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Optimization of system parameters for gas-generator engines

机译:燃气发电机发动机系统参数的优化

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摘要

For this paper, software to optimize the system parameters for gas-generator cycle engines was developed. The gas-generator cycle engine was decomposed into several principal components, such as the thruster chamber, turbopumps and gas generators. Numerical simulation codes were developed for each component and the system. According to the different relationships within the components, these modularized codes could be integrated arbitrarily. A generic optimization procedure of system parameters for the gas-generator cycle engine was developed. According to the design mission, optimal engine system parameters could be decided for different components assembling forms by using this procedure. One hybrid algorithm, which combined the genetic algorithm with gradient-based algorithm, was utilized during optimization procedure to increase the globality of the optimization exploration. Several traditional multi-objective methods, such as changing weight method, e-constraint method, and neighborhood cultivation genetic algorithm were used to obtain the Pareto frontier solutions. Physical programming method and fuzzy related degree method were used to obtain optimal design based on preference of the designer. Comparative results presented show the impact of different methods on the design variables and objectives.
机译:在本文中,开发了用于优化燃气发电机循环发动机系统参数的软件。燃气发电机循环发动机被分解为几个主要部件,例如推进器室,涡轮泵和气体发生器。为每个组件和系统开发了数值模拟代码。根据组件内的不同关系,可以将这些模块化代码任意集成。开发了燃气发电机循环发动机系统参数的通用优化程序。根据设计任务,可以使用此过程为不同的组件组装形式确定最佳的发动机系统参数。在优化过程中,采用了一种将遗传算法与基于梯度的算法相结合的混合算法,以提高优化探索的全局性。几种传统的多目标方法,例如变权法,电子约束法和邻域耕种遗传算法,被用于获得帕累托边界解。根据设计者的偏爱,采用物理规划法和模糊关联度法获得最优设计。比较结果显示了不同方法对设计变量和目标的影响。

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