As fuel cells continue to improve in performance and power densities levels rise, potential applications ensue. System-level performance modeling tools are needed to further the investigation of future applications. One such application is small-scale aircraft propulsion. Both piloted and unmanned fuel cell aircrafts have been successfully demonstrated suggesting the near-term viability of revolutionizing small-scale aviation. Nearly all of the flight demonstrations and modeling efforts are conducted with low temperature fuel cells; however, the solid oxide fuel cell (SOFC) should not be overlooked. Attributing to their durability and popularity in stationary applications, which require continuous operation, SOFCs are attractive options for long endurance flights. This study presents the optimization of an integrated solid oxide fuel cell-fuel processing system model for performance evaluation in aircraft propulsion. System parameters corresponding to maximum steady state thermal efficiencies for various flight phase power levels were obtained through implementation of the PSO algorithm (Particle Swarm Optimization). Optimal values for fuel utilization, air stoichiometric ratio, air bypass ratio, and burner ratio, a 4-dimensional optimization problem, were obtained while constraining the SOFC operating temperature to 650–1000 °C. The PSO swarm size was set to 35 particles and the number of iterations performed for each case flight power level was set at 40. Results indicate the maximum thermal efficiency of the integrated fuel cell-fuel processing system remains in the range of 44–46% throughout descend, loitering, and cruise conditions. This paper discusses a system-level model of an integrated fuel cell - fuel processing system, and presents a methodology for system optimization through the particle swarm algorithm.

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