شماره ركورد كنفرانس :
4018
عنوان مقاله :
Investigation of Gas Channel shape effect on Proton Exchange Membrane Fuel Cell Performance
پديدآورندگان :
Rezazadeh Sajad Sor.mems@gmail.com Assistant professor, Department of Mechanical Engineering, Urmia university of technology, Urmia, Iran , Sadeghi Haleh h.sadeghi@msn.com PhD student, Department of Mechanical Engineering, Urmia university, Urmia, Iran , Ahmadi Nima Nima.ahmadi.eng@gmail.com Department of Mechanical Engineering, Urmia university of technology, Urmia, Iran.
كليدواژه :
Gas channels , Computational Fluid Dynamics , Fuel cell performance , Membrane , PEM fuel cells.
عنوان كنفرانس :
هفدهمين كنفرانس ملي ديناميك شاره ها
چكيده فارسي :
A three-dimensional, single phase model of a proton exchange membrane fuel cell
(PEMFC) with both the gas distribution flow channels and the Membrane Electrode Assembly
(MEA) has been developed. A single set of conservation equations which are valid for the
flow channels, gas-diffusion electrodes, catalyst layers, and the membrane region are
developed and numerically solved using a finite volume based Computational Fluid Dynamics
(CFD) technique. In this research, some parameters such as oxygen consumption, water
production, temperature distribution, ohmic losses, anode water activity, cathode over
potential and the fuel cell performance for straight single cell were investigated in more
details. The numerical simulations reveal that these important operating parameters are
highly dependent to each other and the fuel cell efficiency is affected by the kind of species
distribution. So for especial uses in desirable voltages, for preventing from the unwilling
losses, these numerical results can be useful. The important goal of this research is the
investigation of the step-liked gas channel shape effect on the fuel cell performance compared
with the conventional model, which is highlighted in the results section with more details.
Finally, the numerical results of proposed CFD model have been compared with the
published experimental data that represent good agreement.