摘要
本文利用符号回归分析换热器的实验数据,以寻找更高精度的换热关联式。为改进MATLAB环境下的遗传规划工具箱GPLAB的性能,增入了四种新功能:个体简化、常数优化、单亲交叉、新生操作。利用改进后的GPLAB对连续螺旋折流板管壳式换热器壳程传热的实验数据进行了符号回归,得到的换热关联式的预测精度高且稳健度强。
In the present study, symbolic regression is used to analyze the experimental data to find a more accurate correlation for heat transfer rate of heat exchangers. In order to improve the performance of genetic programming toolbox GPLAB of MATLAB environment, four new function modules are added into GPLAB, including： structure simplification module; constants optimization module; expansion rate reduction module with ＂self-swap＂ genetic operator; small term search intensity enhancement module with ＂intro-new＂ genetic operator. The modified symbolic regression is then used in the experimental data reduction process for shell-and-tube heat exchanger with continuous helical baffles. The correlations obtained have higher predictive accuracy and are less sensitive to the disturbance variation of the arguments.
出处
《工程热物理学报》
EI
CAS
CSCD
北大核心
2012年第8期1415-1418,共4页
Journal of Engineering Thermophysics
基金
国家自然科学基金(No.50776068)
关键词
符号回归
遗传规划
换热器
关联式
symbolic regression
genetic programming
heat exchanger
correlations
