Forecasting of saturated boiling heat transfer inside smooth helically coiled tubes using conventional and machine learning techniquesPrévision du transfert de chaleur en ébullition saturée à l'intérieur de tubes lisses enroulés en hélice à l'aide de techniques conventionnelles et d'apprentissage automatique
Section snippets
Abbreviations
- AARE
Average absolute relative error
- GPR
Gaussian process regression
- HTC
Heat transfer coefficient
- LSFM
Least square fitting method
- MLP
Multilayer perceptron
- RBF
Radial basis function
- RRMSE
Relative root mean squared error
GPR
The gaussian process regression (GPR) is known as a non-parametric and supervised machine learning algorithm which uses the concept of probability. It should be noted that this method provides reasonable outcomes even for limited numbers of data samples. Accordingly, it is utilized for a broad range of problems with nonlinear behaviors. GPR includes a collection of random variables which they have the multivariate normal distributions (Hosseini et al., 2019). If the outputs of are
Assessment of the earlier models
As mentioned before, several researchers have developed empirical models for HTC inside coiled tubes based on their own limited data. In contrast, some others have suggested the use of straight tubes’ models for flow boiling HTC in helical coils. A summary of earlier models for predicting HTC during flow boiling inside straight and coiled tube is presented in Table 3.
The accuracy of the various models listed in Table 3 are examined with the collected data points and the results are presented in
Conclusions
This study dealt with modeling of the HTC during saturated flow boiling in coiled tubes. In addition to conventional modeling approach of LSFM, three well-known machine learning algorithms, i.e., GPR, MLP and RBF as well as the intelligent method of GP were implemented to development of comprehensive and reliable models. The models were validated by an extensive set of 1035 experimental data collected from 13 published sources. The main findings of this study are listed as follows:
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By a
Data availability statements
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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