Physics-based parameterized neural ordinary differential equations: prediction of laser ignition in a rocket combustor

• URL: http://arxiv.org/abs/2302.08629v2
• Date: Wed, 3 May 2023 22:48:06 GMT
• Title: Physics-based parameterized neural ordinary differential equations: prediction of laser ignition in a rocket combustor
• Authors: Yizhou Qian, Jonathan Wang, Quentin Douasbin, Eric Darve
• Abstract summary: We present a physics-based data-driven framework for reduced-order modeling of laser ignition in a model rocket combustor. Deep neural networks are embedded as functions of high-dimensional parameters of laser ignition to predict various terms in a 0D flow model.
• Score: 2.227105438439618
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we present a novel physics-based data-driven framework for reduced-order modeling of laser ignition in a model rocket combustor based on parameterized neural ordinary differential equations (PNODE). Deep neural networks are embedded as functions of high-dimensional parameters of laser ignition to predict various terms in a 0D flow model including the heat source function, pre-exponential factors, and activation energy. Using the governing equations of a 0D flow model, our PNODE needs only a limited number of training samples and predicts trajectories of various quantities such as temperature, pressure, and mass fractions of species while satisfying physical constraints. We validate our physics-based PNODE on solution snapshots of high-fidelity Computational Fluid Dynamics (CFD) simulations of laser-induced ignition in a prototype rocket combustor. We compare the performance of our physics-based PNODE with that of kernel ridge regression and fully connected neural networks. Our results show that our physics-based PNODE provides solutions with lower mean absolute errors of average temperature over time, thus improving the prediction of successful laser ignition with high-dimensional parameters.

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