A decoupled design approach for complex systems under lack-of-knowledge uncertainty

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). (C) 2020 Elsevier Inc. All rights reserved.