A survey of dynamic methodologies for probabilistic safety assessment of nuclear power plants

Science and Technology of Nuclear Installations

2017

Integrated Deterministic and Probabilistic Safety Analysis (IDPSA) of dynamic systems calls for the development of efficient methods for accidental scenarios generation. The necessary consideration of failure events timing and sequencing along the scenarios requires the number of scenarios to be generated to increase with respect to conventional PSA. Consequently, their postprocessing for retrieving safety relevant information regarding the system behavior is challenged because of the large amount of generated scenarios that makes the computational cost for scenario postprocessing enormous and the retrieved information difficult to interpret. In the context of IDPSA, the interpretation consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs), and Prime Implicants (PIs). To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self-Organizing Maps (SSSOMs) whose outcomes are combined by a locally weighted aggregation according to two strategies: a locally weighted aggregation and a decision tree based aggregation. In the former, we resort to the Local Fusion (LF) principle for accounting the classification reliability of the different SSSOM classifiers, whereas in the latter we build a classification scheme to select the appropriate classifier (or ensemble of classifiers), for the type of scenario to be classified. The two strategies are applied for the postprocessing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG).

Section: Design Of the Aggregation Mechanismmentioning

confidence: 99%

“…This can make the computational cost for scenario postprocessing enormous and the retrieved information difficult to interpret [1][2][3][4]. The main goal of postprocessing is the classification of the dynamic scenarios generated as safe, failed, Near Misses (NM), and Prime Implicants (PIs) clusters.…”

Section: Introductionmentioning

confidence: 99%

Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps

Maio

Rossetti

Science and Technology of Nuclear Installations

2017

“…In this respect, a Dynamic probabilistic Safety Margin (DSM) approach is proposed in this paper, based on time-dependent phenomenological models of stochastic system evolution including possible dependencies between failure events [Aldemir, 2013]. For this, we introduce a novel definition of a DSM by the combined quantification of a percentile (e.g., 95 th ) of the safety parameter distribution (e.g., oil temperature, peak cladding temperature) and a percentile (e.g., 5 th ) of the distribution of the earliest time required to the safety parameter to reach the given percentile value.…”

Section: Notation and List Of Acronymsmentioning

confidence: 99%

“…Dynamic reliability approaches [Siu, 1994;Devooght, 1997;Marseguerra et al, 1998;Labeau et al, 2000;Dufour et al, 2002;Di Maio et al, 2009;Aldemir, 2013] [Aldemir, 2013;Zio, 2014] and as a by-product for the quantification of operational safety margins within a dynamic reliability scheme [Zio et al, 2012].…”

Section: Notation and List Of Acronymsmentioning

confidence: 99%

A dynamic probabilistic safety margin characterization approach in support of Integrated Deterministic and Probabilistic Safety Analysis

Maio

Rai

Reliability Engineering & System Safety

2016

“…Some approaches have been recently proposed in the nuclear community, which make use of sophisticated softwares that combine methodologies for risk assessment with very accurate thermohydraulic codes (Alfonsi et al, 2015;Alfonsi et al, 2016;Izquierdo et al, 2009), to perform the so-called Integrated Deterministic Probabilistic Safety Assessment (IDPSA) (Aldemir, 2013;Zio, 2014) or…”

Section: Introductionmentioning

confidence: 99%

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

Turati

Pedroni

Reliability Engineering & System Safety

2017

. 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).