Application of Computational Fluid Dynamics and Machine Learning in Predicting Performance of Tubular Solid Oxide Fuel Cell Ammonia Fuelled

Document Type : Research Paper

Authors

1 Mechanical Engineering Faculty, K. N. T. University of Technology, Tehran, Iran

2 Iran, Tehran, Tehran, K. N. Toosi University of technology, Mechanical Department

Abstract

In this research, an ammonia fuelled intermediate temperature solid oxide fuel cell (IT-SOFC) has been simulated and performance evaluated by computational fluid dynamics and machine learning. First, the geometry of the problem is modeled in an axisymmetric manner and the equations including conservation of mass, momentum, species, energy and electric charge are defined, coupled and solved using a finite element numerical code. Then, to check the machine learning algorithm, terms of power density and maximum temperature are selected as objective functions and terms of input temperature, porosity of electrodes and velocity of fuel and air flows are selected as influencing variables. After generating the adequate data by repeating the numerical solution six hundred and one times in different cases of the input parameters, the machine learning process begins by using eighty five percent of the data on the different structures of the deep neural network algorithm. The results show that in the optimal structure of the algorithm, the performance of the machine in predicting the objective functions is appropriate and acceptable. Therefore, R^2 of the machine in predicting the maximum temperature and power density functions are 0.99 and 0.98, respectively.

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Journal of Petroleum Research
Volume 35, 1404-1 - Serial Number 140
Special Issue on the Development of the Hydrogen TechnologyChain
May and June 2025

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