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• W2015246244 abstract "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, ε 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." @default.
• W2015246244 created "2016-06-24" @default.
• W2015246244 creator A5061646416 @default.
• W2015246244 date "2012-05-01" @default.
• W2015246244 modified "2023-10-14" @default.
• W2015246244 title "Applying support vector machine to predict the critical heat flux in concentric-tube open thermosiphon" @default.
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• W2015246244 doi "https://doi.org/10.1016/j.anucene.2011.12.029" @default.
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