Reliability evaluation of a natural circulation system

Monte Carlo simulation-based sensitivity analysis of the model of a thermal–hydraulic passive system

“…In addition, the shape of the distributions P(F|x j ), j = 1, 2, 3, obviously reflects the "structure" of the four failure regions {F l , l = 1, 2, 3, 4} (22)- (25): for example, the distribution P(F|x 1 ) takes values different from zero when x 1 approximately ranges between +1 and +2 (see (22)- (25)); the distribution P(F|x 2 ) takes values different from zero when x 2 approximately ranges between -2 and -1 (see (22) and (25)) or between +1 and +2 (see (23) and (24)); finally, the distribution P(F|x 3 ) takes values different from zero when x 3 assumes values around -3 (see (22) and (23)) or +3 (see (24) and (25)). …”

“…Due to these uncertainties, the physical phenomena involved in the passive system functioning (e.g., natural circulation) might develop in such a way to lead the system to fail its function (e.g., decay heat removal): actually, deviations in the natural forces and in the conditions of the underlying physical principles from the expected ones can impair the function of the system itself [9]- [21]. In the analysis of such functional failure behavior [10], the passive system is modeled by a mechanistic Thermal-Hydraulic (T-H) code and the probability of failing to perform the required function is estimated based on a Monte Carlo (MC) sample of code runs which propagate the uncertainties in the model and numerical values of its parameters/variables [22]- [38].…”

A review: passive system reliability analysis – accomplishments and unresolved issues

“…In University of Pisa (UNIPI), D'Auria and Galassi (2000) studied it further and a few years later, this methodology was proposed as reliability evaluation of passive safety system (REPAS). REPAS (Jafari et al, 2003) methodology was a joint effort of UNIPI, ENEA, University of Rome, and Polytechnic of Milan. In REPAS, failure probability of passive system was evaluated by propagating the epistemic uncertainties of important physical and geometric parameters, which affects the system performance the most.…”

“…In REPAS, the uncertainties in code predictions are evaluated by performing sensitivity study of input parameters and by code to code comparisons. Jafari et al (2003) applied this methodology to an experimental natural circulation test loop. Zio et al (2003) used REPAS for reliability analysis of an ICS.…”

Quantitative functional failure analysis of a thermal–hydraulic passive system by means of bootstrapped Artificial Neural Networks

“…In this view, a passive system fails to perform its function due to deviations from its expected behavior which lead the load imposed on the system (e.g., the peak value of the fuel cladding temperature during a LOCA transient) to overcome its capacity (e.g., a threshold value imposed by regulating authorities or by the mechanical properties of structural materials). This concept is at the basis of many works of literature, including (Jafari et al, 2003;Marquès et al, 2005;Pagani et al, 2005;Bassi and Marquès, 2008;Mackay et al, 2008;Mathews et al, 2008 andPatalano et al, 2008;Fong et al, 2009;Zio and Pedroni, 2009a and b); in these works, the passive system is modeled by a detailed, mechanistic T-H system code and the probability of not performing the required function is estimated based on a Monte Carlo (MC) sample of code runs which propagate the epistemic (state-of-knowledge) uncertainties in the model describing the system and the numerical values of its parameters/variables; because of these uncertainties, the system may not accomplish its mission, even if no hardware failure occurs.…”