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Research Papers

A Generative Human-in-the-Loop Approach for Conceptual Design Exploration Using Flow Failure Frequency in Functional Models1

[+] Author and Article Information
Ryan M. Arlitt

SUTD-MIT International Design Centre,
Singapore University of Technology and Design,
Singapore 487372
e-mails: arlitt.ryan@gmail.com;
rmarl@mek.dtu.dk

Douglas L. Van Bossuyt

Department of Systems Engineering,
Naval Postgraduate School,
Monterey, CA 93940
e-mail: douglas.vanbossuyt@nps.edu

2Present address: Department of Mechanical Engineering, Technical University of Denmark, Lyngby DK-2800 Kgs, Denmark.

3Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received August 26, 2018; final manuscript received February 14, 2019; published online March 18, 2019. Assoc. Editor: Jitesh H. Panchal.This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.

J. Comput. Inf. Sci. Eng 19(3), 031001 (Mar 18, 2019) (10 pages) Paper No: JCISE-18-1219; doi: 10.1115/1.4042913 History: Received August 26, 2018; Revised February 14, 2019

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.

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Figures

Grahic Jump Location
Fig. 1

The method presented here includes nine distinct steps, as shown in this graphic

Grahic Jump Location
Fig. 2

Visualization of roulette wheel sampling with branching factor of 1. Generated models expand outward into the search space toward local regions that are potentially interesting (as opposed to optimal). Higher fitness is represented as light, and lower fitness as dark. When the search concludes, results are selected for presentation to the user with respect to performance and global uniqueness.

Grahic Jump Location
Fig. 3

Functional model of electrical power system

Grahic Jump Location
Fig. 4

A snippet heat map of a model with poor performance. The fan module fails in many scenarios, indicated as a high failure rate in the flows related to cooling the inverter. In some cases, the failure propagates to the flows related to the inverter, which increases the failure rate of those flows.

Grahic Jump Location
Fig. 5

A snippet heat map of a model with medium performance. In this case, grammar rules have added an additional subgraph for exporting material, which led to a reduced rate of failure in the associated flows.

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