Proper Orthogonal Decomposition-based Model-Order Reduction for Smoothed Particle Hydrodynamics Simulation -- Mass-Spring-Damper System

TL;DR

This paper explores the application of Proper Orthogonal Decomposition-based Model-Order Reduction to Lagrangian SPH simulations of a mass-spring-damper system, demonstrating effective reduction and potential computational acceleration.

Contribution

It introduces a POD-MOR approach for Lagrangian SPH simulations and evaluates its performance and acceleration potential in a mass-spring-damper system.

Findings

POD-MOR effectively reduces degrees of freedom in SPH simulations.

Acceleration of POD-MOR is possible without losing accuracy.

POD-MOR captures essential modes in nonlinear Lagrangian systems.

Abstract

Model Order Reduction (MOR) based on Proper Orthogonal Decomposition (POD) and Smooth Particle Hydrodynamics (SPH) has proven effective in various applications. Most MOR methods utilizing POD are implemented within a pure Eulerian framework, while significantly less attention has been given to POD in a Lagrangian context. In this paper, we present the POD-MOR of SPH simulations applied to a mass-spring-damper system with two primary objectives: 1. To evaluate the performance of the data-driven POD-MOR approach. 2. To investigate potential methods for accelerating POD-MOR computations. Although the mass-spring-damper system is linear, its SPH implementations are nonlinear, and POD-MOR does not automatically lead to faster computations. Our findings indicate that (1) the POD-MOR effectively reduces the degrees of freedom in the SPH simulations by capturing the essential modes, and (2) in various cases, the acceleration of POD-MOR can be achieved without compromising accuracy. We hope that our results will motivate further investigations into the design of POD-MOR algorithms for nonlinear Lagrangian systems.