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Research Papers: Design Automation

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

[+] Author and Article Information
Daniel Hulse, Christopher Hoyle

School of Mechanical, Industrial and
Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97330

Kai Goebel

Tech Area Lead,
Discovery and Systems Health,
Intelligent Systems Division,
NASA Ames Research Center,
Moffett Field, CA 94035;
Adjunct Professor
Division of Operation and
Maintenance Engineering,
Luleå Technical University,
Luleå 97187, Sweden

Irem Y. Tumer

Professor
School of Mechanical, Industrial and
Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97330

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received June 28, 2018; final manuscript received September 10, 2018; published online December 20, 2018. Assoc. Editor: Nam H. Kim. This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.

J. Mech. Des 141(2), 021403 (Dec 20, 2018) (16 pages) Paper No: MD-18-1503; doi: 10.1115/1.4041571 History: Received June 28, 2018; Revised September 10, 2018

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.

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Figures

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Fig. 1

Illustration of a fault propagation simulation using IBFM. A fault propagates from an initiating mode through the flows of the functional model until it produces an end-state with resulting fault modes and flow health states.

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Fig. 2

Costs associated with a failure event in a resilient system

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Fig. 3

Illustration of fault re-simulation required to capture the costs of partial recovery Cr

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Fig. 4

Functional model of a signal-carrying medium, with modes, conditions, costs, and probabilities associated with each function

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Fig. 5

Framework enabled by integrating cost-based scoring and fault simulation. The designer sets up a parameterized design problem which is then solved by an optimization algorithm.

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Fig. 6

Functional model of base monopropellant system

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Fig. 7

Example controlling function conditions and modes

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Fig. 8

Cost optimization of the functional model using the evolutionary algorithm, showing how value can be increased using the presented optimization framework

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Fig. 9

Differential costs of design variants based on fault simulation

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Fig. 10

Design variant 1: redundant gas tanks

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Fig. 11

Design variant 2: redundant thrusters

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Fig. 12

Design variant 3: heat recovery system

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Fig. 13

Design variant 4: redundant pressure regulators

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Fig. 14

Design variant 5: optimized control features

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