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Multi-disciplinary and multi-objective optimization of an over-wing-nacelle aircraft concept

机译:机翼机舱飞机概念的多学科和多目标优化

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In this paper, a multi-disciplinary and multi-objective optimization (MDO-MOO) of a baseline over-wing-nacelle (OWN) concept design is presented. The present study extends the previous works, which considered only aerodynamic optimization, to include structural and mission design parameters. The competing objectives of minimum empty weight and minimum fuel weight for a design mission are considered in the multi-objective formulation as well as the single-objective problem of minimizing takeoff gross weight, one of many compromises possible for the multi-objective problem. An integrated computational environment has been implemented. High-fidelity analyses for the structural and aeroelastic assessment, together with middle-fidelity analyses for aerodynamic, mission, and performance analyses are performed. A complex multi-disciplinary analysis framework is proposed, to account for the interdisciplinary interaction and to provide a consistent computational framework. Optimization results with a Multi-objective Genetic Algorithm (MOGA) show Pareto frontiers accounting for structural, aeroelastic, and mission design constraints. The disciplines coupling is quantified, in terms of constraints, design variables influences, and possible trade-offs among the objectives.
机译:本文提出了一种基线跨机舱(OWN)概念设计的多学科多目标优化(MDO-MOO)。本研究将仅考虑空气动力学优化的先前工作扩展到包括结构和任务设计参数。多目标制定中考虑了设计任务的最小空载重量和最小燃料重量的竞争目标,以及最小化起飞总重量的单目标问题,这是多目标问题可能做出的许多折衷之一。已经实现了集成的计算环境。进行结构和气动弹性评估的高保真度分析,以及进行空气动力学,任务和性能分析的中保真度分析。提出了一个复杂的多学科分析框架,以解决跨学科的相互作用并提供一致的计算框架。使用多目标遗传算法(MOGA)进行的优化结果表明,帕累托边界解决了结构,气动弹性和任务设计方面的限制。根据约束,设计变量的影响以及目标之间可能的权衡,对学科耦合进行量化。

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