Generative prediction of laser-induced rocket ignition with dynamic latent space representations

TL;DR

This paper introduces a machine learning surrogate model combining convolutional autoencoders and neural ODEs to rapidly predict laser-induced rocket ignition dynamics, enabling efficient exploration of complex turbulent combustion systems.

Contribution

The study develops a novel ML-based surrogate that captures high-dimensional flow dynamics in a low-dimensional latent space for fast, accurate spatiotemporal predictions of laser-ignited rocket combustion.

Findings

Reduces simulation time by several orders of magnitude.

Enables real-time exploration of input parameters.

Provides physically grounded spatiotemporal predictions.

Abstract

Accurate and predictive scale-resolving simulations of laser-ignited rocket engines are highly time-consuming because the problem includes turbulent fuel-oxidizer mixing dynamics, laser-induced energy deposition, and high-speed flame growth. This is conflated with the large design space primarily corresponding to the laser operating conditions and target location. To enable rapid exploration and uncertainty quantification, we propose a data-driven surrogate modeling approach that combines convolutional autoencoders (cAEs) with neural ordinary differential equations (neural ODEs). The present target application of an ML-based surrogate model to leading-edge multi-physics turbulence simulation is part of a paradigm shift in the deployment of surrogate models towards increasing real-world complexity. Sequentially, the cAE spatially compresses high-dimensional flow fields into a low-dimensional latent space, wherein the system's temporal dynamics are learned via neural ODEs. Once trained, the model generates fast spatiotemporal predictions from initial conditions and specified operating inputs. By learning a surrogate to replace the entirety of the time-evolving simulation, the cost of predicting an ignition trial is reduced by several orders of magnitude, allowing efficient exploration of the input parameter space. Further, as the current framework yields a spatiotemporal field prediction, appraisal of the model output's physical grounding is more tractable. This approach marks a significant step toward real-time digital twins for laser-ignited rocket combustors and represents surrogate modeling in a complex system context.