Quantitative Set-Based Design to Inform Design Teams

Abstract

System designers, analysts, and engineers use various techniques to develop complex systems. A traditional design approach, point-based design (PBD), uses system decomposition and modeling, simulation, optimization, and analysis to find and compare discrete design alternatives. Set-based design (SBD) is a concurrent engineering technique that compares a large number of design alternatives grouped into sets. The existing SBD literature discusses the qualitative team-based characteristics of SBD, but lacks insights into how to quantitatively perform SBD in a team environment. This paper proposes a qualitative SBD conceptual framework for system design, proposes a team-based, quantitative SBD approach for early system design and analysis, and uses an unmanned aerial vehicle case study with an integrated model-based engineering framework to demonstrate the potential benefits of SBD. We found that quantitative SBD tradespace exploration can identify potential designs, assess design feasibility, inform system requirement analysis, and evaluate feasible designs. Additionally, SBD helps designers and analysts assess design decisions by providing an understanding of how each design decision affects the feasible design space. We conclude that SBD provides a more holistic tradespace exploration process since it provides an integrated examination of system requirements and design decisions.

Application of Probabilistic Set-Based Design Exploration on the Energy Management of a Hybrid-Electric Aircraft

“…Set-Based Design has been recently investigated for the problem of designing products under flexible requirements [37,38], and incorporate resilience to unexpected modes of operation [39]. Furthermore, the large sweeping of the design space combined with a multi-disciplinary model makes SBD principles suitable for design space exploration and trade-off activities of complex systems [40]. SBD has also been applied in decision analysis, where it has been combined with Value of Information methods, enabling robust design selection [41,42].…”

Using value of information in quantitative set‐based design

“…These activities are critical to reducing the system design space onto a single set of design solutions. 3,4 Thus, design convergence requires that program managers make both uncertainty reduction and design maturation decisions. An uncertainty reduction decision is any decision whose objective is the reduction of epistemic uncertainty, or improved understanding of the sources and effects of aleatory uncertainty on system design and performance.…”

“…Previous research has used this case study in demonstrating approaches enabling tradespace exploration, 13,23,24 and SBD program management. 1,4 We provide a synopsis of results from research investigating SBD design maturation, 1 which we will later compare to our MOVOI enabled case study.…”