سال انتشار: ۱۳۹۰

محل انتشار: اولین کنفرانس بین المللی رویکردهای نوین در نگهداشت انرژی

تعداد صفحات: ۶

نویسنده(ها):

shahriar bozorgmehri – University of Tehran, School of Mechanical Engineering,
mohsen hamedi – Niroo Research Institute, Renewable Energy Department,

چکیده:

Parametric study is performed by sensitivity analysis (SA) for solid oxide fuel cells (SOFCs) on an artificial neural network (ANN) model of the SOFC performance. The ANN model have been used to predict the SOFC performance exactly and then the effects of cell parameters, i.e. anode supported layer thickness, porosity, electrolyte thickness, and cathode functional layer thickness, are calculated to recognize the significant factors on the power density of SOFC by using the ANN model. Therefore, this approach can be used to recognize the effects of the cell parameters of the SOFCs and increase the performance in the optimal design of SOFC.