A Review of Ultrasonic Reflectometry for the Physical Characterization of Lubricated Tribological Contacts: History,Methods, Devices,and Technological Trends

Tribology Letters - The tribological behavior of a nickel-based single crystal superalloy treated by shot-peening has been investigated. Friction tests under three normal loads and three...     

A fuzzy data envelopment analysis approach for FMEA

We present a data envelopment analysis approach for determining ranking indices among failure modes in which the typical FMEA parameters are modeled as fuzzy sets. By this approach, inference rules of the IF THEN kind can be bypassed. The proposed approach is applied to a typical PWR auxiliary feedwater system. The results are compared to those obtained by means of: the risk priority numbers, pure fuzzy logic concepts, and finally the DEA-APGF (profiling of severity efficiency) approach. The results demonstrate the potential of the combination of fuzzy logic concepts and data envelopment analysis for this class of problems.     

Multiprocessor modeling of parallel Particle Swarm Optimization applied to nuclear engineering problems

Particle Swarm Optimization (PSO) is a population-based metaheuristic (PBM), in which solution candidates evolve through simulation of a simplified social adaptation model. Putting together robustness, efficiency and simplicity, PSO has gained great popularity. Many successful applications of PSO are reported, in which PSO demonstrated to have advantages over other well-established PBM. However, computational costs are still a great constraint for PSO, as well as for all other PBMs, especially in optimization problems with time consuming objective functions. To overcome such difficulty, parallel computation has been used. The default advantage of parallel PSO (PPSO) is the reduction of computational time. Master-slave approaches, exploring this characteristic are the most investigated. However, much more should be expected. It is known that PSO may be improved by more elaborated neighborhood topologies. Hence, in this work, we develop several different PPSO algorithms exploring the advantages of enhanced neighborhood topologies implemented by communication strategies in multiprocessor architectures. The proposed PPSOs have been applied to two complex and time consuming nuclear engineering problems: i) reactor core design (CD) and ii) fuel reload (FR) optimization. After exhaustive experiments, it has been concluded that: i) PPSO still improves solutions after many thousands of iterations, making prohibitive the efficient use of serial (non-parallel) PSO in such kind of real-world problems and ii) PPSO with more elaborated communication strategies demonstrated to be more efficient and robust than the master-slave model. Advantages and peculiarities of each model are carefully discussed in this work.     

A Real-Time Artificially Intelligent Monitoring System for Nuclear Power Plants Operators Support

In this paper, we describe the artificially intelligent monitoring system (AIMS), a framework for power plants real-time monitoring systems (RT/MS), developed at Federal University of Rio de Janeiro (COPPE/UFRJ) and applied to the Brazilians Angra-1 and Angra-2 nuclear power plants. The kernel of AIMS is an object-oriented knowledge-base system, in which acquired and calculated variables, as well as their interdependencies, are mapped into a hierarchical objects network where the rules and real-time constraints are implicit in objects operators and network topology. The state of monitored variables updates a fact-base, which is used by a real-time inference-machine (RT/IM) to activate and synchronize the fire of the knowledge-base (KB) rules. The operators man–machine interface (MMI) are, then, updated. Besides, also following the object-oriented paradigm, AIMS provides many facilities for building and maintaining the KB and the operators MMI. In order to illustrate the use of AIMS, we show part of a real application in Angra-2 NPP.     

Swarm intelligence of artificial bees applied to In-Core Fuel Management Optimization

Artificial Bee Colony (ABC) algorithm is a relatively new member of swarm intelligence. ABC tries to simulate the intelligent behavior of real honey bees in food foraging and can be used for solving continuous optimization and multi-dimensional numeric problems. This paper introduces the Artificial Bee Colony with Random Keys (ABCRK), a modified ABC algorithm for solving combinatorial problems such as the In-Core Fuel Management Optimization (ICFMO). The ICFMO is a hard combinatorial optimization problem in Nuclear Engineering which during many years has been solved by expert knowledge. It aims at getting the best arrangement of fuel in the nuclear reactor core that leads to a maximization of the operating time. As a consequence, the operation cost decreases and money is saved. In this study, ABCRK is used for optimizing the ICFMO problem of a Brazilian “2-loop” Pressurized Water Reactor (PWR) Nuclear Power Plant (NPP) and the results obtained with the proposed algorithm are compared with those obtained by Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The results show that the performance of the ABCRK algorithm is better than or similar to that of other population-based algorithms, with the advantage of employing fewer control parameters.     

A nuclear reactor core fuel reload optimization using artificial ant colony connective networks

The core of a nuclear Pressurized Water Reactor (PWR) may be reloaded every time the fuel burn-up is such that it is not more possible to maintain the reactor operating at nominal power. The nuclear core fuel reload optimization problem consists in finding a pattern of burned-up and fresh-fuel assemblies that maximize the number of full operational days. This is an NP-Hard problem, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Moreover, the problem is non-linear and its search space is highly discontinuous and multi-modal.     

Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm

In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results.     

Particle Swarm Optimization applied to the nuclear reload problem of a Pressurized Water Reactor

The concept of Swarm Intelligence is based on the ability of individuals to learn with their own experience in a group as well as to take advantage of the performance of other individuals, which are social–collaborative aspects of intelligence. In 1995, Kennedy and Eberhart presented the Particle Swarm Optimization (PSO), a Computational Intelligence metaheuristic technique. Since then, some PSO models for discrete search spaces have been developed for combinatorial optimization, although none of them presented satisfactory results to optimize a combinatorial problem such as the Nuclear Reactor Reload Problem (NRRP). In this sense, we have developed the Particle Swarm Optimization with Random Keys (PSORK) to optimize combinatorial problems. PSORK has been tested for benchmarks to validate its performance and to be compared to other techniques such as Ant Systems and Genetic Algorithms, and in order to analyze parameters to be applied to the NRRP. We also describe and discuss its performance and applications to the NRRP with a survey of the research and development of techniques to optimize the reloading operation of Angra 1 nuclear power plant, located at the Southeast of Brazil.     

Soft computing systems applied to PWR''s xenon

The present work intends to introduce a soft computing technique as an effective and robust tool available to deal with nuclear engineering problems. This goal is reached by the presentation of an application: a genetic programming system (GP) able to automatically design a controller for the axial xenon oscillations in a pressurized water reactors (PWRs). The axial xenon oscillations control methodology is based on three axial offsets: the xenon axial offset (AOx), the iodine axial offset (AOi) and the neutron flux axial offset (AOf), effectively used in former work. Simulations were made using a two-point xenon oscillation model which employs the non-linear xenon and iodine balance equations and the one group, one-dimensional neutron diffusion equation, with non-linear power reactivity feedback, also proposed in the literature. Obtained results showed the ability of the GP in finding a strategy which can effectively control the axial xenon oscillations.     

Applying particle swarm optimization algorithm for tuning a neuro-fuzzy inference system for sensor monitoring

A neuro-fuzzy inference system (ANFIS) tuned by particle swarm optimization (PSO) algorithm has been developed for monitoring the relevant sensor in a nuclear power plant (NPP) using the information of other sensors. The antecedent parameters of the ANFIS that estimates the relevant sensor signal are optimized by a PSO algorithm and consequent parameters use a least-squares algorithm. The proposed methodology to monitor sensor output signals was demonstrated through the estimation of the nuclear power value in a pressurized water reactor using as input to the ANFIS six other correlated signals. The obtained results are compared to two similar ANFIS using one gradient descendent (GD) and other genetic algorithm (GA), as antecedent parameters' training algorithm.