Genetic algorithms for nuclear reactor fuel load and reload optimization problems

Genetic algorithm is one of the optimization techniques, successfully applied in nuclear reactor environment for a variety of applications. Nuclear fuel management optimization is a classical nuclear engineering problem, which has been studied extensively and several techniques, including genetic algorithms, have been used for its solution. The study presented in this paper addresses the overall procedures and methods developed for the application of genetic algorithms in the optimization studies of nuclear fuel management. The result obtained from a study that explores a typical application of genetic algorithm in nuclear fuel management is also presented. Finding out of optimal number of fuel subassemblies in the core of a nuclear reactor (having power generating capacity of 500 MWe) is taken up for the study. The results demonstrate the suitability and efficiency of the algorithm in generating feasible solutions for the selected optimization problem.

In-pile testing of ITER first wall mock-ups at relevant thermal loading conditions in the LVR-15 nuclear research reactor

Neutronic calculations and thermalhydraulic application using CFD for the nuclear research reactor NUR at steady state mode

Design of the CAREM nuclear reactor core with dual cooled annular fuel and optimizing the thermal-hydraulic, natural circulation, and neutronics parameters

A genetic algorithm for online-scheduling of a multiproduct polymer batch plant

Numerical modeling simulations and the use of high-performance computing are fundamental for detailed safety analysis, control and operation of a nuclear reactor, allowing the study and analysis of problems related with thermal-hydraulics, neutronic and the dynamic of fluids which are involved in these systems. In this work we introduce the bases for the implementation of the smoothed particle hydrodynamics (SPH) approach to analyze heat transfer in a nuclear reactor core. Heat transfer by means of convection is of great importance in many engineering applications and especially in the analysis of heat transfer in nuclear reactors. As a first approach, the natural convection in the gap (space that exists between the fuel rod and the cladding) can be analyzed helping to reduce uncertainty in such calculations that usually relies on empirical correlations while using other numerical tools. The numerical method developed in this work was validated while comparing the results obtained in previous numerical simulations and experimental data reported in the literature showing that our implementation is suitable for the study of heat transfer in nuclear reactors. Numerical simulations were done with the DualSPHysics open source code that allows to perform parallel calculations using different number of cores. The current implementation is a version written in CUDA (Compute Unified Device Architecture) that allows also the use of GPU processors (Graphics Processor Unit) to accelerate the calculations in parallel using a large number of cores contained in the GPU. This makes possible to analyze large systems using a reasonable computer time. The obtained results verified and validated our method and allowed us to have a strong solver for future applications of heat transfer in nuclear reactors fuel inside the reactor cores.

This paper innovates, within the context of preventive maintenance planning, the application of Genetic Algorithm, as a modern powerful optimization tool, in minimum downtime strategies, for optimal part replacement. Therefore, a brief account of Genetic Algorithm is given and a computer program is written to aid the solution of such problems by Genetic Algorithm. Then, the program is used to solve a numerical example, concerning an equipment with Gaussian failure characteristics. The results obtained by Genetic Algorithm and the ordinary methods are compared to show that exactly the same results can be approached by the application of Genetic Algorithm. Genetic Algorithm and maintenance planning, both being subjects of statistical nature, can be combined to provide a vast area of interesting research work and, hence, some guidelines are suggested for further research.

Application of Data-Driven technology in nuclear Engineering: Prediction, classification and design optimization

The Meta heuristic algorithms are the higher level technique which helps to find the best feasible solution out of all possible solution of an optimization problem. There are various different types of meta heuristic algorithms like Ant Colony Optimization (ACO), Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization, etc. Genetic Algorithm is a search-based optimization technique based on the biological principle of Genetics and adaptation. It is a meta-heuristic approach which is used to solve complex combinatorial problem. The integration of Genetic algorithm with machine learning will be helpful in solving unconstrained and constrained optimization problem. The various genetic operator like selection operator, mutation and cross-over are discussed which will be helpful in knowing how these operators significantly improves State Space search. In this paper the various applications of Genetic algorithms which can be used in machine learning has been discussed. In this paper the author discussed how the significance of Genetic algorithm will be improved while solving complex optimization problem in machine learning. In this paper, flow diagram of Genetic Algorithms has been discussed which will ease the understanding of complex optimization problem like 0–1 Knapsack, Traveling Salesman Problem, etc. In this paper a comparative analysis between traditional algorithm and genetic algorithm has been done on the basis of parameters like flow of control, state space search, Complication, Preconditions, CPU utilization etc. The various limitations of Genetic Algorithms in solving problems with optimal solutions has also been discussed.

As one of the forth-generation nuclear energy system reactor types, lead fast reactor has good safety and economical properties due to the stable chemical properties of the coolant and the proliferation characteristics of the fuel, and modular nuclear power faster reactor designed for nuclear plant can further improve the economics of the reactor. In this article, the conceptual design of the lead-based modular power reactors with different power levels loaded with uranium alloy fuel is found to be found that when reactor core size increased to a certain level, the proliferation performance is too high due to the increase of the reactor core size under a specific core life such as 2000EFPD, so at the end of core life, the reactor core still has a large remaining reactivity. The proliferation advantage of the core cannot be fully released during the current core life time. Based on this phenomenon, in this article, we optimized the conceptual design of lead-based modular nuclear power reactor core loaded with uranium alloy fuel, and proposed to choose the appropriate rod to diameter ratio and effective density of fuel based on the power level and life time of the core. By adjusting the amount of uranium and 235U per unit volume, the proliferation performance of the core can be changed to match the power level and life time of the core. So the reactivity of core during the life period does not change, which not only reduce the difficulty of the reactivity control, but also make full use of the proliferation performance of the core. And at the same time, the reasonable rod to diameter ratio can provide safety and design margin for the analysis of thermal and hydraulic safety, and effectively improve the economy and safety of the core.

We review the recent work done by our group on theoretically analyzing the behavior of genetic algorithms(GAs) and searching for new ideas and strategies to design more efficient GAs. By introducing the concept of degree of population diversity and using the tools from finite Markov chain, we presented fairly clear characterizations of premature convergence in GAs and proved some results on the relationships between premature convergence and GA’s control parameters such as the population size, the mutation and crossover probabilities, and some population statistics. In the mean time, we have also obtained a result that depicts the search ability of the crossover operators in GAs. An upper bound on the convergence rate for the GAs has been established. Based on the theoretical analyses, we have proposed a novel GA called Genetic Algorithm with No Genetic Operators (GANGO), which needs neither to maintain a population nor to use the conventional genetic operators, and yet has the same search mechanisms as the classical GAs. As another development in improving the performance of GAs, a new concept called Dual Fitness Function was proposed which, when incorporated into the classical GAs will enable the algorithms to judge whether the current population is in the local or global attractive basins. And in this way, the control parameters of GAs can be adjusted adaptively. Some applications of our results to fields such as fuzzy systems, multi-destination routing problem (MDR) are briefly

Comparative study of the conceptual design of Gas-cooled Fast Reactor (GFR) core type tall versus pancake based on modified CANDLE-B (MCANDLE-B) burnup strategy has been done. MCANDLE-B is a burnup strategy that utilizes natural uranium or depleted fuel as its input cycle. The conceptual design of a reactor core that compared is the tall cylinder and the pancake cylinder. In this case, the fuel used is U-10%Zr, SS-316 as a cladding material and helium as a coolant. The total volume of the two reactor cores is the same, namely 15.4 m3. SRAC 2K6 software with PIJ and CITATION modules is used to carry out simulations. The PIJ module is used for fuel cell calculations and the CITATION module is used for reactor core calculations. The results of the comparison show that the pancake core allows the reactor core to use fuel with a volume fraction of 50%: 10%: 40%, for fuel, cladding and coolant respectively. The design obtained can be operated for 10 years without refueling.

A review of CFD studies on thermal hydraulic analysis of coolant flow through fuel rod bundles in nuclear reactor

In order to ensure the compactness and high-power density of a nuclear power reactor, the research on tight-lattice fuel bundle, with a narrow gap distance between fuels, has been highlighted. Recently, KAERI (Korea Atomic Energy Research Institute) has been developing dual-cooled annular fuel to increase a significant amount of the reactor power in OPR1000 (Optimized Power Reactor), a PWR (Pressurized Water Reactor) optimized in the Republic of Korea. The dual-cooled annular fuel is configured to allow a coolant flow through the inner channel as well as the outer channel. To introduce the dual-cooled annular fuel to OPR1000 is aiming at increasing the reactor power by 20% and reducing the fuel-pellet temperature by 30%, as compared to the cylindrical solid fuel, without a change in reactor components. In such a case, due to larger outer diameter of a dual-cooled annular fuel, the dual-cooled annular fuel assembly exhibits a smaller P/D (Pitch-to-Diameter ratio) than the conventional cylindrical solid fuel assembly. In other words, the dual-cooled annular fuel array becomes the tight-lattice fuel bundle configuration, and such a change in P/D can significantly affect the thermal-hydraulic characteristics in nuclear reactor core. In this paper, the pressure drop and flow pulsation in tight-lattice rod bundle were investigated. As the test sections, the tight-lattice rod bundle of P/D = 1.08 was prepared with the regular rod bundle of P/D = 1.35. The friction factors in P/D = 1.08 appeared smaller than those in P/D = 1.35. For P/D = 1.08, the twist-vane spacer grid became the larger pressure loss coefficients than the plain spacer grid. In P/D = 1.08, the flow pulsation, quasi-periodic oscillating flow motion, was visualized successfully by PIV (Particle Image Velocimetry) and MIR (Matching Index of Refraction) techniques. The peak frequency and power spectral density of flow pulsation increased with increasing the Reynolds number. Our belief is that this work can contribute to a design of nuclear reactor with tight-lattice fuel bundle for compactness and power-uprate and a further understanding of the coolant mixing phenomena in a nuclear fuel assembly.

The results of multigroup calculations of continuous irradiation of Np, Am, and Cm in VVER-1000, PHWR-880, Superphoenix-1200, BREST-1000, and ELYaU-800 reactors are used to compare transmutation efficiency. The sources of continuous replenishment for the transmuters were Np, Am, and Cm extracted after a 3-yr holding period from the VVER and Superphoenix spent fuel. It is shown that the most effective transmuter is a subcritical liquid-fuel ELYaU system with an average thermal-neutron flux in the blanket 2·10 15 sec ‐1 ·cm ‐2 . For solid-fuel reactors, the continuous-irradiation model makes it possible to describe approximately the multiple transmutation regime. In the foreseeable future, one-time transmutation of Np, Am, and Cm in a solid-fuel reactor followed by storage in a long-term storage facility is feasible. The results of different computational variants for such regimes show that for transmutation in 10 yr in PHWR the radiotoxicity of Np, Am, and Cm accumulated in long-term storage reaches an equilibrium in no longer than 100 yr. Nuclear reactors used for transmutation actually become a kind of repository for sources of long-lived radionuclides. There is no need to prove that the content of the most dangerous (for humans) radionuclides in transmutation reactors today is incomparably more dangerous than storage in specially equipped enclosures at low temperatures and pressures. However, these considerations, in our opinion, should not be taken as a justification for considering research on transmutation as unpromising. The specialists who are proposing nuclear power as a large-scale and long-term method for producing energy are bound to develop and substantiate an effective method for eliminating harmful wastes produced as a result of such production. It is not ruled out that the practical application of transmutation will be needed only in the distant future, possibly in the period when nuclear power plants will not longer be operating. But, means must be expended even now on investigations which will make it possible to show the actual possibility of annihilating long-lived radionuclides. Otherwise, society may completely reject the use of atomic energy. In this paper, we present the results of investigations of the effectiveness of transmutation of long-lived radionuclides in reactor systems operating under closed fuel cycle conditions. The technological aspects of the problem, such as the arrangement of radionuclides in the reactor core, the influence of the radionuclides on the degree of burnup of the fuel, and the reactivity coefficients, were not studied. The results of such comparative investigations were first published in [1]. In th e present paper, multigroup calculations of the burnup and fission rate, i.e., the elimination of Np, Am, Cm, and their daughter nuclei during irradiation in different neutron fluxes and spectra, are presented. In addition, the latest data on the radiation safety standards are used [2] and the list of possible transmutation reactors and Np, Am, and Cm sources is expanded. The combined approach to handling long-lived radionuclides using transmutation and long-term storage is analyzed. The results obtained made it possible to choose the optimal, in our opinion, scenario for transmutation of Np, Am, and Cm.