MCDET: A Probabilistic Dynamics Method Combining Monte Carlo Simulation with the Discrete Dynamic Event Tree Approach

Kloos, Martina; Peschke, J.

doi:10.13182/nse06-a2601

Cited by 53 publications

(30 citation statements)

References 6 publications

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“…In this context, DET is used as the logical modeling technique to derive, by means of simulation, the scenarios that can arise in the life evolution of a dynamic system as the result of a sequence of successes and failures of different components and functions. (8,29) Much software for DET analyses, such as DYLAM, (10) ADS, (11) and MCDET, (14) is available and, in principle, all the possible (accident) scenarios could be extracted, especially by recurring to massive parallel computing (30) or to backtracking techniques. (8) However, not all the possible different time sequences within a given scenario can be explored, by reason of the extremely high computational cost needed for simulating all of them.…”

Section: Problem Definitionmentioning

confidence: 99%

“…In this line of thought, works on dynamic event trees (DETs) have highlighted that the end states reached by a system as a result of an accident scenario do not depend only on the order of occurrence of the events in the sequence, but also on the exact time at which these events occur and on their magnitude . However, the introduction of the time and magnitude dimensions into the analysis makes the size of the system state‐space theoretically infinite and, thus, impossible to be explored completely.…”

Section: Introductionmentioning

confidence: 99%

“…(4)(5)(6) In this context, the combination of event trees (ETs) (representing the logic of the system) and mathematical models of the system dynamics (describing the dynamics of the physical phenomena involved) has been largely advocated as the way for determining the end states (ESs) that can be reached by the system and deriving the corresponding causality relations among the events. (7)(8)(9) In this line of thought, works on dynamic event trees (DETs) (10)(11)(12)(13)(14)(15) have highlighted that the end states reached by a system as a result of an accident scenario do not depend only on the order of occurrence of the events in the sequence, but also on the exact time at which these events occur and on their magnitude. (8,9,(16)(17)(18) However, the introduction of the time and magnitude dimensions into the analysis makes the size of the system state-space theoretically infinite and, thus, impossible to be explored completely.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems

2016

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The end states reached by an engineered system during an accident scenario depend not only on the sequences of the events composing the scenario, but also on their timing and magnitudes. Including these additional features within an overarching framework can render the analysis infeasible in practical cases, due to the high dimension of the system state-space and the computational effort correspondingly needed to explore the possible system evolutions in search of the interesting (and very rare) ones of failure. To tackle this hurdle, in this article we introduce a framework for efficiently probing the space of event sequences of a dynamic system by means of a guided Monte Carlo simulation. Such framework is semi-automatic and allows embedding the analyst's prior knowledge about the system and his/her objectives of analysis. Specifically, the framework allows adaptively and intelligently allocating the simulation efforts preferably on those sequences leading to outcomes of interest for the objectives of the analysis, e.g., typically those that are more safety-critical (and/or rare). The emerging diversification in the filling of the state-space by the preference-guided exploration allows also the retrieval of critical system features, which can be useful to analysts and designers for taking appropriate means of prevention and mitigation of dangerous and/or unexpected consequences. A dynamic system for gas transmission is considered as a case study to demonstrate the application of the method.

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Section: Problem Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems

2016

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“…In turn, probabilistic dynamics methods can potentially cover the spectrum of event sequences and achieve a realistic probabilistic safety assessment [1][2][3][4][5]. Moreover, the application of these methods generally reduces the analyst-to-analyst variability, because it requires less expert judgement.…”

Section: Introductionmentioning

confidence: 99%

“…The method MCDET (Monte Carlo dynamic event tree) which was developed at GRS is a combination of Monte Carlo (MC) simulation and the discrete dynamic event tree (DDET) method [5]. MCDET was implemented as a module that can operate in tandem with any deterministic dynamics code.…”

Section: Introductionmentioning

confidence: 99%

Consideration of human actions in combination with the probabilistic dynamics method Monte Carlo dynamic event tree

Kloos

Peschke

2008

Proceedings of the Institution of Mechanical Engineers, Part O:

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The various accident sequences to be considered in the framework of a probabilistic safety analysis (PSA) for a nuclear power plant derive from interactions between the physical process, technical system functions, operator performance and stochastic influences along the time axis. Probabilistic dynamics methods have been developed to adequately account for these interactions. They can potentially cover the spectrum of event sequences which may actually evolve and achieve a realistic probabilistic safety assessment. The probabilistic dynamics method MCDET is a combination of Monte Carlo simulation and the discrete dynamic event tree (DDET) method. It was implemented as a module which can operate in tandem with any deterministic code simulating the system and process dynamics. MCDET was supplemented by a so-called Crew-Module which permits to simulate the dynamics of human actions depending on but also acting on the system and process dynamics as modelled in the deterministic code and on stochastic influences as modelled in MCDET. This paper presents the Crew-Module and gives an overview of the results which may be obtained from its combination with MCDET and a deterministic dynamics code.

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Knowledge‐driven System Simulation for Scenario Analysis in Risk Assessment

Turati

Pedroni

Zio

2017

Knowledge in Risk Assessment and Management

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MCDET: A Probabilistic Dynamics Method Combining Monte Carlo Simulation with the Discrete Dynamic Event Tree Approach

Cited by 53 publications

References 6 publications

An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems

An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems

Consideration of human actions in combination with the probabilistic dynamics method Monte Carlo dynamic event tree

Knowledge‐driven System Simulation for Scenario Analysis in Risk Assessment

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