José F. Villanueva

RAMS+C informed decision-making with application to multi-objective optimization of technical specifications and maintenance using genetic algorithms

Abstract The role of technical specifications and maintenance (TSM) activities at nuclear power plants (NPP) aims to increase reliability, availability and maintainability (RAM) of Safety-Related Equipment, which, in turn, must yield to an improved level of plant safety. However, more resources (e.g. costs, task force, etc.) have to be assigned in above areas to achieve better scores in reliability, availability, maintainability and safety (RAMS). Current situation at NPP shows different programs implemented at the plant that aim to the improvement of particular TSM-related parameters where the decision-making process is based on the assessment of the impact of the change proposed on a subgroup of RAMS+C attributes. This paper briefly reviews the role of TSM and two main groups of improvement programs at NPP, which suggest the convenience of considering the approach proposed in this paper for the Integrated Multi-Criteria Decision-Making on changes to TSM-related parameters based on RAMS+C criteria as a whole, as it can be seem as a decision-making process more consistent with the role and synergic effects of TSM and the objectives and goals of current improvement programs at NPP. The case of application to the Emergency Diesel Generator system demonstrates the viability and significance of the proposed approach for the Multi-objective Optimization of TSM-related parameters using a Genetic Algorithm.

Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization

Optimization of testing and maintenance activities performed in the different systems of a complex industrial plant is of great interest as the plant availability and economy strongly depend on the maintenance activities planned. Traditionally, two types of models, i.e. deterministic and probabilistic, have been considered to simulate the impact of testing and maintenance activities on equipment unavailability and the cost involved. Both models present uncertainties that are often categorized as either aleatory or epistemic uncertainties. The second group applies when there is limited knowledge on the proper model to represent a problem, and/or the values associated to the model parameters, so the results of the calculation performed with them incorporate uncertainty. This paper addresses the problem of testing and maintenance optimization based on unavailability and cost criteria and considering epistemic uncertainty in the imperfect maintenance modelling. It is framed as a multiple criteria decision making problem where unavailability and cost act as uncertain and conflicting decision criteria. A tolerance interval based approach is used to address uncertainty with regard to effectiveness parameter and imperfect maintenance model embedded within a multiple-objective genetic algorithm. A case of application for a stand-by safety related system of a nuclear power plant is presented. The results obtained in this application show the importance of considering uncertainties in the modelling of imperfect maintenance, as the optimal solutions found are associated with a large uncertainty that influences the final decision making depending on, for example, if the decision maker is risk averse or risk neutral.

Use of multiple objective evolutionary algorithms in optimizing surveillance requirements

This paper presents the development and application of a double-loop Multiple Objective Evolutionary Algorithm that uses a Multiple Objective Genetic Algorithm to perform the simultaneous optimization of periodic Test Intervals (TI) and Test Planning (TP). It takes into account the time-dependent effect of TP performed on stand-by safety-related equipment. TI and TP are part of the Surveillance Requirements within Technical Specifications at Nuclear Power Plants. It addresses the problem of multi-objective optimization in the space of dependable variables, i.e. TI and TP, using a novel flexible structure of the optimization algorithm. Lessons learnt from the cases of application of the methodology to optimize TI and TP for the High-Pressure Injection System are given. The results show that the double-loop Multiple Objective Evolutionary Algorithm is able to find the Pareto set of solutions that represents a surface of non-dominated solutions that satisfy all the constraints imposed on the objective functions and decision variables. Decision makers can adopt then the best solution found depending on their particular preference, e.g. minimum cost, minimum unavailability.

Genetic algorithm-based optimization of testing and maintenance under uncertain unavailability and cost estimation: A survey of strategies for harmonizing evolution and accuracy

Abstract This paper presents the results of a survey to show the applicability of an approach based on a combination of distribution-free tolerance interval and genetic algorithms for testing and maintenance optimization of safety-related systems based on unavailability and cost estimation acting as uncertain decision criteria. Several strategies have been checked using a combination of Monte Carlo (simulation)––genetic algorithm (search-evolution). Tolerance intervals for the unavailability and cost estimation are obtained to be used by the genetic algorithms. Both single- and multiple-objective genetic algorithms are used. In general, it is shown that the approach is a robust, fast and powerful tool that performs very favorably in the face of noise in the output (i.e. uncertainty) and it is able to find the optimum over a complicated, high-dimensional nonlinear space in a tiny fraction of the time required for enumeration of the decision space. This approach reduces the computational effort by means of providing appropriate balance between accuracy of simulation and evolution; however, negative effects are also shown when a not well-balanced accuracy–evolution couple is used, which can be avoided or mitigated with the use of a single-objective genetic algorithm or the use of a multiple-objective genetic algorithm with additional statistical information.

Evaluation of risk impact of changes to surveillance requirements addressing model and parameter uncertainties

This paper presents a three steps based approach for the evaluation of risk impact of changes to Surveillance Requirements based on the use of the Probabilistic Risk Assessment and addressing identification, treatment and analysis of model and parameter uncertainties in an integrated manner. The paper includes also an example of application that focuses on the evaluation of the risk impact of a Surveillance Frequency change for the Reactor Protection System of a Nuclear Power Plant using a level 1 Probabilistic Risk Assessment. Surveillance Requirements are part of Technical Specifications that are included into the Licensing Basis for operation of Nuclear Power Plants. Surveillance Requirements aim at limiting risk of undetected downtimes of safety related equipment by imposing equipment operability checks, which consist of testing of equipment operational parameters with established Surveillance Frequency and Test Strategy.

Particle Swarm Optimization of safety components and systems of nuclear power plants under uncertain maintenance planning

Maintenance planning is a subject of concern to many industrial sectors as plant safety and business depend on it, and can be formulated in terms of a multi-objective optimization problem where reliability, availability, maintainability and cost act as decision criteria and surveillance test and maintenance strategies act as decision variables. Usually, the frequency of performing a maintenance task is considered, in the optimization process, as a constant value but a certain range of variation from such value is observed in real practice. Thus, to obtain a more realistic approach, a certain degree of uncertainty should be considered in the decision variables. This paper presents two examples of maintenance optimization using Particle Swarm as optimization technique and a tolerance interval based approach to address uncertainty, one is focused on a safety component and the other considers a nuclear power plant safety system.

Current trends in Risk-Informed changes to Limiting Conditions for Operation

Among the applications of the Probabilistic Safety Analysis (PSA) for analysing safety related issues at Nuclear Power Plants (NPP) one can find the use of the PSA to support the risk-informed analysis of changes to Technical Specification (TS) as a very important application area. Thus, the early 80’s saw the development of methodologies based on probabilistic methods to analysis the impact on the risk level of the changes to the Allowed Outage Times (AOT) and Surveillance Test intervals (STI), which are included in the Limiting Conditions for Operation (LCO) and in the Surveillance Requirements (SR) respectively, which, in turn, are part of the plant Technical Specifications (TS). As probabilistic information provided by the PSA was the first to be considered, these applications were referred to as risk-based analysis of changes to AOT and STI in TS (RBTS). The middle 90’s saw the growth of a new trend towards the addition of deterministic information to PSA applications to support the integrated (deterministic and probabilistic) analysis of the changes to both requirements, yielding to the use of the PSA in the so called risk-informed analysis of changes to AOT and STI in TS (RITS) [1, 2].

Uncertainty analysis of a large break loss of coolant accident in a pressurized water reactor using non-parametric methods

Abstract The safety analysis of nuclear power plant is moving toward a realistic approach in which the simulations performed using best estimate computer codes must be accompanied by an uncertainty analysis, known as the Best Estimate Plus Uncertainties approach. The most popular statistical method used in these analyses is the Wilks’ method, which is based on the principle of order statistics for determining a certain coverage of the Figures-of-Merit with an appropriate degree of confidence. However, there exist other statistical techniques that could provide similar or even better results. This paper explores the performance of alternative non-parametric methods as compared to the Wilks’ method of obtaining such Figure-of-Merits tolerance intervals. Three methods are investigated, i.e. Hutson and Beran–Hall methods and a bootstrap method. All the techniques have been used to perform the uncertainty analysis of a Large-Break Loss of Coolant Accident. The Figure-of-Merit of interest in this application is the maximum value reached by the Peaking Clad Temperature. In order to analyze the results obtained by the different methods, four performance metrics are proposed to measure the coverage, dispersion, conservativeness, and robustness of the tolerance intervals.

An extended BEPU approach integrating probabilistic assumptions on the availability of safety systems in deterministic safety analyses

Abstract The International Atomic Energy Agency (IAEA) produced guidance on the use of Deterministic Safety Analysis (DSA) for the design and licensing of Nuclear Power Plants (NPPs) in “DSA for NPP Specific Safety Guide, No. SSG-2”, which proposes four options for the application of DSA. Option 3 involves the use of Best Estimate codes and data together with an evaluation of the uncertainties, the so called BEPU methodology. Several BEPU approaches have been developed in scopes that are accepted by the regulator authorities nowadays. They normally adopt conservative assumptions on the availability of safety systems. Option 4 goes beyond by pursuing the incorporation of realistic assumption on the availability of safety systems into the DSA. This paper proposes an Extended BEPU (EBEPU) approach that integrates insights from probabilistic Safety Analysis into a typical BEPU approach. There is an aim at combining the use of well-established BEPU methods and realistic (“probabilistic”) assumptions on safety system availability. This paper presents the fundamentals of the EBEPU approach and the main results obtained for an example of application that focuses on an accident scenario corresponding to the initiating event “Loss of Feed Water (LOFW)” for a typical three-loops Pressurized Water Reactor (PWR) NPP.

RELAP5 Simulation of PKL Facility Experiments under Midloop Conditions

Nuclear power plant risk has to be quantified in full power and in other modes of operation. This latter situation corresponds to low power and shutdown modes of operation in which the residual heat removal (RHR) system is required to extract the heat generated in the core. These accidental sequences are great contributors to the total plant risk. Thus, it is important to analyze the plant behavior to establish the accident mitigation measures required. In this way, PKL facility experimental series were undertaken to analyze the plant behavior in other modes of operation when the RHR is lost. In these experiments, the plant configurations were changed to analyze the influence of steam generators secondary side configurations, the temperature inside the pressurizer, and the inventory level on the plant behavior. Moreover, different accident management measures were proposed in each experiment to reach the conditions to restart the RHR. To understand the physical phenomena that takes place inside the reactor, the experiments are simulated with thermal-hydraulic codes, and this makes it possible to analyze the code capabilities to predict the plant behavior. This work presents the simulation results of four experiments included in PKL experimental series obtained using RELAP5/Mod3.3.