FuelGen: a genetic algorithm-based system for fuel loading pattern design in nuclear power reactors

1998 ◽  
Vol 14 (4) ◽  
pp. 461-470 ◽  
Author(s):  
Jun Zhao ◽  
Brian Knight ◽  
Ephraim Nissan ◽  
Alan Soper
Keyword(s):  
Genetic Algorithm ◽  
Nuclear Power ◽  
Fuel Loading ◽  
Pattern Design ◽  
Loading Pattern

ICONE15-10008 A New Approach for Optimal Fuel Core Loading Pattern Design in VVER Nuclear Power Reactors Using Genetic Algorithm

2007 ◽  
Vol 2007.15 (0) ◽  
pp. _ICONE1510-_ICONE1510
Author(s):  
Niloofar Mohseni ◽  
Mehrdad Boroushaki ◽  
Mohammad B. Ghofrani ◽  
Mohammad H. Raji ◽  
Morteza Gharib
Keyword(s):  
Genetic Algorithm ◽  
Nuclear Power ◽  
Pattern Design ◽  
New Approach ◽  
Loading Pattern

Discussion of the Effects of AP1000 First Core Loading Pattern Design on Nuclear Power Plant Operation

2010 ◽  
Author(s):  
Xuesong Li
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Pattern Design ◽  
Plant Operation ◽  
Loading Pattern ◽  
Low Leakage ◽  
Power Plant Operation ◽  
The Impact ◽  
Operation Cost

Core loading pattern design has great influence on nuclear power plant operation. An excellent core loading pattern can not only enhance operation factor, reduce operation cost, but also increase operation safety. Under the premise of nuclear safety, AP1000 first core loading pattern achieves the goal of low leakage loading by simulating the reactivity distribution of the 18-month Equilibrium Cycle design. The fuel management presented in this paper illustrates the economic performance and technical feasibility of the advanced 18-month cycle first core fuel. The advanced feature of this first core design include: the development of loading pattern that simulates the reactivity distribution of a typical low leakage reload core in order to reduced leakage, the use of radial enrichment zoning in higher enriched assemblies to lower peaking factors, and the use of axial burnable absorber zoning to improve axial power shape control and reduce axial peaking. The discussion provided in this paper demonstrates the ability of the advanced first core design operate safety and efficiently, and the core is designed with adequate peaking factor margin in both base-load and load-follow operation. Finally, this paper analyses the impact brought about by multi-enrichment on the nuclear power plant operation capacity and operation cost, and arrive at a conclusion that precise fuel cycle evaluation such that the enrich uranium and operation costs can be accurately quantified and control in a detailed economic evaluation.

RELIABILITY BASED OPTIMIZATION OF TECHNICAL SPECIFICATION OF FRONTLINE SYSTEMS OF NUCLEAR POWER PLANTS USING MULTI-OBJECTIVE APPROACH

2012 ◽  
Vol 19 (01) ◽  
pp. 1250002
Author(s):  
G. SRINIVAS ◽  
A. K. VERMA ◽  
A. SRIVIDYA ◽  
SANJAY KUMAR KHATTRI
Keyword(s):  
Genetic Algorithm ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Technical Specification ◽  
Multi Objective Optimization ◽  
Potential Core ◽  
Multi Objective ◽  
Genetic Algorithm Approach ◽  
Technical Specifications

Technical Specifications define the limiting conditions of operation, maintenance and surveillance test requirements for the various Nuclear Power plant systems in order to meet the safety requirements to fulfill regulatory criteria. These specifications impact even the economics of the plant. The regulatory approach addresses only the safety criteria, while the plant operators would like to balance the cost criteria too. The attempt to optimize both the conflicting requirements presents a case to use Multi-objective optimization. Evolutionary algorithms (EAs) mimic natural evolutionary principles to constitute search and optimization procedures. Genetic algorithms are a particular class of EA's that use techniques inspired by evolutionary biology such as inheritance, mutation, natural selection and recombination (or cross-over). In this paper we have used the plant insights obtained through a detailed Probabilistic Safety Assessment with the Genetic Algorithm approach for Multi-objective optimization of Surveillance test intervals. The optimization of Technical Specifications of three front line systems is performed using the Genetic Algorithm Approach. The selection of these systems is based on their importance to the mitigation of possible accident sequences which are significant to potential core damage of the nuclear power plant.

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