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

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...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Qian, Yizhou, Wang, Jonathan, Douasbin, Quentin, Darve, Eric
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.05.2023
Subjects
Artificial neural networks
Combustion chambers
Computational fluid dynamics
Differential equations
Ignition
Lasers
Mathematical models
Neural networks
Ordinary differential equations
Parameterization
Parameters
Physics
Reduced order models
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2331-8422