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A Reconstruction Method of Detonation Wave Surface Based on Convolutional Neural Network

Title:

A Reconstruction Method of Detonation Wave Surface Based on Convolutional Neural Network

Bian, Jing, Zhou, Lin, Yang, Pengfei, Teng, Honghui and Ng, Hoi Dick ORCID: https://orcid.org/0000-0002-8459-5990 (2022) A Reconstruction Method of Detonation Wave Surface Based on Convolutional Neural Network. Fuel, 315 . p. 123068. ISSN 0016-2361

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Official URL: https://doi.org/10.1016/j.fuel.2021.123068

Abstract

Detonation wave surface is composed of lead shock and reactive front, which are difficult to be measured simultaneously, so it is necessary to reconstruct the detonation surface. In this study, a reconstruction method is proposed for predicting lead shock from reactive front to obtain a full cellular detonation surface. The reconstruction uses a convolutional neural network (CNN) with the advantages of feature extraction and data dimensionality reduction, and the proposed method has been verified by data from numerical simulations in this work. The results indicate that this method performs much better than the traditional multi-layer perceptron (MLP), benefiting from the advanced architecture of CNN. Furthermore, effects of hyper-parameter choice have been tested, and the generalization capability of trained CNN for different activation-energy cases are also discussed.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Article
Refereed:Yes
Authors:Bian, Jing and Zhou, Lin and Yang, Pengfei and Teng, Honghui and Ng, Hoi Dick
Journal or Publication:Fuel
Date:1 May 2022
Digital Object Identifier (DOI):10.1016/j.fuel.2021.123068
Keywords:Detonation waves, Wave surface reconstruction, Convolutional neural network, Machine learning
ID Code:990771
Deposited By: Hoi Dick Ng
Deposited On:17 Aug 2022 21:13
Last Modified:17 Aug 2022 21:13
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