A Generative Human-in-the-Loop Approach for Conceptual Design Exploration Using Flow Failure Frequency in Functional Models
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Authors
Arlitt, Ryan M.
Van Bossuyt, Douglas L.
Subjects
Advisors
Date of Issue
2019
Date
2019
Publisher
ASME
Language
Abstract
A challenge systems engineers and designers face when applying system failure risk assessment methods such as probabilistic risk assessment (PRA) during conceptual design is their reliance on historical data and behavioral models. This paper presents a framework for exploring a space of functional models using graph rewriting rules and a qualitative failure simulation framework that presents information in an intuitive manner for human-in-the-loop decision-making and human-guided design. An example is presented wherein a functional model of an electrical power system testbed is iteratively perturbed to generate alternatives. The alternative functional models suggest different approaches to mitigating an emergent system failure vulnerability in the electrical power system’s heat extraction capability. A preferred functional model configuration that has a desirable failure flow distribution can then be identified. The method presented here helps systems designers to better understand where failures propagate through systems and guides modification of systems functional models to adjust the way in which systems fail to have more desirable characteristics.
Type
Article
Description
The article of record as published may be found at https://doi.org/10.1115/1.4042913
Series/Report No
Department
Systems Engineering (SE)
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
This research is partially supported by the Naval Postgraduate School, the Singapore University of Technology and Design, the Technical University of Denmark, and Oregon State University.
Funder
Format
10 p.
Citation
Arlitt, Ryan M., and Douglas L. Van Bossuyt. "A generative human-in-the-loop approach for conceptual design exploration using flow failure frequency in functional models." Journal of Computing and Information Science in Engineering 19.3 (2019).
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.