Systematic Comparison of Functional Models in SysML for Design Library Evaluation

Kruse, Benjamin; Gilz, Torsten; Shea, Kristina; Eigner, Martin

doi:10.1016/j.procir.2014.03.175

Cited by 11 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While a variety of conventions have been presented, in general function modelling represents a system as a set of functions which act on flows to accomplish a given task [19]. This specific representation of functionality is one of many system representations of products, but is uniquely useful for its lack of ambiguity and ability to be reused and transformed to simulate behavior [21]. It has subsequently been standardized as a part of the systems design process [22] [23].…”

Section: Background 21 Functional Modelingmentioning

confidence: 99%

Optimizing Function-Based Fault Propagation Model Resilience Using Expected Cost Scoring

Hulse

Hoyle

Goebel

et al. 2018

Volume 2A: 44th Design Automation Conference

View full text Add to dashboard Cite

Complex engineered systems are often associated with risk due to high failure consequences, high complexity, and large investments. As a result, it is desirable for complex engineered systems to be resilient such that they can avoid or quickly recover from faults. Ideally, this should be done at the early design stage where designers are most able to explore a large space of concepts. Previous work has shown that functional models can be used to predict fault propagation behavior and motivate design work. However, little has been done to formally optimize a design based on these predictions, partially because the effects of these models have not been quantified into an objective function to optimize. This work introduces a scoring function which integrates with a fault scenario-based simulation to enable the risk-neutral optimization of functional model resilience. This scoring function accomplishes this by resolving the tradeoffs between the design costs, operating costs, and modeled fault response of a given design in a way that may be parameterized in terms of designer-specified resilient features. This scoring function is adapted and applied to the optimization of controlling functions which recover flows in a monopropellant orbiter. In this case study, an evolutionary algorithm is found to find the optimal logic for these functions, showing an improvement over a typical a-priori guess by exploring a large range of solutions, demonstrating the value of the approach.

show abstract

Section: Background 21 Functional Modelingmentioning

confidence: 99%

Optimizing Function-Based Fault Propagation Model Resilience Using Expected Cost Scoring

Hulse

Hoyle

Goebel

et al. 2018

Volume 2A: 44th Design Automation Conference

View full text Add to dashboard Cite

show abstract

“…While a variety of modeling conventions have been presented in general, function modeling represents a system as a set of functions which act on flows of energy, material, and signal to accomplish a given task [47]. This specific representation of functionality is one of many system representations of products, but is uniquely useful for its lack of ambiguity and ability to be reused and transformed to simulate behavior [49]. It has long been a part of the engineering design curriculum [45,50,51] and has subsequently been standardized as a part of the systems design process [52,53].…”

Section: Functionalmentioning

confidence: 99%

Quantifying the Resilience-Informed Scenario Cost Sum: A Value-Driven Design Approach for Functional Hazard Assessment

Hulse

Hoyle

Goebel

et al. 2018

Journal of Mechanical Design

View full text Add to dashboard Cite

Complex engineered systems can carry risk of high failure consequences, and as a result, resilience—the ability to avoid or quickly recover from faults—is desirable. Ideally, resilience should be designed-in as early in the design process as possible so that designers can best leverage the ability to explore the design space. Toward this end, previous work has developed functional modeling languages which represent the functions which must be performed by a system and function-based fault modeling frameworks have been developed to predict the resulting fault propagation behavior of a given functional model. However, little has been done to formally optimize or compare designs based on these predictions, partially because the effects of these models have not been quantified into an objective function to optimize. The work described herein closes this gap by introducing the resilience-informed scenario cost sum (RISCS), a scoring function which integrates with a fault scenario-based simulation, to enable the optimization and evaluation of functional model resilience. The scoring function accomplishes this by quantifying the expected cost of a design's fault response using probability information, and combining this cost with design and operational costs such that it may be parameterized in terms of designer-specified resilient features. The usefulness and limitations of using this approach in a general optimization and concept selection framework are discussed in general, and demonstrated on a monopropellant system design problem. Using RISCS as an objective for optimization, the algorithm selects the set of resilient features which provides the optimal trade-off between design cost and risk. For concept selection, RISCS is used to judge whether resilient concept variants justify their design costs and make direct comparisons between different model structures.

show abstract

“…However this automation seems to restrict the complete abstraction of the designer. The graphical modeling language, Systems Modeling Language (SysML), has been incorporated into a behavior library at a high-level representation (Kruse et al, 2012(Kruse et al, , 2014Kruse & Shea, 2016). Yet many of these databases or repositories are independently operated and do not necessarily support collaboration.…”

Section: Engineering Design and Related Workmentioning

confidence: 99%

Introduction to quantitative engineering design methods via controls engineering

Lucero

Adams

Turner

2017

AIEDAM

View full text Add to dashboard Cite

Functional modeling is an effective method of depicting products in the design process. Using this approach, product architecture, concept generation, and physical modeling all contribute to the design process to generate a result full of quality and functionality. The functional basis approach provides taxonomy of uniform vocabulary to produce function structures with consistent functions (verbs) and flows (nouns). Material and energy flows dominate function structures in the mechanical engineering domain with only a small percentage including signal flows. Research suggests that the signal flow gap is due to the requirement of “carrier” flows of either material or energy to transport the signals between functions. This research suggests that incorporating controls engineering methodologies may increase the number of signal flows in function structures. We show correlations between the functional modeling and controls engineering in four facets: schematic similarities, performance matching through flows, mathematical function creation using bond graphs, and isomorphic matching of the aforementioned characteristics allows for analogical solutions. Controls systems use block diagrams to represent the sequential steps of the system. These block diagrams parallel the function structures of engineering design. Performance metrics between the two domains can be complimentary when decomposed down to nondimensional engineering units. Mathematical functions of the actions in controls systems can resemble the functional basis functions with bond graphs by identifying characteristic behavior of the functions on the flows. Isomorphic matching, using the schematic diagrams, produces analogies based upon similar functionality and target performance metrics. These four similarities bridge the mechanical and electrical domains via the controls domain. We provide concepts and contextualization for the methodology using domain-agnostic examples. We conclude with suggestion of pathways forward for this preliminary research.

show abstract

Systematic Comparison of Functional Models in SysML for Design Library Evaluation

Cited by 11 publications

References 14 publications

Optimizing Function-Based Fault Propagation Model Resilience Using Expected Cost Scoring

Optimizing Function-Based Fault Propagation Model Resilience Using Expected Cost Scoring

Quantifying the Resilience-Informed Scenario Cost Sum: A Value-Driven Design Approach for Functional Hazard Assessment

Introduction to quantitative engineering design methods via controls engineering

Contact Info

Product

Resources

About