Application of Machine Learning in the Design of High-Performance Heat Transfer Structures

Machine learning has been increasingly applied in various fields, including the design of high-performance heat transfer structures for turbines, heat exchangers, chips, etc. Structural innovation and optimization are crucial for improving the performance of these systems, achieving high heat transfer and low pressure drop. However, traditional design methods are often labor-intensive and time-consuming, requiring extensive experimental testing and numerical simulation. Machine learning can potentially overcome these limitations by enabling rapid design iterations and predicting the performance of novel structures using historical data and simulation results.

The goal of this Research Topic is to apply machine learning techniques to optimize the design of high-performance heat transfer structures. Specifically, it aims to develop a data-driven framework that integrates machine learning algorithms with numerical simulation and experimental testing to achieve the following objectives:
1. Accelerate the design process by identifying key design parameters and their interactions that govern the performance of heat transfer structures;
2. Enhance the quality and quantity of flow and heat transfer data obtained from existing experiments and simulations through data enhancement techniques;
3. Predict the performance of novel heat transfer structures and guide their optimal design using machine learning models trained with limited experimental and simulation data;
4. Determine the most suitable machine learning algorithms for optimization design of heat transfer structures;
5. Minimize the number of experimental tests required for validating the accuracy of machine learning models and optimizing the design of heat transfer structures.

This Research Topic will focus on the application of machine learning in the design of heat transfer structures used in a variety of engineering systems, including but not limited to energy systems (e.g., turbines, heat exchangers, and reactors) and electronic products (e.g., chips, integrated circuits). The Research Topic welcomes papers on:
• Proposal and design of innovative high-performance heat transfer structure concept;
• Data enhancement methods for flow and heat transfer data, such as Generative Adversarial Networks (GAN), Conditional Tabular GAN (CTGAN), or other methods;
• Construction of machine learning models for flow and heat transfer performance, such as BPNN, CNN, SVM, etc.;
• Optimization design of high-performance heat transfer structures;
• Verification methods for machine learning models and optimization results of high-performance heat transfer structures.

Keywords: Structural innovation, Heat transfer enhancement, low pressure drop, machine learning, data enhancement, Optimization design

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.