Multi-objective optimization design of air distribution of grate cooler by entropy generation minimization and genetic algorithm

2016 ◽  
Vol 108 ◽  
pp. 76-83 ◽  
Author(s):  
Wei Shao ◽  
Zheng Cui ◽  
Lin Cheng
Keyword(s):  
Genetic Algorithm ◽  
Entropy Generation ◽  
Optimization Design ◽  
Air Distribution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Entropy Generation Minimization ◽  
Grate Cooler

Multi-Objective Optimization of Heat Exchanger Design by Entropy Generation Minimization

2010 ◽  
Vol 132 (8) ◽  
Author(s):  
Jiangfeng Guo ◽  
Lin Cheng ◽  
Mingtian Xu
Keyword(s):  
Heat Exchanger ◽  
Entropy Generation ◽  
Optimization Design ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pumping Power ◽  
Multi Objective ◽  
Entropy Generation Minimization ◽  
Pareto Optimal Set ◽  
Optimal Set

In the present work, a multi-objective optimization of heat exchanger thermal design in the framework of the entropy generation minimization is presented. The objectives are to minimize the dimensionless entropy generation rates related to the heat conduction under finite temperature difference and fluid friction under finite pressure drop. Constraints are specified by the admissible pressure drop and design standards. The genetic algorithm is employed to search the Pareto optimal set of the multi-objective optimization problem. It is found that the solutions in the Pareto optimal set are trade-off between the pumping power and heat exchanger effectiveness. In some sense, the optimal solution in the Pareto optimal set achieves the largest exchanger effectiveness by consuming the least pumping power under the design requirements and standards. In comparison with the single-objective optimization design, the multi-objective optimization design leads to the significant decrease in the pumping power for achieving the same heat exchanger effectiveness and presents more flexibility in the design process.

Multi-Objective Optimization Design of Screw Conveyor Using Genetic Algorithm

2013 ◽  
Vol 732-733 ◽  
pp. 402-406
Author(s):  
Duan Yi Wang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimization Design ◽  
Optimization Problem ◽  
Machine Design ◽  
Screw Conveyor ◽  
Optimized Design ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
The Mathematical Model

The weight minimum and drive efficiency maxima1 of screw conveyor were considered as double optimizing objects in this paper. The mathematical model of the screw conveyor has been established based on the theory of the machine design, and the genetic algorithm was adopted to solving the multi-objective optimization problem. The results show that the mass of spiral shaft reduces 13.6 percent, and the drive efficiency increases 6.4 percent because of the optimal design based on genetic algorithm. The genetic algorithm application on the screw conveyor optimized design can provided the basis for designing the screw conveyor.

Multi-Objective Optimization Design of Tanker Ships via a Genetic Algorithm

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Marcelo Ramos Martins ◽  
Diego F. Sarzosa Burgos
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Ship Design ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Total Cost ◽  
Multi Objective ◽  
The Cost

The cost of a new ship design heavily depends on the principal dimensions of the ship; however, dimensions minimization often conflicts with the minimum oil outflow (in the event of an accidental spill). This study demonstrates one rational methodology for selecting the optimal dimensions and coefficients of form of tankers via the use of a genetic algorithm. Therein, a multi-objective optimization problem was formulated by using two objective attributes in the evaluation of each design, specifically, total cost and mean oil outflow. In addition, a procedure that can be used to balance the designs in terms of weight and useful space is proposed. A genetic algorithm was implemented to search for optimal design parameters and to identify the nondominated Pareto frontier. At the end of this study, three real ships are used as case studies.

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