Abstract This paper introduces a special approach for the design of complex systems in early development stages accounting for lack-of-knowledge uncertainty. As complex systems have to be broken down into their components by a decoupling methodology, the presented work regards so-called box-shaped solution spaces as subsets of the total set of permissible designs not violating any design constraints. Hereby, the design variables are decoupled and their design intervals are maximized. Then, each aspect related to the corresponding interval can be studied independently in a subsequent development step by different stakeholders (design groups or designers). Especially in early design phases, the consideration of uncertainty is crucial; this is not so much related to aleatoric uncertainty as probability functions are often unavailable. More important and more difficult to handle is epistemic uncertainty, i.e., lack-of-knowledge uncertainty. Here, uncertainties which occur later in the development and the facts that the current design stage does not include smaller design features and that the available models represent only coarsely the later designs are important. This paper complements prior work by providing a complete methodology for relevant uncertainties. This includes uncertainties in controllable design variables as well as in uncontrollable parameters all captured by interval arithmetic. Furthermore, it extends existing worst-case approaches by best-case approaches. The user can now base design decisions on (a) a deterministic solution space without consideration of lack-of-knowledge uncertainties, (b) the same with consideration of uncertainties in uncontrollable parameters only or controllable variables only, or (c) the most complete approach where uncertainties in both controllable variables and uncontrollable parameters are considered. The corresponding scenarios are exemplified via examples from automotive engineering (design for crashworthiness).

中文翻译：

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缺乏知识不确定性下复杂系统的解耦设计方法

摘要 本文介绍了一种在早期开发阶段设计复杂系统的特殊方法，以解决知识缺乏的不确定性。由于复杂系统必须通过解耦方法分解为它们的组件，因此所提出的工作将所谓的盒形解决方案空间视为不违反任何设计约束的全部允许设计集的子集。因此，设计变量被解耦并且它们的设计间隔被最大化。然后，不同的利益相关者（设计组或设计师）可以在后续的开发步骤中独立研究与相应区间相关的每个方面。尤其是在早期设计阶段，对不确定性的考虑至关重要；这与随机不确定性没有太大关系，因为概率函数通常不可用。更重要和更难处理的是认知不确定性，即知识缺乏的不确定性。在这里，开发后期出现的不确定性以及当前设计阶段不包括较小的设计特征以及可用模型仅粗略地代表后期设计的事实很重要。本文通过为相关不确定性提供完整的方法来补充先前的工作。这包括可控设计变量以及所有由区间算法捕获的不可控参数的不确定性。此外，它通过最佳情况方法扩展了现有的最坏情况方法。用户现在可以基于 (a) 确定性解决方案空间做出设计决策，而无需考虑缺乏知识的不确定性，(b) 仅考虑不可控参数的不确定性或仅考虑可控变量的不确定性，或 (c) 考虑可控变量和不可控参数的不确定性的最完整方法。相应的场景通过汽车工程（耐撞性设计）的示例进行了举例说明。