Prediction of LBB leakage for various conditions by genetic neural network and genetic algorithms

Abstract In this study, three-layer Back Propagation Network (BPN) and Genetic Neural Network (GNN) were applied to predict the leakage of Leak Before Break (LBB) for various conditions. The inputs include six dimensionless variables, and the Reynolds number is set as the output. The GNN (with relative error of 22.7%) shows a higher accuracy than the BPN (with relative error of 26.1%), the existing models and commercial software. Influences of thermal–hydraulic properties and crack morphologies on LBB leakage were discussed based on the trained GNN: the LBB leakage is proportional to the Crack Opening Displacement (COD), crack length, subcooling degree, stagnation pressure and the area ratio of inlet to outlet, while it is inversely proportional to crack depth and local roughness. Moreover, mechanism-based correlations for LBB leakage were proposed by genetic algorithm in this study. The flow resistance due to phase transition and area variation was considered, and the entrance resistance coefficient, friction resistance factor and plugging were presented. The presented correlations provide higher precision than the existing correlation, with average error of 35.9%. The proposed correlations are meaningful for LBB leakage estimation.

[1]  M. T. Flaman,et al.  Leak rate experiments for through-wall artificial cracks , 1990 .

[2]  Stephen B. M. Beck,et al.  Identification of fluid flow regimes in narrow cracks , 2000 .

[3]  H. Fauske.  CONTRIBUTION TO THE THEORY OF TWO-PHASE, ONE-COMPONENT CRITICAL FLOW , 1962 .

[5]  Mehdi Mehrabi,et al.  Application of the FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network approaches to modelling the thermal conductivity of alumina–water nanofluids , 2012 .

[6]  S. Ashforth-Frost,et al.  Evaluating convective heat transfer coefficients using neural networks , 1996 .

[7]  G. Wallis.  One Dimensional Two-Phase Flow , 1969 .

[8]  Wenxi Tian,et al.  Research on the leak-rate characteristics of leak-before-break (LBB) in pressurized water reactor (PWR) , 2014 .

[9]  Jovica R. Riznic,et al.  Flashing Flow of Subcooled Liquid through Small Cracks , 2013 .

[10]  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 .

[11]  José Luis González-Andújar,et al.  Comparison of fitting weed seedling emergence models with nonlinear regression and genetic algorithm , 2009 .

[12]  Wenxi Tian,et al.  Depressurization study of supercritical fluid blowdown from simple vessel , 2014 .

[13]  Nader Nariman-zadeh,et al.  Modelling and Pareto optimization of heat transfer and flow coefficients in microchannels using GMDH type neural networks and genetic algorithms , 2008 .

[14]  H. Itō.  Pressure Losses in Smooth Pipe Bends , 1960 .

[15]  Yang Xu,et al.  Demonstration of leak-before-break in Japan Sodium cooled Fast Reactor (JSFR) pipes , 2014 .

[16]  Ahmet Selim Dalkılıç,et al.  Artificial neural network techniques for the determination of condensation heat transfer characteristics during downward annular flow of R134a inside a vertical smooth tube , 2011 .

[17]  J. Ahmad,et al.  Evaluation and refinement of leak-rate estimation models. Revision 1 , 1991 .

[18]  J. C. Leung,et al.  A generalized correlation for one‐component homogeneous equilibrium flashing choked flow , 1986 .

[19]  M. Anderson,et al.  CRITICAL FLOW EXPERIMENT AND ANALYSIS FOR SUPERCRITICAL FLUID , 2008 .

[20]  Ali Mirsepahi,et al.  A comparative artificial intelligence approach to inverse heat transfer modeling of an irradiative dryer , 2013 .

[21]  Robert E. Henry,et al.  The Two-Phase Critical Flow of One-Component Mixtures in Nozzles, Orifices, and Short Tubes , 1971 .

[22]  Sandip Ghosh,et al.  An experimental analysis of subcooled leakage flow through slits from high pressure high temperature pipelines , 2011 .

[23]  W. A. Sutherland,et al.  Critical flow of saturated and subcooled water at high pressure. [PWR; BWR] , 1975 .

[24]  Chakravarthy Balaji,et al.  Thermal optimization of PCM based pin fin heat sinks: An experimental study , 2013 .

[25]  B. Chexal,et al.  PICEP: Pipe Crack Evaluation Program , 1984 .

[26]  B. Chexal,et al.  Calculation of leak rates through cracks in pipes and tubes. Final report , 1983 .

[27]  M. A. Grolmes,et al.  A generalized correlation for flashing choked flow of initially subcooled liquid , 1988 .

[28]  Robert E. Henry,et al.  Two-Phase Critical Flow at Low Qualities Part I: Experimental , 1970 .

[29]  Guanghui Su,et al.  Applications of ANNs in flow and heat transfer problems in nuclear engineering: A review work , 2013 .

[30]  Yasar Islamoglu,et al.  A new approach for the prediction of the heat transfer rate of the wire-on-tube type heat exchanger––use of an artificial neural network model , 2003 .

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

[32]  H. John,et al.  Critical two-phase flow through rough slits , 1988 .

[33]  Tadashi Narabayashi,et al.  Study on crack opening area and coolant leak rates on pipe cracks , 1991 .

[34]  V. E. Schrock,et al.  Two-phase critical flow in slits , 1983 .

[35]  Chunlei Zhang,et al.  Generalized neural network correlation for flow boiling heat transfer of R22 and its alternative refrigerants inside horizontal smooth tubes , 2006 .

[36]  S. Sablani.  A neural network approach for non-iterative calculation of heat transfer coefficient in fluid–particle systems , 2001 .

[37]  Q. Bi,et al.  Leak rates of high pressure steam–water across simulation crack , 2014 .

[38]  L. Piazza,et al.  Analysis of forced convection heat transfer to supercritical carbon dioxide inside tubes using neural networks , 2003 .