Simulation-based exploration of high-dimensional system models for identifying unexpected events

Abstract

. Simulation-based exploration of highdimensional system models for identifying unexpected events. Reliability Engineering and System Safety.http://dx.doi.org/10.1016/j.ress.2017.04.004Simulation-based exploration of high-dimensional system models for identifying unexpected events AbstractMathematical numerical models are increasingly employed to simulate system behavior and identify sequences of events or configurations of the system's design and operational parameters that can lead the system to extreme conditions (Critical Region, CR). However, when a numerical model is: i) computationally expensive, ii) high-dimensional, and iii) complex, these tasks become challenging.In this paper, we propose an adaptive framework for efficiently tackling this problem: i) a dimensionality reduction technique is employed for identifying the factors and variables that most affect the system behavior; ii) a meta-model is sequentially trained to replace the computationally expensive model with a computationally cheap one; iii) an adaptive exploration algorithm based on Markov Chain Monte Carlo is introduced for exploring the system state space using the meta-model; iv) clustering and other techniques for the visualization of high dimensional data (e.g., parallel coordinates plot) are employed to summarize the retrieved information.The method is employed to explore a power network model involving 20 inputs. The CRs are properly identified with a limited computational cost, compared to another exploration technique of literature (i.e., Latin Hypercube Sampling).

Knowledge‐driven System Simulation for Scenario Analysis in Risk Assessment

“…The driving idea of the proposed framework is to iteratively: i) run a (possibly small) number of model simulations, ii) retrieve knowledge from the available simulations and iii) guide the selection of new configurations towards the regions of interest (Turati, Pedroni, & Zio, 2016b). The framework is characterized by four principal steps (see Figure 9).…”

“…An algorithm based on the MCMC M-H algorithm has been designed. Although we refer the reader to the corresponding paper (Turati et al, 2016b), we list here the main ideas. The iterative algorithm, at each step, firstly identifies the number of CRs already discovered using clustering techniques; then, several Markov Chains are distributed among the CRs in order to guarantee that each CR has been explored with the same meticulousness.…”

Integrated deterministic and probabilistic safety assessment of a superconducting magnet cryogenic cooling circuit for nuclear fusion applications

“…Finally, it is worth mentioning that other methods can be used to efficiently generate accident scenarios for the analysis of complex systems, containing a large number of components. In those cases, intelligent sampling techniques could be adopted to preferentially guide the exploration of the (large) system state-space towards the critical regions of interest (i.e., abnormal scenarios), making the best use of the information and knowledge gained at previous steps and iterations of the search (see the adaptive framework proposed by (Turati et al, 2017) as an example).…”

A Monte Carlo-based exploration framework for identifying components vulnerable to cyber threats in nuclear power plants

“…Modeling and simulation can be used to explore and understand the behavior of a system, under different, possibly uncertain conditions, including hazardous ones (Turati et al, 2017a;Turati et al, 2017b). Design-Of-Experiment (DOE) approaches have been proposed to study different operating conditions, in order to analyze the corresponding system responses with respect to specified performance criteria: safety, reliability, resilience, business continuity, etc.…”