MBSE and MDAO for Early Validation of Design Decisions: a Bibliography Survey

Chaudemar, Jean-Charles; Saqui-Sannes, Pierre de

doi:10.1109/syscon48628.2021.9447140

Cited by 13 publications

(10 citation statements)

References 29 publications

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“…Over the past decade, Model Based Systems Engineering (MBSE) and Multidisciplinary Design Analysis and Optimization (MDAO) have been investigated separately. In [20] Chaudemar and de Saqui-Sannes have identified several benefits of coupling MBSE and MDAO approaches. This paper goes one step forward and instantiates the ideas developed in [20].…”

Section: Discussionmentioning

confidence: 99%

“…In [20] Chaudemar and de Saqui-Sannes have identified several benefits of coupling MBSE and MDAO approaches. This paper goes one step forward and instantiates the ideas developed in [20].…”

Section: Discussionmentioning

confidence: 99%

“…In [20], Chaudemar and de Saqui-Sannes state that joint use of MBSE and MDAO techniques may help taking design decisions and thus populating the design diagrams and the tools enabling checking of design diagrams.…”

Section: B Mdao Modelmentioning

confidence: 99%

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Populating MBSE Models from MDAO Analysis

Aiello

Kandel

Chaudemar

et al. 2021

2021 IEEE International Symposium on Systems Engineering (ISSE)

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Over the past decade, Systems Engineering has switched from document-centric approaches to model-based ones. In this context, Model Based System Engineering (MBSE) and Multidisciplinary Design Analysis and Optimization (MDAO) have emerged as two complementary disciplines. How to combine MBSE and MDAO approaches for the benefits of systems engineers is still an open issue. This paper discusses a case study of coupling MBSE and MDAO. The MBSE part relies on SysML and timed automata, two modeling languages that are supported by the TTool and UPPAAL-SMC tools, respectively. The MDAO part is developed in the context of Open MDAO. The paper uses a drone as a case study and focuses discussion on battery usage. The SysML model of the drone is enhanced with a timed automata model of the battery. The SysML model of the battery is populated with results from MDAO analysis. In this context, combining SysML, UPPAAL-SMC and Open-MDAO offers to improve self-confidence in some values lying in the model, improve self-confidence in the requirements satisfaction, as well as to refine some requirements values with better accuracy.

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Section: Discussionmentioning

confidence: 99%

“…In [20] Chaudemar and de Saqui-Sannes have identified several benefits of coupling MBSE and MDAO approaches. This paper goes one step forward and instantiates the ideas developed in [20].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Populating MBSE Models from MDAO Analysis

Aiello

Kandel

Chaudemar

et al. 2021

2021 IEEE International Symposium on Systems Engineering (ISSE)

Self Cite

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“…The main function of both bridges is to verify that the developed MDAO design process sufficiently covers the requirements and architectures so that a realistic and useful result can be obtained from it. Additionally, these bridges can be used to automatically provide feedback as part of the architecture optimization loop and automatically verify requirements for a given design solution, similar to the strong coupling approach identified by (Chaudemar & de Saqui-Sannes, 2021).…”

Section: Mbse Architecting To Mdaomentioning

confidence: 99%

From System Architecting to System Design and Optimization: A Link Between MBSE and MDAO

Bussemaker

Boggero

Ciampa

2022

INCOSE International Symp

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Optimization of system architectures can help deal with finding better system architectures in a large design space plagued by combinatorial explosion of alternatives. To enable architecture optimization, the design space should therefore be formalized into a numerical optimization problem, and it should be possible to quantitatively evaluate architecture alternatives. This paper presents a methodology for generating and modeling architecture design spaces using the Architecture Design Space Graph (ADSG), and using collaborative Multidisciplinary Design Analysis and Optimization (MDAO) techniques to evaluate architectures. Collaborative MDAO leverages disciplinary expertise while ensuring that analysis tools exchange data consistently and correctly using a central data schema. The problem solved in this paper is the missing link between architecture optimization and collaborative MDAO: the reflection of generated architectures in the central data schema. It is solved by the authors by mapping architecture components and Quantities of Interest (QOIs) to the central data schema using Data Schema Operations (DSOs). Such a mapping also assists the user in identifying missing or unnecessary disciplinary analysis tools. Three web-based software tools implementing the methodology are presented. Finally, the methodology and tools are demonstrated using the design of a supersonic business jet as an example.

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“…In this case, the MBSE system model acts as a single source of truth (SSO) for MDAO, as all data (requirements, models, …) related to the developed product are centrally stored. The initial effort for MDAO is reduced because the required development data exists in the MBSE system model in parts or completely [26]. Results of MDAO are systematically transferred to the linked model elements of the system architecture.…”

Section: Problem Statementmentioning

confidence: 99%

Optimization Workflows for Linking Model-Based Systems Engineering (MBSE) and Multidisciplinary Analysis and Optimization (MDAO)

et al. 2022

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Developing modern products involves numerous domains (controlling, production, engineering, etc.) and disciplines (mechanics, electronics, software, etc.). The products have become increasingly complex while their time to market has decreased. These challenges can be overcome by Model-Based Systems Engineering (MBSE), where all development data (requirements, architecture, etc.) is stored and linked in a system model. In an MBSE system model, product requirements at the system level can lead to numerous technical variants with conflicting objectives at the parameter level. To determine the best technical variants or tradeoffs, Multidisciplinary Analysis and Optimization (MDAO) is already being used today. Linking MBSE and MDAO allows for mutually beneficial synergies to be expected that have not yet been fully exploited. In this paper, a new approach to link MBSE and MDAO is proposed. The novelty compared to existing approaches is the reuse of existing MBSE system model data. Models developed during upstream design and test activities already linked to the MBSE system model were integrated into an MDAO problem. Benefits are reduced initial and reconfiguration efforts and the resolution of the MDAO black-box behavior. For the first time, the MDAO problem was modeled as a workflow using activity diagrams in the MBSE system model. For a given system architecture, this workflow finds the design variable values that allow for the best tradeoff of objectives. The structure and behavior of the workflow were formally described in the MBSE system model with SysML. The presented approach for linking MBSE and MDAO is demonstrated using an example of an electric coolant pump.

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MBSE and MDAO for Early Validation of Design Decisions: a Bibliography Survey

Cited by 13 publications

References 29 publications

Populating MBSE Models from MDAO Analysis

Populating MBSE Models from MDAO Analysis

From System Architecting to System Design and Optimization: A Link Between MBSE and MDAO

Optimization Workflows for Linking Model-Based Systems Engineering (MBSE) and Multidisciplinary Analysis and Optimization (MDAO)

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