An approach to address probabilistic assumptions on the availability of safety systems for deterministic safety analysis

[EN] There is an attempt nowadays to provide a more comprehensive and realistic safety assessment of design and operation of Nuclear Power Plants. In this context, innovative approaches are being proposed for safety assessment of nuclear power plants design including both design basis conditions and design extension conditions. An area of research aims at developing methods for combining insights from probabilistic and deterministic safety analyses in Option 4, also called realistic approach, from the International Atomic Energy Agency specific safety guide. The development of Option 4 or realistic approach involves the adoption of best estimate computer codes, best estimate assumptions on systems availability and best estimate of initial and boundary conditions for the safety analysis. This paper focusses on providing the fundamentals and practical implementation of an approach to integrate PSA-based probabilistic models and data, which incorporate best estimate assumptions on the availability of safety systems, into Option 4. It is presented a practical approach to identify relevant, i.e. most probable, configurations of safety systems and to assess the associated occurrence probability of each configuration using PSA models and data of a NPP, which is based on the use of a Pure Monte Carlo method. An example of application is provided to demonstrate how this approach performs. The case study focusses on an accident scenario corresponding to the initiating event Loss Of Feed Water (LOFW) for a typical three-loops Pressurized Water Reactor (PWR) NPP.Authors are grateful to the Spanish CSN (Consejo de Seguridad Nuclear) for the financial support of this research (Research Project SIN/4078/2013/640; MASA Project).Martorell Alsina, SS.; Martorell-Aygues, P.; Martón Lluch, I.; Sánchez Galdón, AI.; Carlos Alberola, S. (2017). An approach to address probabilistic assumptions on the availability of safety systems for deterministic safety analysis. Reliability Engineering & System Safety. 160:136-150. https://doi.org/10.1016/j.ress.2016.12.009S13615016

A METHODOLOGY FOR TRUCK ALLOCATION PROBLEMS CONSIDERING DYNAMIC CIRCUMSTANCES IN OPEN PIT MINES, CASE STUDY OF THE SUNGUN COPPER MINE

Problem raspodjele kamiona smatra se jednim od najvažnijih čimbenika u postizanju planiranih proizvodnih kapaciteta u rudarstvu. Tradicionalne tehnike raspodjele kamiona (npr. matematičko programiranje, teorije čekanja u redu) podliježu različitim razinama pojednostavljenja u formuliranju stvarnoga prijevoza u heterogenim okolnostima. U ovome radu analiziran je problem raspodjele kamiona razvojem metode za optimizaciju raspodjele kamiona koja se temelji na simulaciji optimizacije (SBO) s obzirom na nesigurnosti tijekom rada kamionskoga voznog parka. Metoda osigurava integriranu strukturu simultanom kombinacijom optimizacije i simulacije stohastičkih diskretnih događaja. Ciljna je funkcija minimiziranje ukupnoga broja kamiona za transport sa simulacijom diskretnih događaja korištenih za modeliranje rubnih uvjeta. U ovome radu istražen je rad voznoga parka na primjeru rudnika bakra Sungun kako bi se postigla optimalna raspodjela kamiona pri različitim radnim operacijama na eksploatacijskome polju rudnika. Pojedinosti rada procijenjene su na temelju različitih pokazatelja kao što su iskorištenje, vrijeme čekanja i količina transportiranoga materijala za svaku radnu operaciju. K onačno, uska grla operacija prepoznata su za svaku situaciju.Truck allocation problems are considered as one of the most substantial factors in the achievement of planned production capacity in the mining industry. Traditional truck allocation techniques (e.g. mathematical programming, queueing theories) have undergone different levels of simplifications in formulating actual haulage operations under heterogeneous circumstances. In this study, the truck allocation problem is analysed through the development of the simulation-based optimization (SBO) method for the optimization of truck assignment considering uncertainties during fleet operation. This method provides an integrated structure by the simultaneous combination of optimization and stochastic discrete-event simulation. The objective function is to minimize the total number of trucks for haulage operation with discrete-event simulation employed to model the constraints. As a case study, the fleet operation of the Sungun copper mine is investigated to accomplish an optimal truck allocation for various working benches in the mine site. Operation details are evaluated through different indicators such as utilization, waiting times, and the amount of transported materials for each working bench. Finally, the operation bottlenecks are recognized for each situation

Conference Proceedings Report: ASME-SERAD and UMD-CRR Interactive seminar & pre-workshop on the intersection of PRA and PHM

Pre-workshop held October 2, 2020 via web.This event is the first in a two-part series exploring the intersection of PRA and PHM in the context of complex engineering systems. Initially the workshop was planned as a fully in-person workshop to be held in April, 2020, but as with many events in 2020, it was postponed due to the travel restrictions resulting from COVID-19 pandemic. The organizers recognized that the online format isn't amenable to the deep discussions which were intended to be at the heart of the in person workshop, but we decided to try an experiment: to see if we could make a “pre-workshop” as interactive possible in an era of webinar fatigue. Thus the workshop was reimagined as an online, interactive pre-workshop in 2020, to be followed with the in person, discussion-heavy workshop to be held when we are able to travel again in 2021.University of Maryland, Center for Risk and Reliability; ASME, Safety Engineering and Risk Analysis Divisio

A Bayesian ensemble of sensitivity measures for severe accident modeling

International audienceIn this work, a sensitivity analysis framework is presented to identify the relevant input variables of a severe accident code, based on an incremental Bayesian ensemble updating method. The proposed methodology entails: i) the propagation of the uncertainty in the input variables through the severe accident code; ii) the collection of bootstrap replicates of the input and output of limited number of simulations for building a set of Finite Mixture Models (FMMs) for approximating the probability density function (pdf) of the severe accident code output of the replicates; iii) for each FMM, the calculation of an ensemble of sensitivity measures (i.e., input saliency, Hellinger distance and Kullback–Leibler divergence) and the updating when a new piece of evidence arrives, by a Bayesian scheme, based on the Bradley-Terry model for ranking the most relevant input model variables. An application is given with respect to a limited number of simulations of a MELCOR severe accident model describing the fission products release in the LP-FP-2 experiment of the Loss Of Fluid Test (LOFT) facility, which is a scaled-down facility of a pressurized water reactor (PWR)