메뉴 건너뛰기




Volumn 43, Issue , 2012, Pages 114-122

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

Author keywords

Artificial neural network; Critical heat flux; Prediction; Support vector machine

Indexed keywords

BACK-PROPAGATION NEURAL NETWORKS; BP NETWORKS; MEAN RELATIVE ERROR; MEAN SQUARE ROOT ERROR; OPERATING CONDITION; OPTIMAL PARAMETER SETTINGS; PREDICTION MODEL; SUPPORT VECTOR MACHINE (SVM); SVM MODEL;

EID: 84856483584     PISSN: 03064549     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.anucene.2011.12.029     Document Type: Article
Times cited : (36)

References (29)
  • 1
    • 55949095851 scopus 로고    scopus 로고
    • Calculation of the power peaking factor in a nuclear reactor using support vector regression models
    • I.H. Bae, M.G. Na, Y.J. Lee, and G.C. Park Calculation of the power peaking factor in a nuclear reactor using support vector regression models Ann. Nucl. Energy 35 12 2008 2200 2205
    • (2008) Ann. Nucl. Energy , vol.35 , Issue.12 , pp. 2200-2205
    • Bae, I.H.1    Na, M.G.2    Lee, Y.J.3    Park, G.C.4
  • 2
    • 33751074960 scopus 로고    scopus 로고
    • Online forecasting of steam turbine exhaust enthalpy based on support vector machine method
    • (in Chinese)
    • J.J. Cai, and X.Q. Ma Online forecasting of steam turbine exhaust enthalpy based on support vector machine method Autom. Elec. Pow. Syst. 30 8 2006 77 82 (in Chinese)
    • (2006) Autom. Elec. Pow. Syst. , vol.30 , Issue.8 , pp. 77-82
    • Cai, J.J.1    Ma, X.Q.2
  • 3
    • 33750219415 scopus 로고    scopus 로고
    • Forecasting unburned carbon content in the fly ash from coalfired utility boilers based on SVM
    • (in Chinese)
    • J.J. Cai, and X.Q. Ma Forecasting unburned carbon content in the fly ash from coalfired utility boilers based on SVM J. Comb. Sci. Tech. 12 4 2006 312 317 (in Chinese)
    • (2006) J. Comb. Sci. Tech. , vol.12 , Issue.4 , pp. 312-317
    • Cai, J.J.1    Ma, X.Q.2
  • 4
    • 64749099212 scopus 로고    scopus 로고
    • On-line monitoring the performance of coal-fired power unit: A method based on support vector machine
    • J.J. Cai, X.Q. Ma, and Q. Li On-line monitoring the performance of coal-fired power unit: A method based on support vector machine Appl. Th. Eng. 29 11-12 2009 2308 2319
    • (2009) Appl. Th. Eng. , vol.29 , Issue.1112 , pp. 2308-2319
    • Cai, J.J.1    Ma, X.Q.2    Li, Q.3
  • 5
    • 0742268991 scopus 로고    scopus 로고
    • Support vector machine with adaptive parameters in financial time series forecasting
    • L.J. Cao, and E.H. Tay Francis Support vector machine with adaptive parameters in financial time series forecasting IEEE Trans. Neural Network 14 6 2003 1506 1523
    • (2003) IEEE Trans. Neural Network , vol.14 , Issue.6 , pp. 1506-1523
    • Cao, L.J.1    Tay Francis, E.H.2
  • 6
    • 77952009580 scopus 로고    scopus 로고
    • Prediction of CHF in concentric-tube open thermosiphon using artificial neural network and genetic algorithm
    • R.H. Chen, G.H. Su, S.Z. Qiu, and K. Fukuda Prediction of CHF in concentric-tube open thermosiphon using artificial neural network and genetic algorithm Heat Mass Transf. 46 3 2010 345 353
    • (2010) Heat Mass Transf. , vol.46 , Issue.3 , pp. 345-353
    • Chen, R.H.1    Su, G.H.2    Qiu, S.Z.3    Fukuda, K.4
  • 7
    • 33947230691 scopus 로고    scopus 로고
    • Prediction of the pool boiling critical heat flux using artificial neural network
    • DOI 10.1109/TCAPT.2006.885944
    • H. Ertunc Prediction of the pool boiling critical heat flux using artificial neural network IEEE Trans. Comp. 29 4 2006 770 777 (Pubitemid 46417164)
    • (2006) IEEE Transactions on Components and Packaging Technologies , vol.29 , Issue.4 , pp. 770-777
    • Ertunc, H.M.1
  • 8
    • 0346250790 scopus 로고    scopus 로고
    • Practical selection of SVM parameters and noise estimation for SVM regression
    • DOI 10.1016/S0893-6080(03)00169-2
    • V. Cherkassky, and Y. Ma Practical selection of SVM parameters and noise estimation for SVM regression Neural Networks 17 1 2004 113 126 (Pubitemid 38019003)
    • (2004) Neural Networks , vol.17 , Issue.1 , pp. 113-126
    • Cherkassky, V.1    Ma, Y.2
  • 11
    • 23844501353 scopus 로고    scopus 로고
    • CHF characteristics and correlations of concentric-tube open thermosyphon working with R22
    • DOI 10.1016/j.ijheatmasstransfer.2005.03.033, PII S0017931005003406
    • M.A. Islam, M. Monde, and Y. Mitsutake CHF characteristics and correlations of concentric-tube open thermosyphon working with R22 Int. J. Heat Mass Transfer 48 21-22 2005 4615 4622 (Pubitemid 41168454)
    • (2005) International Journal of Heat and Mass Transfer , vol.48 , Issue.21-22 , pp. 4615-4622
    • Islam, M.A.1    Monde, M.2    Mitsutake, Y.3
  • 12
    • 0342955703 scopus 로고    scopus 로고
    • A correction method for heated length effect in critical heat flux prediction
    • Y.H. Lee, W.P. Baek, and S.H. Chang A correction method for heated length effect in critical heat flux prediction Nucl. Eng. Des. 199 1-2 2000 1 11
    • (2000) Nucl. Eng. Des. , vol.199 , Issue.12 , pp. 1-11
    • Lee, Y.H.1    Baek, W.P.2    Chang, S.H.3
  • 13
    • 64849083683 scopus 로고    scopus 로고
    • Applying support vector machine to predict hourly cooling load in the building
    • Q. Li, Q.L. Meng, J.J. Cai, H. Yoshino, and A. Mochida Applying support vector machine to predict hourly cooling load in the building Appl. Energ. 86 10 2009 2249 2256
    • (2009) Appl. Energ. , vol.86 , Issue.10 , pp. 2249-2256
    • Li, Q.1    Meng, Q.L.2    Cai, J.J.3    Yoshino, H.4    Mochida, A.5
  • 14
    • 56049088473 scopus 로고    scopus 로고
    • Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks
    • Q. Li, Q.L. Meng, J.J. Cai, H. Yoshino, and A. Mochida Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks Energy Convers. 50 1 2009 90 96
    • (2009) Energy Convers. , vol.50 , Issue.1 , pp. 90-96
    • Li, Q.1    Meng, Q.L.2    Cai, J.J.3    Yoshino, H.4    Mochida, A.5
  • 15
    • 0036882493 scopus 로고    scopus 로고
    • Neural network analysis of boiling heat transfer enhancement using additives
    • T.Q. Liu, X.Y. Sun, X.Q. Li, and H.L. Wang Neural network analysis of boiling heat transfer enhancement using additives Int. J. Heat Mass Transfer 45 25 2002 5083 5089
    • (2002) Int. J. Heat Mass Transfer , vol.45 , Issue.25 , pp. 5083-5089
    • Liu, T.Q.1    Sun, X.Y.2    Li, X.Q.3    Wang, H.L.4
  • 16
    • 0001022365 scopus 로고    scopus 로고
    • Integrating artificial neural networks and empirical correlations for the prediction of water-subcooled critical heat flux
    • A. Mazzola Integrating artificial neural networks and empirical correlations for the prediction of water-subcooled critical heat flux Revue Générale de Thermique 36 11 1997 799 806 (Pubitemid 127818611)
    • (1997) Revue Generale de Thermique , vol.36 , Issue.11 , pp. 799-806
    • Mazzola, A.1
  • 17
    • 8444240737 scopus 로고    scopus 로고
    • Experimental study of the critical heat flux in a two-phase open concentric-tube thermosyphon
    • Y. Mitsutake, M. Monde, M.Z. Hasan, and W. Kim Experimental study of the critical heat flux in a two-phase open concentric-tube thermosyphon Heat Transfer-Jap. Res. 26 5 1997 319 331
    • (1997) Heat Transfer-Jap. Res. , vol.26 , Issue.5 , pp. 319-331
    • Mitsutake, Y.1    Monde, M.2    Hasan, M.Z.3    Kim, W.4
  • 18
    • 0009853791 scopus 로고
    • Critical heat flux during natural convective boiling in a vertical uniformly heated inner tubes in vertical annular tubes submerged in saturated liquid
    • M. Monde, Y. Mitsutake, and S. Kubo Critical heat flux during natural convective boiling in a vertical uniformly heated inner tubes in vertical annular tubes submerged in saturated liquid Wärme Stoffübertrag 29 4 1994 271 276
    • (1994) Wärme Stoffübertrag , vol.29 , Issue.4 , pp. 271-276
    • Monde, M.1    Mitsutake, Y.2    Kubo, S.3
  • 19
    • 0002060271 scopus 로고    scopus 로고
    • Parametric trends analysis of the critical heat flux based on artificial neural networks
    • S.K. Moon, W.P. Baek, and S.H. Chang Parametric trends analysis of the critical heat flux based on artificial neural networks Nucl. Eng. Des. 163 1-2 1996 29 49 (Pubitemid 126369912)
    • (1996) Nuclear Engineering and Design , vol.163 , Issue.1-2 , pp. 29-49
    • Moon, S.K.1    Baek, W.-P.2    Chang, S.H.3
  • 20
    • 0028761466 scopus 로고
    • Classification and prediction of the critical heat flux using fuzzy theory and artificial neural networks
    • S.K. Moon, and S.H. Chang Classification and prediction of the critical heat flux using fuzzy theory and artificial neural networks Nucl. Eng. Des. 150 1 1994 151 161
    • (1994) Nucl. Eng. Des. , vol.150 , Issue.1 , pp. 151-161
    • Moon, S.K.1    Chang, S.H.2
  • 21
    • 84857997921 scopus 로고    scopus 로고
    • Empirical correlation study of dryout heat transfer at high pressure using high order neural network and feed forward neural network
    • D. Rostamifard, M. Fallahnezhad, S. Zaferanlouei, S. Setayeshi, and M.H. Moradi Empirical correlation study of dryout heat transfer at high pressure using high order neural network and feed forward neural network Heat Mass Transf. 47 4 2011 439 448
    • (2011) Heat Mass Transf. , vol.47 , Issue.4 , pp. 439-448
    • Rostamifard, D.1    Fallahnezhad, M.2    Zaferanlouei, S.3    Setayeshi, S.4    Moradi, M.H.5
  • 23
    • 0036577756 scopus 로고    scopus 로고
    • Application of an artificial neural network in reactor thermohydraulic problem: Prediction of critical heat flux
    • G.H. Su, K. Fukuda, D.N. Jia, and K. Morita Application of an artificial neural network in reactor thermohydraulic problem: prediction of critical heat flux J. Nucl. Sci. Technol. 39 5 2002 564 571 (Pubitemid 34741215)
    • (2002) Journal of Nuclear Science and Technology , vol.39 , Issue.5 , pp. 564-571
    • Su, G.1    Fukuda, K.2    Jia, D.3    Morita, K.4
  • 24
    • 0037333125 scopus 로고    scopus 로고
    • Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions. applications of artificial neural network
    • G.H. Su, K. Morita, K. Fukuda, M. Pidduck, D.N. Jia, and J. Miettien Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions. applications of artificial neural network Nucl. Eng. Des. 220 1 2003 17 35
    • (2003) Nucl. Eng. Des. , vol.220 , Issue.1 , pp. 17-35
    • Su, G.H.1    Morita, K.2    Fukuda, K.3    Pidduck, M.4    Jia, D.N.5    Miettien, J.6
  • 27
    • 84887252594 scopus 로고    scopus 로고
    • Support vector method for function approximation, regression estimation and signal processing
    • MIT Press Denver (CO)
    • V. Vapnik, S.E. Golowich, and A.J. Smola Support vector method for function approximation, regression estimation and signal processing Advanced Neural Information Processing System 1996 MIT Press Denver (CO) 281 287
    • (1996) Advanced Neural Information Processing System , pp. 281-287
    • Vapnik, V.1    Golowich, S.E.2    Smola, A.J.3
  • 28
    • 4644327028 scopus 로고    scopus 로고
    • Performance prediction for non-adiabatic capillary tube suction line heat exchanger: An artificial neural network approach
    • I. Yasar, K. Akif, and P. Cem Performance prediction for non-adiabatic capillary tube suction line heat exchanger: an artificial neural network approach Energy Convers. 46 2 2005 223 233
    • (2005) Energy Convers. , vol.46 , Issue.2 , pp. 223-233
    • Yasar, I.1    Akif, K.2    Cem, P.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.