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

Message:
Article Type:
Research/Original Article (دارای رتبه معتبر)
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.

Language:
Persian
Published:
Pages:
81 to 95
https://www.magiran.com/p2869585  
سامانه نویسندگان
  • Corresponding Author (1)
    Mahdi Keyhanpour
    Phd Student Mechanical Engineering, Khaje Nasir Toosi University of Technology, Tehran, Iran
    Keyhanpour، Mahdi
اطلاعات نویسنده(گان) توسط ایشان ثبت و تکمیل شده‌است. برای مشاهده مشخصات و فهرست همه مطالب، صفحه رزومه را ببینید.
مقالات دیگری از این نویسنده (گان)