Cathode side transport phenomena investigation and Multi-Objective optimization of a tapered parallel flow field PEMFC. (1st August 2022)

• Record Type: Journal Article
• Authors: Ghasabehi, Mehrdad; Jabbary, Ali; Shams, Mehrzad
• Is Part Of: Energy conversion and management. Volume 265(2022)
• Publication Date: 2022-08-01
• Subjects: PEMFC -- Transport Phenomena -- Water Management -- Gas diffusion layer -- Operating Parameters -- Multi-objective Optimization; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2022.115761
• Languages: English
• ISSNs: 0196-8904

Abstract

Graphical abstract: Highlights: An Enhanced parallel flow field is represented using a 3D Multiphase simulation. The effects of operating conditions and GDL are investigated. Multi-layer multi-objective optimization was carried out to introduce an optimum point. NSGA-II and MOPSO distinguished the same fronts and the runtime of MOPSO is lower. The pressure and porosity played the most significant roles in determining objectives. Abstract: A Proton Exchange Membrane Fuel Cell (PEMFC) provides stable, emission-free, high-efficiency power. Water management and durability of PEMFCs are directly affected by transport phenomena at the cathode side. In the present study, transport phenomena are investigated and optimized in a tapered parallel flow field. Main channels in the flow field are tapered, which increases limiting current density by 41%. Two objectives, i.e. water saturation and transport resistance, are considered metrics for transport phenomena in a tapered parallel flow field PEMFC. Operating pressure, temperature, stoichiometries at both sides, and the porosity of gas diffusion layers are selected as parameters to be optimized. Two functions are generated for objectives by integrating 3D multiphase-flow computational fluid dynamics and Response Surface Methodology. Multi-Objective Optimization (MOO) is carried out with two different methods. Multi-Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are employed to produce two challenging Pareto fronts. The results demonstrate that MOPSO performs better than NSGA-II. MOPSO recognized quite the same Pareto front with lower runtime. In the last step, the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) is used to select an optimum point from the Pareto front. The results are compared against experimental data, and good correspondence is observed. The optimum features are temperature 323, pressure 1 atm, anode stoichiometry 3, cathode stoichiometry 2.62, and porosity 0.68. The porosity and pressure played the most significant roles in determining water saturation and resistance.