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Multifidelity Multidisciplinary Design Optimization of Integral Solid Propellant Ramjet Supersonic Cruise Vehicles

机译:多尺多学科设计优化整体式固体推进剂Ramjet超声波巡航车辆

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Integral solid propellant ramjet (ISPR) supersonic cruise vehicles share the characteristic that they are highly integrated configurations. The traditional design of vehicles cannot achieve a balance between computational expense and accuracy. A multifidelity multidisciplinary design optimization (MDO) platform has been developed in this study. The focus of the platform is on ISPR supersonic cruise vehicles. Firstly, codes of discipline with different levels of fidelity (LoF) were established, such as geometry, aerodynamics, radar cross-section calculations, propulsion, mass, and trajectory discipline codes. Secondly, two MDO frameworks were constructed through discipline codes. A low LoF MDO framework is suitable for conceptual design, and a medium LoF MDO framework is suitable for preliminary design. Finally, taking the optimization problem with the minimum overall detection probability of flight trajectory as an example, the low LoF framework first explores the entire design space to achieve the mission requirements, and then, the medium LoF MDO framework accepts the low LoF framework optimization parameters. Hence, the optimization target is reached with more detailed parameters and higher fidelity. Additionally, an example for a solid propellant missile with minimum total mass is tested by the platform. The study results show that the multifidelity MDO framework not only exploits interactions between the disciplines but also improves the accuracy of optimization results and reduces the iteration time.
机译:整体固体推进剂拉姆喷嘴(ISPR)超音速巡航车辆共享它们是高度集成配置的特征。传统的车辆设计无法在计算费用和准确性之间实现平衡。本研究开发了多思率多学科设计优化(MDO)平台。该平台的重点是在ISPR超音速巡航车辆上。首先,建立了具有不同保真度(LOF)级别的纪律守则,例如几何,空气动力学,雷达横截面计算,推进,质量和轨迹纪律代码。其次,通过纪律代码构建了两个MDO框架。低LOF MDO框架适用于概念设计,媒体LOF MDO框架适用于初步设计。最后,通过飞行轨迹的最小总体检测概率来实现优化问题,例如,低调框架首先探讨了整个设计空间以实现任务要求,然后,媒体LOF MDO框架接受低LOF框架优化参数。因此,利用更详细的参数和更高的保真度来达到优化目标。另外,该平台测试了具有最小总质量的固体推进剂导弹的实例。研究结果表明,多尺MDO框架不仅利用了学科之间的相互作用,而且还提高了优化结果的准确性并减少了迭代时间。

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