Machine learning moment closures for accurate and efficient simulation of polydisperse evaporating sprays

James B. Scoggins, Jiequn Han, Marc Massot

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a novel machine learning moment method for the closure of the moment transport equations associated with the solution of the Williams-Boltzmann equation for polydisperse, evaporating sprays. The method utilizes neural networks to learn optimal closures approximating the dynamics of the kinetic equation using a supervised learning approach. The neural network closure is compared to reference solutions obtained using a Lagrangian random particle method as well as two other state-of-the-art closure models, based on the maximum entropy assumption. Results on 0D and 1D test cases demonstrate that the closures obtained using the machine learning approach is significantly more accurate than the maximum entropy closures with comparable CPU performance. This suggests that such models can be used to replace expensive Lagrangian techniques with similar accuracy at far less cost.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2021 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Pages1-19
Number of pages19
ISBN (Print)9781624106095
StatePublished - 2021
EventAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021 - Virtual, Online
Duration: Jan 11 2021Jan 15 2021

Publication series

NameAIAA Scitech 2021 Forum

Conference

ConferenceAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021
CityVirtual, Online
Period1/11/211/15/21

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

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