Toward scaling model-based engineering for systems of systems

Antul, Laura; Ricks, Sean; Cho, Lance Mann Kyo; Cotter, Matt; Jacobs, Ryan B.; Markina‐Khusid, Aleksandra; Kamenetsky, Janna; Dahmann, Judith; Tran, Huy

doi:10.1109/aero.2018.8396427

Cited by 4 publications

(3 citation statements)

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“…Other studies explore network metrics to characterize the overall robustness of a system (or an SoS) to the removal or loss of a node or an edge (e.g., a CS removal or an interaction loss between two CSs). For instance, Antul et al, (2017) represents an SoS as the network of its CSs and measures the algebraic connectivity to capture "a network's vulnerability to disconnection (e.g., removal of a CSs)." In a similar fashion, the robustness coefficient has been used in the literature (Haley et al, 2016;Paparistodimou, Duffy, Whitfield, Knight, & Robb, 2020;Walsh et al, 2019) and characterizes the largest connected component (connected nodes) after a node removal (e.g., removal of a CS in an SoS or the loss of a function).…”

Section: -(C4) Low Computational Costmentioning

confidence: 99%

Characterizing Systems Of Systems Change And Failure Via Network-based Metrics

Fakhfakh

Hein

Chazal

et al. 2020

DS 103: Proceedings of the 22nd International DSM Conference (DSM 2020), MIT, Cambridge, Massachusetts, October 13th - 15th 202

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A System of System (SoS) is a synthesis of independent systems functioning together towards a common goal. They are characterized by their dynamic nature and evolvability during operation: addition, removal, and modification of component systems and functions. It is, therefore, important to characterize the tolerance of such systems to changes and failures. Most change propagation and failure analysis methods require some knowledge of failure and change probabilities, failure modes, and design parameters, which is difficult to obtain or unavailable to an SoS decision-maker, as component systems are independent in their management and operation. Consequently, this paper uses highlevel SoS functional models and network-based metrics to characterize SoSs functions and assess the functional change and failure of such systems. The proposed measures are deployed on an electric vehicle to grid-related service to show how it can aid an SoS decision-maker during the system's development and operation.

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Section: -(C4) Low Computational Costmentioning

confidence: 99%

Characterizing Systems Of Systems Change And Failure Via Network-based Metrics

Fakhfakh

Hein

Chazal

et al. 2020

DS 103: Proceedings of the 22nd International DSM Conference (DSM 2020), MIT, Cambridge, Massachusetts, October 13th - 15th 202

View full text Add to dashboard Cite

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“…Though these metrics do not quantify resilience (since they do not explicitly account for recovery dynamics), they are still useful for assessing SoS architecture alternatives given the importance of robustness to resilience. Antul et al (2018) quantified the robustness of three architecture variations of a notional military SoS and compared observed trends from this lightweight approach to one based on running detailed simulations to characterize the performance of the same architectures. Virtual simulation experiments provided evidence of the predictive value of the graph-theoretic metrics for SoS analysis.…”

Section: Evaluating Alternativesmentioning

confidence: 99%

“…The notional scenario considered for network robustness analysis. The critical asset is in the center, with red lines indicating undetected threat aircraft and green dots representing successfully intercept threats (Antul et al 2018 Antul et al (2018) challenges above. A structured approach to establishing goals and evaluation criteria is a critical early step in trade-off analysis, and SVN modeling shows potential for elucidating stakeholder priorities in an SoS context with conflicting objectives.…”

Section: Evaluating Alternativesmentioning

confidence: 99%

Challenges and Opportunities in Trade‐off Analytics for Systems of Systems

Monahan

Jacobs

Markina‐Khusid

et al. 2018

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Increasingly the systems engineering community recognizes that success of many of today's enterprises depends on our ability to effectively reason about and harness Systems of Systems (SoS) capabilities. This paper specifically addresses the challenges in conducting trade‐off analysis for SoS, approaches that help to alleviate these challenges, and identifies opportunities for further development. For large SoS the use of lightweight analysis approaches provides a way to manage the complexity and identify promising options for more detail trade‐off analysis approaches.

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