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Solutions to decomposed branching trajectories with powered flyback using multidisciplinary design optimization.

机译：使用多学科设计优化解决带有动力反激的分解分支轨迹的解决方案。

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摘要

In the advanced launch vehicle design community, there exists considerable interest in fully reusable, two-stage-to-orbit vehicle designs that use ‘branching trajectories’ during their missions. For these reusable systems, the booster must fly to a predetermined landing site after staging occurs.; The solution to this problem using an industry-standard trajectory optimization code typically requires at least two separate computer jobs—one for the orbital branch from the ground to orbit (in some cases, this can be broken into two computer jobs) and one for the flyback branch from the staging point to the landing site. These jobs are tightly coupled and their data requirements are interdependent. In addition, the objective functions for each computer job differ and conflict.; This research produces a method to solve these distributed branching trajectory problems with respect to an overall system-level objective while maintaining data consistency within the problem. This method is used to solve the trajectories of two relevant two-stage-to-orbit vehicles: the Kistler K-1 and the Stargazer launch vehicles. Both of these vehicles require a powered flyback. Thus, optimization contingent on the feedback of the flyback fuel is a relevant part of this study.; The solutions of the branching trajectory problems via traditional methods, termed ‘One-and-Done’ and manual iteration, are compared with those involving the multidisciplinary design optimization techniques of fixed-point iteration, optimization-based decomposition, and collaborative optimization. Optimization-based decomposition was used to solve each problem; the K-1 trajectory includes a fixed-point iteration solution. The use of collaborative optimization as an solution technique for branching trajectories is introduced in the solution to each problem.; Results show that proposed method involving collaborative optimization and optimization-based decomposition performed well for both the K-1 and Stargazer branching trajectories. The use of these methods for the Kistler K-1 problem shows that an increase in payload weight of 1.0%, on average, could be obtained. Similarly, a reduction in Stargazer's dry weight of approximately 0.8% was achieved through the MDO methods. Conclusions concerning the method outline, comparisons of the method with differing solution techniques, staging flight path angle trends, and the automation of the optimization process are included.
机译：在先进的运载火箭设计界，人们对完全可重复使用的两阶段到轨道飞行器设计产生了浓厚的兴趣，这些设计在执行任务时使用了“分支轨迹”。对于这些可重复使用的系统，助推器必须在过渡发生后飞到预定的着陆点。使用行业标准的轨迹优化代码来解决此问题通常需要至少两个单独的计算机作业-一个用于从地面到轨道的轨道分支（在某些情况下，这可以分为两个计算机作业），而一个用于从暂存点到着陆点的反激分支。这些作业紧密耦合，并且它们的数据要求是相互依赖的。此外，每个计算机作业的目标功能各不相同且相互冲突。这项研究提出了一种方法来解决这些分布式分支轨迹问题，相对于整个系统级目标，同时保持问题内的数据一致性。该方法用于求解两种相关的两阶段进入轨道飞行器的轨迹：奇石乐 K-1 和 Stargazer 运载火箭。这两种车辆都需要动力反激。因此，取决于反激燃料的反馈的优化是本研究的相关部分。将通过传统方法（称为“一劳永逸”和手动迭代）解决分支轨迹问题的解决方案与涉及定点迭代，基于优化的分解和协作优化的多学科设计优化技术的解决方案进行了比较。基于优化的分解用于解决每个问题。 K-1 轨迹包括定点迭代解。在解决每个问题的方法中引入了使用协作优化作为分支轨迹的解决方案技术。结果表明，所提出的包含协同优化和基于优化的分解方法在 K-1 和 Stargazer 分支轨迹中均表现良好。将这些方法用于Kistler K-1 问题表明，平均有效载荷重量可增加1.0％。同样，通过MDO方法， Stargazer 的干重减少了约0.8％。包括有关方法概述的结论，使用不同解决方案技术进行的方法比较，分段飞行路径角度趋势以及优化过程的自动化。

著录项

  • 作者

    Ledsinger, Laura Anne.;

  • 作者单位

    Georgia Institute of Technology.;

  • 授予单位 Georgia Institute of Technology.;
  • 学科 Engineering Aerospace.
  • 学位 Ph.D.
  • 年度 2000
  • 页码 144 p.
  • 总页数 144
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 航空、航天技术的研究与探索;
  • 关键词

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