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A methodology for rapid vehicle scaling and configuration space exploration.

机译：快速车辆缩放和配置空间探索的方法。

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摘要

The Configuration-space Exploration and Scaling Methodology (CESM) entails the representation of component or sub-system geometries as matrices of points in 3D space. These typically large matrices are reduced using minimal convex sets or convex hulls. This reduction leads to significant gains in collision detection speed at minimal approximation expense. (The Gilbert-Johnson-Keerthi algorithm [79] is used for collision detection purposes in this methodology.) Once the components are laid out, their collective convex hull (from here on out referred to as the super-hull) is used to approximate the inner mold line of the minimum enclosing envelope of the vehicle concept. A sectional slicing algorithm is used to extract the sectional dimensions of this envelope. An offset is added to these dimensions in order to come up with the sectional fuselage dimensions. Once the lift and control surfaces are added, vehicle level objective functions can be evaluated and compared to other designs. The size of the design space coupled with the fact that some key constraints such as the number of collisions are discontinuous, dictate that a domain-spanning optimization routine be used. Also, as this is a conceptual design tool, the goal is to provide the designer with a diverse baseline geometry space from which to chose. For these reasons, a domain-spanning algorithm with counter-measures against speciation and genetic drift is the recommended optimization approach. The Non-dominated Sorting Genetic Algorithm (NSGA-II) [60] is shown to work well for the proof of concept study.;There are two major reasons why the need to evaluate higher fidelity, custom geometric scaling laws became a part of this body of work. First of all, historical-data based regressions become implicitly unreliable when the vehicle concept in question is designed around a disruptive technology. Second, it was shown that simpler approaches such as photographic scaling can result in highly suboptimal concepts even for very small scaling factors. Yet good scaling information is critical to the success of any conceptual design process. In the CESM methodology, it is assumed that the new technology has matured enough to permit the prediction of the scaling behavior of the various subsystems in response to requirement changes. Updated subsystem geometry data is generated by applying the new requirement settings to the affected subsystems. All collisions are then eliminated using the NSGA-II algorithm. This is done while minimizing the adverse impact on the vehicle packing density. Once all collisions are eliminated, the vehicle geometry is reconstructed and system level data such as fuselage volume can be harvested. This process is repeated for all requirement settings. Dimensional analysis and regression can be carried out using this data and all other pertinent metrics in the manner described by Mendez [124] and Segel [173]. The dominant parameters for each response show up as in the dimensionally consistent groups that form the independent variables. More importantly the impact of changes in any of these variables on system level dependent variables can be easily and rapidly evaluated. In this way, the conceptual design process can be accelerated without sacrificing analysis accuracy. Scaling laws for take-off gross weight and fuselage volume as functions of fuel cell specific power and power density for a notional General Aviation vehicle are derived for the proof of concept.;CESM enables the designer to maintain design freedom by portably carrying multiple designs deeper into the design process. Also since CESM is a bottom-up approach, all proposed baseline concepts are implicitly volumetrically feasible. System level geometry parameters become fall-outs as opposed to inputs. This is a critical attribute as, without the benefit of experience, a designer would be hard pressed to set the appropriate ranges for such parameters for a vehicle built around a disruptive technology. Furthermore, scaling laws generated from custom data for each concept are subject to less design noise than say, regression based approaches. Through these laws, key physics-based characteristics of vehicle subsystems such as energy density can be mapped onto key system level metrics such as fuselage volume or take-off gross weight. These laws can then substitute some historical-data based analyses thereby improving the fidelity of the analyses and reducing design time. (Abstract shortened by UMI.)
机译：配置空间探索和缩放方法论（CESM）要求将组件或子系统的几何图形表示为3D空间中的点矩阵。使用最少的凸集或凸包可减少这些通常较大的矩阵。这种减少导致以最小的近似开销显着提高了碰撞检测速度。 （在该方法中，Gilbert-Johnson-Keerthi算法[79]用于碰撞检测。）一旦布置了组件，就使用它们的集体凸包（从此开始称为超壳）进行近似。车辆概念的最小封闭外壳的内模线。使用截面切片算法来提取此信封的截面尺寸。为这些尺寸增加了偏移量，以得出机身的截面尺寸。一旦增加了提升和控制面，就可以评估车辆高度目标功能并将其与其他设计进行比较。设计空间的大小以及一些关键约束（例如冲突数）是不连续的事实，决定了使用跨域优化例程。另外，由于这是一种概念设计工具，因此目标是为设计人员提供可供选择的不同基线几何空间。由于这些原因，建议使用具有针对物种和遗传漂移的对策的跨域算法。非支配排序遗传算法（NSGA-II）[60]被证明可以很好地用于概念验证研究。工作主体。首先，当所讨论的车辆概念围绕破坏性技术进行设计时，基于历史数据的回归将变得不可靠。其次，研究表明，即使对于很小的缩放比例，更简单的方法（例如摄影缩放）也可能导致高度次优的概念。然而，良好的缩放信息对于任何概念设计过程的成功都是至关重要的。在CESM方法论中，假设新技术已经足够成熟，可以预测各种子系统响应需求变化的缩放行为。通过将新的需求设置应用于受影响的子系统来生成更新的子系统几何数据。然后使用NSGA-II算法消除所有冲突。这样做是在将对车辆包装密度的不利影响降至最低的同时。一旦消除了所有碰撞，就可以重新构造车辆的几何形状，并可以收集系统级数据，例如机身体积。对所有需求设置重复此过程。可以使用这些数据和所有其他相关指标，按照Mendez [124]和Segel [173]所述的方式，进行维度分析和回归。每个响应的主要参数显示在形成自变量的尺寸一致的组中。更重要的是，可以轻松，快速地评估任何这些变量的变化对系统级依存变量的影响。这样，可以在不牺牲分析准确性的情况下加速概念设计过程。为概念验证得出了起飞总重量和机身体积随燃料电池特定功率和功率密度而变化的比例定律，以进行概念验证。CESM使设计师能够通过携带更深的多个设计来保持设计自由度进入设计过程。同样，由于CESM是一种自下而上的方法，因此所有提出的基线概念在体积上都是隐含的。与输入相反，系统级几何参数成为结果。这是一个关键属性，因为如果没有经验的帮助，设计人员将很难为围绕破坏性技术制造的车辆设置此类参数的适当范围。此外，与每个基于回归的方法相比，从每个概念的自定义数据生成的缩放定律受到的设计噪声较小。通过这些定律，可以将车辆子系统的基于物理的关键特性（例如能量密度）映射到关键系统级指标（例如机身体积或起飞总重量）上。然后，这些定律可以替代某些基于历史数据的分析，从而提高分析的保真度并减少设计时间。 （摘要由UMI缩短。）

著录项

  • 作者

    Balaba, Davis.;

  • 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 2009
  • 页码 302 p.
  • 总页数 302
  • 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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