Applying support vector machine to predict the critical heat flux in concentric-tube open thermosiphon

The critical heat flux (CHF) prediction model based on support vector machine (SVM) is established, and has been applied to the experimental cases in concentric-tube open thermosiphon under a variety of operating conditions, together with a traditional back-propagation (BP) neural network model. In this process, the performance of SVM with different parameters of C, e and δ2 has been investigated and the optimal parameter setting is obtained. Based on the experimental results, through the comparison to the prediction results of the two models, the mean square root error and the mean relative error from SVM model are found to be about 50% of those from BP network model. The simulation results demonstrate that the SVM method can achieve better accuracy and generalization than the traditional BP network model, and it is effective for the CHF prediction in concentric-tube open thermosiphon.

[1]  Soon Heung Chang,et al.  Parametric trends analysis of the critical heat flux based on artificial neural networks , 1996 .

[2]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[3]  Hideaki Imura,et al.  Critical heat flux in a closed two-phase thermosyphon , 1983 .

[4]  Soon Heung Chang,et al.  Classification and prediction of the critical heat flux using fuzzy theory and artificial neural networks , 1994 .

[5]  Tianqing Liu,et al.  Neural network analysis of boiling heat transfer enhancement using additives , 2002 .

[6]  Saeed Setayeshi,et al.  Prediction of critical heat flux using ANFIS , 2010 .

[7]  G. H. Su,et al.  Prediction of CHF in concentric-tube open thermosiphon using artificial neural network and genetic algorithm , 2010 .

[8]  Guanghui Su,et al.  Application of an Artificial Neural Network in Reactor Thermohydraulic Problem: Prediction of Critical Heat Flux , 2002 .

[9]  Kenji Fukuda,et al.  Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions. Applications of artificial neural network , 2003 .

[10]  Jiejin Cai,et al.  Applying support vector machine to predict hourly cooling load in the building , 2009 .

[11]  Yuichi Mitsutake,et al.  CHF characteristics and correlations of concentric-tube open thermosyphon working with R22 , 2005 .

[12]  Qiong Li,et al.  On-line monitoring the performance of coal-fired power unit: A method based on support vector machine , 2009 .

[13]  M. Monde,et al.  Critical heat flux during natural convective boiling on uniformly heated inner tubes in vertical annular tubes submerged in saturated liquid , 1994 .

[14]  Jiejin Cai,et al.  Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks , 2009 .

[15]  Yasar Islamoglu,et al.  Performance prediction for non-adiabatic capillary tube suction line heat exchanger: an artificial neural network approach , 2005 .

[16]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[17]  Man Gyun Na,et al.  Calculation of the power peaking factor in a nuclear reactor using support vector regression models , 2008 .

[18]  M. Monde,et al.  Experimental study of critical heat flux in concentric-tube open thermosyphon , 1998 .

[19]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

[20]  Bernhard Schölkopf,et al.  Extracting Support Data for a Given Task , 1995, KDD.

[21]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[22]  Alessandro Mazzola,et al.  Integrating artificial neural networks and empirical correlations for the prediction of water-subcooled critical heat flux , 1997 .

[23]  P. Richardson,et al.  Boiling heat transfer and two-phase flow , 1956 .

[24]  Ma Xiaoqian.  Online Forecasting of Steam Turbine Exhaust Enthalpy Based on Support Vector Machine Method , 2006 .

[25]  Won-Pil Baek,et al.  A correction method for heated length effect in critical heat flux prediction , 2000 .

[26]  H. Ertunç,et al.  Prediction of the Pool Boiling Critical Heat Flux Using Artificial Neural Network , 2006, IEEE Transactions on Components and Packaging Technologies.

[27]  Mohammad Hassan Moradi,et al.  Empirical correlation study of dryout heat transfer at high pressure using high order neural network and feed forward neural network , 2011 .