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    Genetic Algorithm Optimization for Primary Surfaces Recuperator of Microturbine

    Source: Journal of Engineering for Gas Turbines and Power:;2007:;volume( 129 ):;issue: 002::page 436
    Author:
    Wang Qiuwang
    ,
    Liang Hongxia
    ,
    Xie Gongnan
    ,
    Zeng Min
    ,
    Luo Laiqin
    ,
    Feng ZhenPing
    DOI: 10.1115/1.2436550
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In recent years, the genetic algorithm (GA) technique has gotten much attention in solving real-world problems. This technique has a strong ability for global searching and optimization based on various objectives for their optimal parameters. The technique may be applied to complicated heat exchangers and is particularly useful for new types. It is important to optimize the heat exchanger, for minimum volume/weight, to save fabrication cost or for improved effectiveness to save energy consumption, under the requirement of allowable pressure drop; simultaneously it is mandatory to optimize geometry parameters of heating plate from technical and economic standpoints. In this paper, GA is used to optimize the cross wavy primary surface (CWPS) and cross corrugated primary surface (CCPS) geometry characteristic of recuperator in a 100kW microturbine, in order to get more compactness and minimum volume and weight. Two kinds of fitness assignment methods are considered. Furthermore, GA parameters are set optimally to yield smoother and faster fitness convergence. The comparison shows the superiority of GA and confirms its potential to solve the objective problem. When the rectangular recuperator core size and corrugated geometries are evaluated, in the CWPS the weight of the recuperator decreases by 12% or more; the coefficient of compactness increases by up to 19%, with an increase of total pressure drop by 0.84% compared to the original design data; and the total pressure drop versus the operating pressure is controlled to be less than 3%. In the CCPS area compactness is increased to 70% of the initial data by decreasing pitch and relatively high height of the passage, the weight decreases by 17–36%, depending on the inclination angle (θ). Comparatively the CCPS shows superior performance for use in compact recuperators in the future. The GA technique chooses from a variety of geometry characters, optimizes them and picks out the one which provides the closest fit to the recuperator for microturbine.
    keyword(s): Weight (Mass) , Microturbines , Optimization , Genetic algorithms , Design , Pressure drop , Geometry , Heat exchangers , Flow (Dynamics) , Pressure , Manufacturing AND Heat transfer ,
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      Genetic Algorithm Optimization for Primary Surfaces Recuperator of Microturbine

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    http://yetl.yabesh.ir/yetl1/handle/yetl/135741
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    • Journal of Engineering for Gas Turbines and Power

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    contributor authorWang Qiuwang
    contributor authorLiang Hongxia
    contributor authorXie Gongnan
    contributor authorZeng Min
    contributor authorLuo Laiqin
    contributor authorFeng ZhenPing
    date accessioned2017-05-09T00:23:44Z
    date available2017-05-09T00:23:44Z
    date copyrightApril, 2007
    date issued2007
    identifier issn1528-8919
    identifier otherJETPEZ-26949#436_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/135741
    description abstractIn recent years, the genetic algorithm (GA) technique has gotten much attention in solving real-world problems. This technique has a strong ability for global searching and optimization based on various objectives for their optimal parameters. The technique may be applied to complicated heat exchangers and is particularly useful for new types. It is important to optimize the heat exchanger, for minimum volume/weight, to save fabrication cost or for improved effectiveness to save energy consumption, under the requirement of allowable pressure drop; simultaneously it is mandatory to optimize geometry parameters of heating plate from technical and economic standpoints. In this paper, GA is used to optimize the cross wavy primary surface (CWPS) and cross corrugated primary surface (CCPS) geometry characteristic of recuperator in a 100kW microturbine, in order to get more compactness and minimum volume and weight. Two kinds of fitness assignment methods are considered. Furthermore, GA parameters are set optimally to yield smoother and faster fitness convergence. The comparison shows the superiority of GA and confirms its potential to solve the objective problem. When the rectangular recuperator core size and corrugated geometries are evaluated, in the CWPS the weight of the recuperator decreases by 12% or more; the coefficient of compactness increases by up to 19%, with an increase of total pressure drop by 0.84% compared to the original design data; and the total pressure drop versus the operating pressure is controlled to be less than 3%. In the CCPS area compactness is increased to 70% of the initial data by decreasing pitch and relatively high height of the passage, the weight decreases by 17–36%, depending on the inclination angle (θ). Comparatively the CCPS shows superior performance for use in compact recuperators in the future. The GA technique chooses from a variety of geometry characters, optimizes them and picks out the one which provides the closest fit to the recuperator for microturbine.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleGenetic Algorithm Optimization for Primary Surfaces Recuperator of Microturbine
    typeJournal Paper
    journal volume129
    journal issue2
    journal titleJournal of Engineering for Gas Turbines and Power
    identifier doi10.1115/1.2436550
    journal fristpage436
    journal lastpage442
    identifier eissn0742-4795
    keywordsWeight (Mass)
    keywordsMicroturbines
    keywordsOptimization
    keywordsGenetic algorithms
    keywordsDesign
    keywordsPressure drop
    keywordsGeometry
    keywordsHeat exchangers
    keywordsFlow (Dynamics)
    keywordsPressure
    keywordsManufacturing AND Heat transfer
    treeJournal of Engineering for Gas Turbines and Power:;2007:;volume( 129 ):;issue: 002
    contenttypeFulltext
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