首页> 外文会议>9th International conference on fuel cell science, engineering, and technology 2011 >OPTIMIZATION OF AN INTEGRATED SOFC-FUEL PROCESSING SYSTEM FOR AIRCRAFT PROPULSION
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OPTIMIZATION OF AN INTEGRATED SOFC-FUEL PROCESSING SYSTEM FOR AIRCRAFT PROPULSION

机译:飞机推进系统集成式燃料处理系统的优化

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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 ℃. 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.
机译:随着燃料电池性能的不断提高和功率密度水平的提高,随之而来的是潜在的应用。需要系统级性能建模工具来进一步研究未来的应用程序。一种这样的应用是小型飞机推进器。无人驾驶燃料电池飞机和无人驾驶燃料电池飞机均已得到成功展示,表明了使小型航空业发生革命性变化的近期可行性。几乎所有的飞行演示和建模工作都是使用低温燃料电池进行的。但是,固体氧化物燃料电池(SOFC)不应被忽视。由于SOFC的耐用性和在需要连续运行的固定应用中的普及性,SOFC是长寿命飞行的有吸引力的选择。这项研究提出了用于飞机推进性能评估的集成固体氧化物燃料电池-燃料处理系统模型的优化。通过实施PSO算法(粒子群优化),获得了与各个飞行阶段功率水平的最大稳态热效率相对应的系统参数。在将SOFC的工作温度限制在650-1000℃的同时,获得了燃料利用率,空气化学计量比,空气旁通比和燃烧器比(4维优化问题)的最佳值。 PSO群大小设置为35个粒子,每种情况下执行的迭代次数设置为40。结果表明,集成燃料电池-燃料处理系统的最大热效率保持在44-46%的范围内整个下降,游荡和巡航条件。本文讨论了集成燃料电池-燃料处理系统的系统级模型,并提出了一种通过粒子群算法进行系统优化的方法。

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