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http://dx.doi.org/10.6108/KSPE.2021.25.6.001

Application of Artificial Neural Network to Flamelet Library for Gaseous Hydrogen/Liquid Oxygen Combustion at Supercritical Pressure  

Jeon, Tae Jun (School of Mechanical Engineering, Kyungpook National University)
Park, Tae Seon (School of Mechanical Engineering, Kyungpook National University)
Publication Information
Journal of the Korean Society of Propulsion Engineers / v.25, no.6, 2021 , pp. 1-11 More about this Journal
Abstract
To develop an efficient procedure related to the flamelet library, the machine learning process based on artificial neural network(ANN) is applied for the gaseous hydrogen/liquid oxygen combustor under a supercritical pressure condition. For hidden layers, 25 combinations based on Rectified Linear Unit(ReLU) and hyperbolic tangent are adopted to find an optimum architecture in terms of the computational efficiency and the training performance. For activation functions, the hyperbolic tangent is proper to get the high learning performance for accurate properties. A transformation learning data is proposed to improve the training performance. When the optimal node is arranged for the 4 hidden layers, it is found to be the most efficient in terms of training performance and computational cost. Compared to the interpolation procedure, the ANN procedure reduces computational time and system memory by 37% and 99.98%, respectively.
Keywords
Artificial Neural Network; Supercritical Combustion; Activation Function; Computational Cost;
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