Physics-based parameterized neural ordinary differential equations: prediction of laser ignition in a rocket combustor
This work addresses the challenge of accurate and efficient modeling of laser ignition for rocket combustor design, representing an incremental improvement in domain-specific reduced-order modeling.
The authors tackled the problem of predicting laser ignition in a rocket combustor by developing a physics-based parameterized neural ODE framework, which achieved lower mean absolute errors in average temperature predictions compared to baseline methods like kernel ridge regression and fully connected neural networks.
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.