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A Novel Approach for Reducing Feature Space Dimensionality and Develop a Universal Machine-Learning Model for Coated Tubes in Cross Flow Heat Exchangers
Jahaninasab, M.; Taheran, E.; Zarabadi, S.A.; Aghaei, M.; Rajabpour, A. A Novel Approach for Reducing Feature Space Dimensionality and Developing a Universal Machine Learning Model for Coated Tubes in Cross-Flow Heat Exchangers. Energies2023, 16, 5185.
Jahaninasab, M.; Taheran, E.; Zarabadi, S.A.; Aghaei, M.; Rajabpour, A. A Novel Approach for Reducing Feature Space Dimensionality and Developing a Universal Machine Learning Model for Coated Tubes in Cross-Flow Heat Exchangers. Energies 2023, 16, 5185.
Jahaninasab, M.; Taheran, E.; Zarabadi, S.A.; Aghaei, M.; Rajabpour, A. A Novel Approach for Reducing Feature Space Dimensionality and Developing a Universal Machine Learning Model for Coated Tubes in Cross-Flow Heat Exchangers. Energies2023, 16, 5185.
Jahaninasab, M.; Taheran, E.; Zarabadi, S.A.; Aghaei, M.; Rajabpour, A. A Novel Approach for Reducing Feature Space Dimensionality and Developing a Universal Machine Learning Model for Coated Tubes in Cross-Flow Heat Exchangers. Energies 2023, 16, 5185.
Abstract
Cross flow heat exchangers are commonly used in the thermal industry to transfer heat from hot tubes to cooling fluid. To protect the heat exchanger tubes from corrosion and dust accumulation, microscale coatings are often applied. In this study, we present machine-learning models for predicting heat transfer from hot tubes with different micro-sized coatings to cooling fluid in a turbulent flow using computational fluid dynamics simulations. A dataset of approximately 1000 cases was generated by varying the coating coverage thickness of each tube, the inlet Reynolds number, fluid flow inlet temperature, and wall temperature of tubes. The machine-learning models were generated to predict the overall heat flow rate in the heat exchanger, and it was found that combining the features based on their importance preserved the accuracy of the models while maintaining all the relevant information. The simulation results demonstrate that the proposed method increases the coefficient of determination (R2) for the models. The R2 values for unseen data for Random Forest, K-Nearest Neighbors, and Support Vector Regression were 0.9810, 0.9037, and 0.9754, respectively, indicating the usefulness of the proposed model for predicting heat transfer in various types of heat exchangers.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
