Early Design Space Exploration with Model-Based System Engineering and Set-Based Design

Specking, Eric; Parnell, Gregory S.; Pohl, Edward A.; Buchanan, Randy K.

doi:10.3390/systems6040045

Cited by 28 publications

(18 citation statements)

References 28 publications

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“…From this set, we identified and collected only refereed journal articles and conference papers, reducing the set to the 64 SBD specific documents considered in the final review. We refer to this set of documents as Group A throughout the remainder of this paper 2–65 . We provide the complete listing for all Group A documents in Table 1.…”

Section: Methodsmentioning

confidence: 99%

“…In practice, SBD develops and analyzes numerous system and subsystem alternatives organized within the design space in groups called sets. A set is a grouping of similar, yet distinct, design alternatives sharing at least one common design feature as shown in Figure 1 16,19,20 . SBD design spaces are suitable for both discrete and continuous design variables, and it is possible for design spaces to contain millions of potential options during early design exploration 6,21 .…”

Section: Sbd For Complex Systemsmentioning

confidence: 99%

“…Figure 8 reinforces our earlier finding that limited research exists contributing comprehensive methodologies to SBD's state‐of‐practice. As we can see, only one document, written by Specking et al, contributes to all four SBD research areas 20 . However, their research focuses on trade‐off analytics, the focus of the paper, with the other three research areas presented in supporting roles.…”

Section: Set‐based Design Knowledge Gaps and Research Opportunitiesmentioning

confidence: 99%

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Set‐based design: The state‐of‐practice and research opportunities

Shallcross¹,

Parnell²,

Pohl

et al. 2020

Systems Engineering

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Increasing system complexity has provided the impetus to develop new and novel systems engineering methodologies. One of these methodologies is set‐based design (SBD), a concurrent design methodology well suited for complex systems subject to significant uncertainty. Since the 1990s, numerous private, public, and defense sector design programs have successfully implemented SBD. However, concerns regarding SBD's complexity, tendency toward qualitative methods, and lack of quantitative tools have limited its use. To address these issues, our research surveys 122 refereed journal articles and conference papers to assess SBD's state‐of‐practice and identify relevant research opportunities. To accomplish these tasks, we perform a structured literature review to identify and assess relevant and influential research. We found that SBD's state‐of‐practice relies heavily upon decision and tradespace analysis with increasing emphasis on uncertainty modeling and MBSE. We found that the majority of SBD research consists of quantitative methodologies focusing on component and small system applications. We also found that complex system applications used mostly qualitative methodologies. We identify SBD research opportunities for requirements development, MBSE, uncertainty modeling, multiresolution modeling, adversarial analysis, and program management. Finally, we recommend the development of a comprehensive SBD methodology and toolkit, suited for complex system design across all stages of the product development life cycle.

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Section: Methodsmentioning

confidence: 99%

Section: Sbd For Complex Systemsmentioning

confidence: 99%

Section: Set‐based Design Knowledge Gaps and Research Opportunitiesmentioning

confidence: 99%

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Set‐based design: The state‐of‐practice and research opportunities

Shallcross¹,

Parnell²,

Pohl

et al. 2020

Systems Engineering

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show abstract

“…Each design phase increases information until a single design is chosen for production. Specking et al (2018b) provides a SBD implementation method for early design space exploration. Figure 1 graphically illustrates SBD compared to PBD.…”

Section: Motivation For Set-based Designmentioning

confidence: 99%

“…The seven bottom rows of Figure 3 were the design decisions. Small (2018) implemented SBD by using the SBD implementation method described by Specking et al (2018b) by uniformly sampling all the design decisions and removing the infeasible design points. The uniform sampling with 100,000 designs used SIPmath from Probability Management (Savage, 2018).…”

Section: Uav Demonstration Modelmentioning

confidence: 99%

Using Set‐Based Design to Inform System Requirements and Evaluate Design Decisions

Parnell

Specking

Goerger

et al. 2019

INCOSE International Symp

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Set‐Based Design (SBD) is a concurrent engineering technique for system design and systems analysis and an alternative to Point‐Based Design (PBD). System analysts perform Tradespace Exploration in early system design to assess requirements and identify affordable designs that meet complex stakeholder requirements. This paper demonstrates the use of SBD to inform system requirements and evaluate design options. We use an Unmanned Aerial Vehicle (UAV) case study with an integrated Model‐Based Engineering framework with SBD to identify potential designs, assess design feasibility, inform requirements, and evaluate feasible designs displayed in sets with common design choices. We use SIP Math from Probability Management© to explore 100,000 potential systems design concepts and architectures in near real‐time. Using our integrated framework with 47 physics‐based models and 11 requirements, we found that only 2.5% of the potential designs were feasible. We validate that SBD finds the efficient frontier in the value versus cost tradespace. We use SBD to inform systems requirements by identifying the number of potential feasible designs in the tradespace for each requirement or combination of requirements. Relaxing the requirements can increase the percentage of feasible designs. Constraining or relaxing 4 of the 11 requirements significantly affected the feasible design space. We also use SBD to evaluate design options, which provides useful information for system designers. We conclude that SBD provides a more comprehensive tradespace exploration than PBD and useful insights into requirement development.

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MBSE Enabled Trade‐Off Analyses

Parnell

Shallcross

Specking

et al. 2021

INCOSE International Symp

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This paper examines the potential to use Model-Based Systems Engineering (MBSE) tools to perform trade-off analysis of alternative systems decisions in the system life cycle from the concept stage to the retirement stage. Specially, we searched for integrated models that automate the simultaneous evaluation of the performance, effectiveness, stakeholder value, and cost of multiple alternative system designs. We used the Web of Science to perform a literature search to identify published papers that describe the use of MBSE tools to support automated analysis of alternatives and trade-off analyses. We found very few papers that claimed to use MBSE to provide analysis of design alternatives or tradespace exploration. Based on the literature search insights, we identify and describe the required and desired capabilities to perform automated trade-off analyses of performance, effectiveness, stakeholder value, and cost for multiple system design alternatives using integrated models.

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Early Design Space Exploration with Model-Based System Engineering and Set-Based Design

Cited by 28 publications

References 28 publications

Set‐based design: The state‐of‐practice and research opportunities

Set‐based design: The state‐of‐practice and research opportunities

Using Set‐Based Design to Inform System Requirements and Evaluate Design Decisions

MBSE Enabled Trade‐Off Analyses

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