Developing a method for time-variant reliability assessment of passive heat removal systems in nuclear power plants

Ebrahimian, M.; Pirouzmand, Ahmad; Rabiee, Ataollah

doi:10.1016/j.anucene.2021.108365

Cited by 7 publications

(3 citation statements)

References 23 publications

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Section: Introductionmentioning

confidence: 99%

“…Reliability analysis aims to estimate probability of failure under various uncertainties. At present, reliability analysis has a wide range of applications in fields such as nuclear power systems, 7 mechanical systems, 8 aerospace, 9 and industrial robots. 10 To accurately estimate probability of failure, many effective approaches have been developed, including approximate analytical method, digital simulation method and surrogate model method.…”

Section: Introductionmentioning

confidence: 99%

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High dimensional reliability analysis based on combinations of adaptive Kriging and dimension reduction technique

Xiao

Zhan

2022

Quality & Reliability Eng

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High dimensional reliability analysis is unavoidable if a structural system involving stochastic process because Karhunen–Loève (K‐L) expansion is commonly used. Reliability analysis for structural systems with computationally intensive numerical models and high dimensions is challenging. In this study, an effective high dimensional reliability analysis method is proposed based on principal components and active subspace (PCAS) and active Kriging, and is termed as PCAS‐AK. The proposed method can address two shortcomings existing in PCAS: the latter select training samples randomly and cannot deal with parallel processing. The proposed method, however, selects training samples adaptively, subsequently, the training samples that have high contribution to probability of failure are selected, which can improve computational efficiency. To reduce overall computational time and allow parallelization, K‐weighted‐means combined with learning function are used to select multiple samples at each iteration. After the dimension reduction of both input and output spaces, the samples selected by adaptive method are used to construct Kriging model in the latent low‐dimensional space. Three examples are used to demonstrate the applicability and accuracy of the proposed method. Results show that the proposed method can deal with parallel processing and is, generally, more effective than PCAS for high dimensional reliability problems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High dimensional reliability analysis based on combinations of adaptive Kriging and dimension reduction technique

Xiao

Zhan

2022

Quality & Reliability Eng

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show abstract

“…The accuracy of the PHI2 method greatly depends on the time step size, thus Sudret [33] proposed the improved PHI2 method to reduce the impact of time step size. Ebrahimian et al [7] developed a PHI2 based algorithm for evaluating the time-variant reliability of the passive heat removal system. Instead of using the outcrossing rate, Quezada del Villar et al [28] estimated the distribution of the first passage time in the outcrossing events to obtain the time-variant reliability.…”

Section: Introductionmentioning

confidence: 99%

An adaptive PC-Kriging method for time-variant structural reliability analysis

Nan¹,

Li²,

Song³

2022

Eksploatacja i Niezawodność – Maintenance and Reliability

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The practical application of time-variant reliability analysis is limited by its computationally expensive models which describe the structural system behavior. This paper presents a new adaptive PC-Kriging (APCK) approach to accurately and efficiently assess the time-variant reliabilities. Time interval is firstly discretized with a series of time instants and then the stochastic process is reconstructed by standard normal random variables and deterministic function of time. PC-Kriging (PCK) models are built at each time instant to predict the instantaneous responses of performance function. To improve the accuracy and efficiency, a new update strategy based on the integration of U- and H- learning functions is developed to refine the PCK models of instantaneous responses. One or two best samples are identified by the proposed learning criterion for updating the PCK models. Finally, Monte Carlo simulation (MCS) is used to estimate the time-variant reliability based on the updated PCK models. Four examples are used to validate the accuracy and efficiency of the proposed method.

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Annual availability assessment of a gas turbine power plant using Monte Carlo simulation based on fuzzy logic and an adaptive neuro-fuzzy repair time prediction system

Mirzaei

Behbahaninia

Abdalisousan

et al. 2023

J Therm Anal Calorim

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Developing a method for time-variant reliability assessment of passive heat removal systems in nuclear power plants

Cited by 7 publications

References 23 publications

High dimensional reliability analysis based on combinations of adaptive Kriging and dimension reduction technique

High dimensional reliability analysis based on combinations of adaptive Kriging and dimension reduction technique

An adaptive PC-Kriging method for time-variant structural reliability analysis

Annual availability assessment of a gas turbine power plant using Monte Carlo simulation based on fuzzy logic and an adaptive neuro-fuzzy repair time prediction system

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