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Pool-Boiling Heat-Transfer Enhancement on Cylindrical Surfaces with Hybrid Wettable Patterns
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Boiling Heat Transfer Evaluation in Nanoporous Surface Coatings.
Uzair Sajjad1, Imtiyaz Hussain2, Muhammad Imran3
1Department of Mechanical Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan.
Nanomaterials (Basel, Switzerland)
|December 24, 2021
Summary
This study introduces a deep learning model to predict the boiling heat transfer coefficient (HTC) for nanoporous surfaces. The model accurately estimates HTC across various fluids and materials, overcoming limitations of traditional methods.
Area of Science:
- Heat Transfer
- Materials Science
- Artificial Intelligence
Background:
- Nanoporous coated surfaces enhance boiling heat transfer.
- Accurate prediction of heat transfer coefficient (HTC) for these surfaces is challenging due to complex interactions.
- Existing mathematical-empirical models are insufficient for diverse working fluids and materials.
Purpose of the Study:
- To develop a robust deep learning (DL) method for predicting the boiling heat transfer coefficient (HTC) of nanoporous coated surfaces.
- To create a model capable of handling various working fluids and substrate/coating materials.
- To identify key parameters influencing HTC prediction.
Main Methods:
- A deep learning approach was developed using a dataset of 1042 experimental points.
- Several deep neural networks were designed and optimized.
- Correlation analysis identified critical parameters: pore diameter, substrate thermal conductivity, heat flow, and fluid thermophysical properties.
Main Results:
- The optimized deep learning model achieved high prediction accuracy.
- The model demonstrated an R-squared value of 0.998.
- The mean absolute error (MAE) was reported as 1.94%.
Conclusions:
- Deep learning provides a powerful tool for predicting HTC on nanoporous surfaces.
- The developed model is versatile, applicable to diverse materials and fluids.
- Key parameters identified offer insights for future surface design and optimization.
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