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TECHNICAL PAPERS: Gas Turbines: Heat Transfer

Genetic Algorithm Optimization for Primary Surfaces Recuperator of Microturbine

[+] Author and Article Information
Wang Qiuwang1

State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P.R. Chinawangqw@mail.xjtu.edu.cn

Liang Hongxia, Xie Gongnan, Zeng Min, Luo Laiqin, Feng ZhenPing

State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P.R. China

1

Corresponding author.

J. Eng. Gas Turbines Power 129(2), 436-442 (Jul 03, 2006) (7 pages) doi:10.1115/1.2436550 History: Received June 16, 2006; Revised July 03, 2006

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.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 1

Flow diagram for genetic algorithm

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Figure 2

Model description: (a) CWPS geometry parameters; (b) CCPS geometry parameters; and (c) recuperator matrix

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Figure 3

Flow chart of RAT routine

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Figure 4

Evolution process for CWPS: (a) by GA1 (for minimum weight); and (b) by GA2 (for minimum weight and maximum compactness)

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Figure 5

Evolution process for CCPS: (a), (c), (e) by GA1; and (b), (d), (f) by GA2

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