiments demonstrate that the proposed approach accurately captures key flow features, including front propagation, plug and shear regions, and near-stopping dynamics, while achieving substantial computational speedups relative to full-order simulations.