CIESC Journal ›› 2016, Vol. 67 ›› Issue (S1): 103-110.DOI: 10.11949/j.issn.0438-1157.20160535

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Inverse heat conduction problem based on least squares prediction

WANG Linlin, LU Mei, HUANG Jian   

  1. School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  • Received:2016-04-25 Revised:2016-05-10 Online:2016-08-31 Published:2016-08-31
  • Supported by:

    supported by the National Natural Science Foundation of China (51176126).


王琳琳, 卢玫, 黄鉴   

  1. 上海理工大学能源与动力工程学院, 上海 200093
  • 通讯作者: 卢玫,
  • 基金资助:



With thermo-gram, parameters of tumor inside can be estimated, and an inverse heat conduction model with unknown inner heat source could be obtained from it, and the solving process need a large number solutions of the heat conduction problem, where temperature field in the sub-domain is calculated. For 3D model, it needs a relatively long time. Particle swarm optimization combined with least square methods was applied to solve the inverse problem, in which least square method was used to predict particle's value of fitness function. During the solution process, some of the particles are going to be excluded from the group, by the judgment of new definition of distance. Hence, these particles' positions were rearranged. This method consumes less time than the modified PSO mentioned above, without sacrificing accuracy. Prediction coefficient was analyzed to find how it influences the searching process. So linear decreasing prediction coefficient was applied. Numerical verification shows that above method can reduce the numbers of solution of heat conduction, shorten the solving time, without sacrificing accuracy.

Key words: inverse heat conduction problem, least square method, prediction, algorithm, imaging



关键词: 导热反问题, 最小二乘法, 预测, 算法, 成像

CLC Number: