Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine

TL;DR

This paper presents a distributed memory algorithm that enables fast, scalable construction of physics-based reduced-order models for complex aerospace simulations, significantly reducing computational time from millions of core hours to seconds.

Contribution

The authors develop a distributed memory algorithm for scalable, physics-based reduced-order modeling of large-scale dynamical systems, surpassing the limitations of serial approaches.

Findings

Achieved model construction in 13 seconds on 2,048 cores.

Successfully scaled to large datasets with 76 million state dimensions.

Enabled real-time surrogate modeling for complex rocket engine simulations.

Abstract

High-performance computing (HPC) has revolutionized our ability to perform detailed simulations of complex real-world processes. A prominent contemporary example is from aerospace propulsion, where HPC is used for rotating detonation rocket engine (RDRE) simulations in support of the design of next-generation rocket engines; however, these simulations take millions of core hours even on powerful supercomputers, which makes them impractical for engineering tasks like design exploration and risk assessment. Data-driven reduced-order models (ROMs) aim to address this limitation by constructing computationally cheap yet sufficiently accurate approximations that serve as surrogates for the high-fidelity model. This paper contributes a distributed memory algorithm that achieves fast and scalable construction of predictive physics-based ROMs trained from sparse datasets of extremely large state dimension. The algorithm learns structured physics-based ROMs that approximate the dynamical systems underlying those datasets.This enables model reduction for problems at a scale and complexity that exceeds the capabilities of standard, serial approaches. We demonstrate our algorithm's scalability using up to $2,048$ cores on the Frontera supercomputer at the Texas Advanced Computing Center. We focus on a real-world three-dimensional RDRE for which one millisecond of simulated physical time requires one million core hours on a supercomputer. Using a training dataset of $2,536$ snapshots each of state dimension $76$ million, our distributed algorithm enables the construction of a predictive data-driven reduced model in just $13$ seconds on $2,048$ cores on Frontera.