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Detonation Cell Size Prediction based on Artificial Neural Networks with Chemical Kinetics and Thermodynamic Parameters

Title:

Detonation Cell Size Prediction based on Artificial Neural Networks with Chemical Kinetics and Thermodynamic Parameters

Bakalis, Georgios, Valipour, Maryam, Bentahar, Jamal, Kadem, Lyes, Teng, Honghui and Ng, Hoi Dick ORCID: https://orcid.org/0000-0002-8459-5990 (2023) Detonation Cell Size Prediction based on Artificial Neural Networks with Chemical Kinetics and Thermodynamic Parameters. Fuel Communications, 14 (100084).

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Official URL: https://doi.org/10.1016/j.jfueco.2022.100084

Abstract

In this paper, we develop a series of Artificial Neural Networks (ANN) using different chemical kinetic and thermodynamic input parameters to predict detonation cell sizes. The feedforward neural networks are trained and validated using available experimental data from the Caltech detonation database covering a wide variety of gaseous combustible mixtures at different initial conditions. For each combination of input parameters, a multiple-stage process is followed, which is described in detail, to first determine the best hyperparameters of the ANN (hidden layers, nodes per layer, etc.) and secondly to establish through a fitting process the optimal parameters for each specific network. The performance of the artificial neural networks with different input features is assessed using data from the same source, but that is kept independent and separate from the training and validation process of the ANN. It is found that ANN with three features can provide an accurate estimation of detonation cell size, while increasing the number of features does not improve the accuracy of the ANN. It is also found that the input parameters with the best performance relate indirectly to the stability parameter χ.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Article
Refereed:Yes
Authors:Bakalis, Georgios and Valipour, Maryam and Bentahar, Jamal and Kadem, Lyes and Teng, Honghui and Ng, Hoi Dick
Journal or Publication:Fuel Communications
Date:March 2023
Funders:
  • Natural Sciences and Engineering Research Council of Canada (NSERC)
Digital Object Identifier (DOI):10.1016/j.jfueco.2022.100084
ID Code:991774
Deposited By: Hoi Dick Ng
Deposited On:13 Feb 2023 15:33
Last Modified:13 Feb 2023 15:33
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