Solving multi-objective cell design problem: an evolutionary genetic algorithm approach

Activation time and discharge time are important criteria for the performance of thermal batteries. In this work a heat transfer analysis is carried out on the working process of thermal batteries. The effects of the thicknesses of heat pellets which are divided into three groups and that of the thickness of insulation layers on activation time and discharge time of thermal batteries are numerically studied using Fluent 15.0 when the sum of the thickness of heating plates and insulation layers remain unchanged. According to the numerical results, the optimal geometric parameters are obtained by using multi-objective genetic algorithm. The results show that the activation time is mainly determined by the thickness of the bottom heat pellet, while the discharge time is determined by the thickness of the heat pellets and that of the insulation layers. The discharge time of the optimized thermal battery is increased by 4.08%, and the activation time is increased by 1.23%.

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A simulation-based multi-objective genetic algorithm (SMOGA) procedure for BOT network design problem

Optimization and Engineering ◽

10.1007/s11081-006-9970-y ◽

2006 ◽

Vol 7 (3) ◽

pp. 225-247 ◽

Cited By ~ 64

Author(s):

Anthony Chen ◽

Kitti Subprasom ◽

Zhaowang Ji

Keyword(s):

Genetic Algorithm ◽

Network Design ◽

Design Problem ◽

Network Design Problem ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Simulation Based

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

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539312500027 ◽

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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