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

Previous Articles     Next Articles

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).


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

CLC Number: 

  • TK124
[1] 苟小龙,张建涛,王广军.基于导热反问题的管道内部缺陷诊断[J].重庆大学学报,2010,33(2):42-46. GOU X L, ZHANG J T,WANG G J. Defects detection in the inner surface of pipes based on inverse heat conduction problem[J]. Journal of Chongqing University, 2010,33(2):42-46.
[2] 吕事桂,杨立,范春利,等. 基于对面红外检测的缺陷混沌-LM混合识别算法[J].工程热物理学报, 2013, 34(7):1352-1356. LÜ S G, YANG L, FAN C L, et al. Chaos-LM hybrid algorithm for the defect identification with opposite-side thermographic testing[J]. Journal of Engineering Thermophysics, 2013, 34(7):1352-1356.
[3] CHENG C H, CHANG M H. Shape identification by inverse heat transfer method[J]. Journal of Heat Transfer, 2003,125(2):224.
[4] 张林, 范春利, 孙丰瑞. 基于APDL的管道内壁边界识别算法[J]. 红外与激光工程, 2015,44(5):1477-1484. ZHANG L, FAN C L, SUN F R. Identification algorithm of pipelines' inner boundary based on APDL[J]. Infrared and Laser Engineering, 2015,44(5):1477-1484.
[5] 范春利, 孙丰瑞, 杨立. 电线电缆破损的定量热像检测与诊断方法研究[J]. 中国电机工程学报, 2005,25(18):162-166. FAN C L, SUN F R, YANG L. Study on quantitative methods of inspection and breakang diagnoses of high voltage and cable by thermography[J]. Proceedings of the CSEE, 2005,25(18):162-166.
[6] RYAN C, NOWROZ A N, REDA S. Post-silicon power characterization using thermal infrared emissions[C]//International Symposium on Low-power Electronics and Design. Austin, TX, USA, 2010.
[7] 文哲希,吕硕,何雅玲.预测喷雾冷却热流密度反问题的粒子群算法研究[J].工程热物理学报, 2013, 34(8):1506-1510. WEN Z X, LÜ S, HE Y L. Investigation of particle swarm optimization algorithm in inverse problems of estimating spray cooling heat flux[J]. Journal of Engineering Thermophysics, 2013, 34(8):1506-1510.
[8] 宋馨, 张有为, 刘自军. 反演航天器在轨瞬态外热流的导热反问题[J]. 北京航空航天大学学报, 2015,41(11):2061-2066. SONG X, ZHANG Y W, LIU Z J. Inverse heat conduction problem for deducing transient heat flux of spacecraft on orbit[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015,41(11):2061-2066.
[9] DAS K, MISHRA S C. Estimation of tumor characteristics in a breast tissue with known skin surface temperature[J]. Journal of Thermal Biology, 2013,38(6):311-317.
[10] MITRA S, BALAJI C. A neural network based estimation of tumour parameters from a breast thermogram[J]. International Journal of Heat and Mass Transfer, 2010,53(21/22):4714-4727.
[11] 龚靖棠, 屈惠明, 陈钱. 体内异常热源信息的红外无损探测模拟[J]. 红外与激光工程, 2014,43(8):2477-2481. GONG J T, QU H M, CHEN Q. Infrared non-destructive detection of abnormal heat information inside body by simulation[J]. Infrared and Laser Engineering, 2014,43(8):2477-2481.
[12] 张立广,屈惠明. 红外无损探测中多宗量多热源反演问题的研究[J]. 物理学报, 2015,64(10):1-14. ZHANG L G, QU H M. Multiple heat sources with multi-parameter inversion of nondestructive infrared detection[J]. Acta Phys. Sin., 2015,64(10):1-14.
[13] HAN F, SHI G, LIANG C. A simple and efficient method for breast cancer diagnosis based on infrared thermal imaging[J]. Cell Biochemistry and Biophysics, 2015,71(1):491-498.
[14] ARORA N, MARTINS D, RUGGERIO D. Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer[J]. Am. J. Surg., 2008,196(4):523-526.
[15] SCOTT E P. Thermal detection of embedded tumors using infrared imaging[J]. Journal of Biomechanical Engineering-Transactions of the ASME, 2007, 129(1):33-39.
[16] MITAL M, PIDAPARTI R M. Breast tumor simulation and parameters estimation using evolutionary algorithms[J]. Modelling and Simulation in Engineering, 2008, 2008:6.
[17] BEZERRA L A, OLIVEIRA M M, ROLIM T L. Estimation of breast tumor thermal properties using infrared images[J]. Signal Processing, 2013, 93(10):2851-2863.
[18] AGNELLI J P, BARREA A A, TURNER C V. Tumor location and parameter estimation by thermography[J].Mathematical and Computer Modelling, 2011, 53(7/8):1527-1534.
[19] EBERHART R C, KENNEDY J. A new optimizer using particle swarm theory[C]//Proceedings of the Sixth International Symposium on Micro Machine and Human Science. 1995:39-43.
[20] 黄鉴, 卢玫,李博汉,等.基于红外检测乳腺癌的多参数反问题研究[J].生物医学工程研究,2015,34(2):74-79. HUANG J, LU M, LI B H, et al. Investigation of multivariable estimation in breast tumor diagnosis by infrared thermography[J]. Journal of Biomedical Engineering Research, 2015,34(2):74-79.
[1] Shumin ZHENG, Pengcheng GUO, Jianguo YAN, Shuai WANG, Wenbo LI, Qi ZHOU. Experimental and predictive study on pressure drop of subcooled flow boiling in a mini-channel [J]. CIESC Journal, 2023, 74(4): 1549-1560.
[2] Xinyuan WU, Qilei LIU, Boyuan CAO, Lei ZHANG, Jian DU. Group2vec: group vector representation and its property prediction applications based on unsupervised machine learning [J]. CIESC Journal, 2023, 74(3): 1187-1194.
[3] Xuerong GU, Shuoshi LIU, Siyu YANG. Research on multi-parameter optimization method based on parallel EGO and surrogate-assisted model [J]. CIESC Journal, 2023, 74(3): 1205-1215.
[4] Sheng’an ZHANG, Guilian LIU. Multi-objective optimization of high-efficiency solar water electrolysis hydrogen production system and its performance [J]. CIESC Journal, 2023, 74(3): 1260-1274.
[5] Kenian SHI, Jingyuan ZHENG, Yu QIAN, Siyu YANG. Two-stage stochastic programming of steam power system based on Markov chain [J]. CIESC Journal, 2023, 74(2): 807-817.
[6] Haiou YUAN, Fangjun YE, Shuo ZHANG, Yiqing LUO, Xigang YUAN. Synthesis of heat-integrated distillation sequences with intermediate heat exchangers [J]. CIESC Journal, 2023, 74(2): 796-806.
[7] Jiahui CHEN, Xinze YANG, Guzhong CHEN, Zhen SONG, Zhiwen QI. A critical discussion on developing molecular property prediction models: density of ionic liquids as example [J]. CIESC Journal, 2023, 74(2): 630-641.
[8] Xuejin GAO, Kun CHENG, Huayun HAN, Huihui Gao, Yongsheng QI. Fault diagnosis of chillers using central loss conditional generative adversarial network [J]. CIESC Journal, 2022, 73(9): 3950-3962.
[9] Yalin WANG, Yuqing PAN, Chenliang LIU. Intermittent process monitoring based on GSA-LSTM dynamic structure feature extraction [J]. CIESC Journal, 2022, 73(9): 3994-4002.
[10] Jing YANG, Zhenkang LIN, Jun TANG, Cheng FAN, Kening SUN. A review of fault characteristics, fault diagnosis and identification for lithium-ion battery systems [J]. CIESC Journal, 2022, 73(8): 3394-3405.
[11] Zhe SUN, Huaqiang JIN, Kang LI, Jiangping GU, Yuejin HUANG, Xi SHEN. Fault diagnosis method of refrigeration and air-conditioning system based on digitized knowledge representation [J]. CIESC Journal, 2022, 73(7): 3131-3144.
[12] Ling YANG, Guomin CUI, Zhiqiang ZHOU, Yuan XIAO. Fine search strategy applied to mass exchange network synthesis [J]. CIESC Journal, 2022, 73(7): 3145-3155.
[13] Taoyan ZHAO, Jiangtao CAO, Ping LI, Lin FENG, Yu SHANG. Application of interval type-2 fuzzy immune PID controller to temperature control system for uncatalysed oxidation of cyclohexane [J]. CIESC Journal, 2022, 73(7): 3166-3173.
[14] Kun WANG, Hongbo SHI, Shuai TAN, Bing SONG, Yang TAO. Local time difference constrained neighborhood preserving embedding algorithm for fault detection [J]. CIESC Journal, 2022, 73(7): 3109-3119.
[15] Xinjie ZHOU, Jianlin WANG, Xingcong AI, Enguang SUI, Rutong WANG. IDPC-RVM based online prediction of quality variables for multimode batch processes [J]. CIESC Journal, 2022, 73(7): 3120-3130.
Full text



No Suggested Reading articles found!