International Communications in Heat and Mass Transfer
Critical heat flux prediction using genetic programming for water flow in vertical round tubes
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Cited by (19)
Dependence of critical heat flux in vertical flow systems on dimensional and dimensionless parameters using machine learning
2024, International Journal of Heat and Mass TransferParticle swarm optimization-based least squares support vector regression for critical heat flux prediction
2013, Annals of Nuclear EnergyCitation Excerpt :There are several hybrid methods that combine ANN and other techniques for prediction of CHF (Kim et al., 2000b; Zaferanlouei et al., 2010). In addition to ANN, genetic algorithm (GA), one of the evolutionary algorithm (EA) methods, is also used alone (Lee et al., 1997) or in combination with ANN to predict the CHF (Kwon et al., 2005; Wei et al., 2010). However, ANN has its own disadvantages, such as having difficulty in determining the number of hidden layers and hidden neurons per layer and may get stuck in local minima and overfitting.
Combination of support vector regression and artificial neural networks for prediction of critical heat flux
2013, International Journal of Heat and Mass TransferCitation Excerpt :Most of them were summarized in Table 4 of [10] and the others were reported in the references [11,12]. Besides, genetic algorithm (GA), as one of the optimization techniques, has also been used to predict CHF [13]. Generally, these above studies can be divided into two categories: one is that ANNs alone were used to predict CHF and the other is that ANNs were combined with other techniques to predict CHF.
Applications of genetic neural network for prediction of critical heat flux
2010, International Journal of Thermal SciencesCitation Excerpt :It tries to find an optimal transformation of variables and then simple regression analysis is performed for the transformed variables. In reference [8], genetic programming was used to find a mathematical expression in a symbolic form between dependent and independent variables. Hundreds of different models and correlations on the prediction of CHF exist in the open literature and enormous amounts of experimental data are available at present.
Heat transfer correlations by symbolic regression
2006, International Journal of Heat and Mass TransferApplications: Applications of artificial neural networks and genetic methods in thermal engineering
2017, CRC Handbook of Thermal Engineering, Second Edition
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