A fuel cell based system performance is basically determined by the amount of current density the stack is able to produce, given a well-defined quantity of reactants flowing through it. Starting from a Proton Exchange Membrane fuel cell (PEMFC) distributed parameters model, considering all the aspects influencing the cell behavior, a Multidisciplinary Design Optimization (MDO) process based on a surrogate model is presented. A Monte Carlo Simulation approach is chosen to perform a sensitivity analysis to estimate the effects of key parameters on performances. This analysis allows the definition of a ranking Pareto plot to operate design variables reduction, decreasing the problem complexity and increasing the orthogonality of the input design matrix. The main purpose is to find a suitable and validated method able to reduce the time expense for a complete simulation, so to originate useful input to a multi-disciplinary design optimization.

Performance estimate for a Proton Exchange Membrane Fuel Cell: Sensitivity Analysis aimed to Optimization / Testa, Enrico; Maggiore, Paolo; Pace, Lorenzo; DALLA VEDOVA, MATTEO DAVIDE LORENZO. - STAMPA. - 42:(2015), pp. 276-281. ((Intervento presentato al convegno 2015 International Conference on Pure Mathematics - Applied Mathematics (PM-AM 2015) tenutosi a Vienna nel 15-17 March 2015.

Performance estimate for a Proton Exchange Membrane Fuel Cell: Sensitivity Analysis aimed to Optimization

TESTA, ENRICO;MAGGIORE, Paolo;PACE, LORENZO;DALLA VEDOVA, MATTEO DAVIDE LORENZO
2015

Abstract

A fuel cell based system performance is basically determined by the amount of current density the stack is able to produce, given a well-defined quantity of reactants flowing through it. Starting from a Proton Exchange Membrane fuel cell (PEMFC) distributed parameters model, considering all the aspects influencing the cell behavior, a Multidisciplinary Design Optimization (MDO) process based on a surrogate model is presented. A Monte Carlo Simulation approach is chosen to perform a sensitivity analysis to estimate the effects of key parameters on performances. This analysis allows the definition of a ranking Pareto plot to operate design variables reduction, decreasing the problem complexity and increasing the orthogonality of the input design matrix. The main purpose is to find a suitable and validated method able to reduce the time expense for a complete simulation, so to originate useful input to a multi-disciplinary design optimization.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2604985
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