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Research Papers

On Risk-Based Design of Complex Engineering Systems: An Analytical Extreme Event Framework

[+] Author and Article Information
Hami Golbayani

Department of Mechanical Engineering,
University of Connecticut, Storrs, CT 06269 e-mail: hami.golbayani@uconn.edu

Kazem Kazerounian

Professor Fellow ASME
Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269 e-mail: kazem@engr.uconn.edu

1Corresponding author.

Manuscript received July 15, 2014; final manuscript received December 1, 2014; published online February 27, 2015. Assoc. Editor: Michael Beer.

ASME J. Risk Uncertainty Part B 1(1), 011002 (Feb 27, 2015) (7 pages) Paper No: RISK-14-1030; doi: 10.1115/1.4029142 History: Received July 15, 2014; Accepted December 09, 2014; Online February 27, 2015

In this work, a novel analytical framework is proposed for the risk-based design of complex engineering systems. The risk based-design process is the reverse process of risk propagation that entails the optimum allocation of the desired risk of failure of the system to all of its components. This paper studies the challenges in the design process of complex systems, the mathematical modeling of their topological architecture, and their unique behaviors. These characteristics make it impossible for the designer to use the common reliability and risk methodologies in the design process. The fundamental development of this work is an analytical upper bound for the distribution of risk of failure in the subsystem or element level. This upper bound satisfies the subadditivity condition of a coherent measure of risk. Additionally, its simplicity and low computational cost provide an appropriate framework as a fundamental building block for the risk-based design of complex systems. The proposed methodology is applied to three examples with insignificant desired probability of failure and its accuracy is compared with the Monte Carlo simulation, demonstrating its effectiveness and value.

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Figures

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Fig. 1

A complex network (source: [1])

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Fig. 2

Failure of the limit state function at the design point and tail of the distributions

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Fig. 3

Two-linkage planar manipulator

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Fig. 4

Simulated limit state function by using MCS and the proposed method (solid line) in Ex. 1

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Fig. 5

QQplot of limit state function in Ex. 1 versus standard normal

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Fig. 6

Three-linkage spatial manipulator

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Fig. 7

Simulated limit state function, by using MCS and the proposed method (solid line) in Ex. 2

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Fig. 8

The complex network of Ex. 3 modeled in NetLogo [31,32]

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Fig. 9

The distribution of the eigenvalues of the adjacency matrix in Ex. 3

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Fig. 10

Simulated PDF of failure for the complex system in Ex. 3, MCS, and the proposed method (solid line)

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