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Research Papers

Simulation of Interactions and Emergent Failure Behavior During Complex System Design

[+] Author and Article Information
Nikolaos Papakonstantinou, David C. Jensen

Department of Automation and Systems Technology,  School of Electrical Engineering, Aalto University, P.O. Box 15500, Aalto, 00076 FinlandComplex Engineered System Design Laboratory, School of Mechanical, Industrial, and Manufacturing Engineering,  Oregon State University, 204 Rogers Hall, Corvallis, OR 97331

Seppo Sierla

Department of Automation and Systems Technology,  School of Electrical Engineering, Aalto University, P.O. Box 15500, Aalto, 00076 Finlandseppo.sierla@aalto.fiComplex Engineered System Design Laboratory, School of Mechanical, Industrial, and Manufacturing Engineering,  Oregon State University, 204 Rogers Hall, Corvallis, OR 97331seppo.sierla@aalto.fi

Irem Y. Tumer1

Department of Automation and Systems Technology,  School of Electrical Engineering, Aalto University, P.O. Box 15500, Aalto, 00076 Finlandirem.tumer@oregonstate.eduComplex Engineered System Design Laboratory, School of Mechanical, Industrial, and Manufacturing Engineering,  Oregon State University, 204 Rogers Hall, Corvallis, OR 97331irem.tumer@oregonstate.edu

1

Corresponding author.

J. Comput. Inf. Sci. Eng 12(3), 031007 (Aug 21, 2012) (10 pages) doi:10.1115/1.4007309 History: Received October 25, 2011; Revised July 19, 2012; Published August 21, 2012; Online August 21, 2012

Emergent behavior is a unique aspect of complex systems, where they exhibit behavior that is more complex than the sum of the behavior of their constituent parts. This behavior includes the propagation of faults between parts, and requires information on how the parts are connected. These parts can include software, electronic and mechanical components, hence requiring a capability to track emergent fault propagation paths as they cross the boundaries of technical disciplines. Prior work has introduced the functional failure identification and propagation (FFIP) simulation framework, which reveals the propagation of abnormal flow states and can thus be used to infer emergent system-wide behavior that may compromise the reliability of the system. An advantage of FFIP is that it is used to model early phase designs, before high cost commitments are made and before high fidelity models are available. This has also been a weakness in previous research on FFIP, since results depend on arbitrary choices for the values of model parameters and timing of critical events. Previously, FFIP has used a discrete set of flow state values and a simple behavioral logic; this has had the advantage of limiting the range of possible parameter values, but it has not been possible to model continuous process dynamics. In this paper, the FFIP framework has been extended to support continuous flow levels and linear modeling of component behavior based on first principles. Since this extension further expands the range of model parameter values, methods and tools for studying the impact of parameter value changes are introduced. The result is an evaluation of how the FFIP results are impacted by changes in the model parameters and the timing of critical events. The method is demonstrated on a boiling water reactor model (limited to the coolant recirculation and steam outlets) in order to focus the analysis of emergent fault behavior that could not have been identified with previously published versions of the FFIP framework.

Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 5

Behavioral model of the white liquor tank component

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Figure 6

A flowchart describing how every combination of parameter values is simulated systematically to determine those combinations of parameter values that result in degradation or loss of functions

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Figure 7

The result of performing the procedure in Fig. 6 when parameter 1 is RefInputLiquidFlow and parameter 2 is leakSize.

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Figure 8

Top level CFG model for boiling water reactor core and its steam outlets

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Figure 9

Internals of the reactor component in Fig. 8

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Figure 10

Behavioral logic for the PressureControlValve component in Fig. 8

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Figure 11

Stateflow chart of the FFL reasoner for the “transmit thermal energy” function in Table 2

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Figure 12

User interface for specifying a set of FFIP simulation scenarios

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Figure 13

Algorithm for generating the set of parameterized FFIP simulations

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Figure 14

Temperature of fuel rods in first phase FFIP simulation scenarios with parameter values resulting in healthy FFL verdicts

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Figure 15

Temperature of fuel rods in first phase FFIP simulation scenarios with parameter values resulting in degraded FFL verdicts

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Figure 16

Temperature of fuel rods in first phase FFIP simulation scenarios with parameter values resulting in lost FFL verdicts

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Figure 17

Temperature of fuel rods in second phase FFIP simulation: emergency power present

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Figure 18

Temperature of fuel rods in third phase FFIP simulation scenarios with narrowed ranges for parameter values

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Figure 1

Piping and instrumentation diagram of the example process

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Figure 2

Functional model of the example process

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Figure 3

Configuration flow graph of the example process

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Figure 4

Function failure logic for the supply liquid material (white liquor) function

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