The intersection of fluids engineering, thermal engineering, and heat transfer plays a crucial role in advancing technologies across various industries. In these fields, accurate prediction and control of fluid flow and heat transfer are essential for optimizing system efficiency, performance, and reliability. The growing integration of machine learning (ML) and artificial intelligence (AI) offers promising avenues to enhance traditional modeling approaches. AI/ML techniques enable data-driven predictions and the discovery of complex patterns in fluid and thermal systems, improving accuracy, reducing computational costs, and accelerating design processes. This convergence drives innovation in fields like aerospace, energy, and microelectronics.
The key problem is the inefficiency and limited accuracy of traditional methods in predicting complex heat transfer and fluid flow behaviors, To address this, integrating machine learning (ML) and artificial intelligence (AI) can enhance conventional models by improving accuracy, reducing computational costs, and automating pattern discovery. Leveraging ML/AI alongside high-fidelity simulations and experimental data will enable more reliable, efficient engineering models. This approach promises to optimize the design and control of thermal and fluid systems, offering scalable solutions for real-world applications in aerospace, energy, and beyond.
The scope of this Research Topic encompasses the integration of machine learning (ML) and artificial intelligence (AI) into fluids engineering, thermal engineering, and heat transfer. We invite contributions that focus on data-driven approaches to improve predictive accuracy, computational efficiency, and design optimization in these fields. Specific themes include AI/ML-based modeling of fluid flow and heat transfer, optimization techniques for thermal management, hybrid AI-physics models, and AI-enhanced simulations for microelectronics, aerospace, and energy applications. We also encourage studies on experimental validation of AI/ML models.We welcome a range of manuscript types, including original research, review articles, case studies, and computational frameworks. Authors are encouraged to present innovative methodologies, comparative studies of AI versus traditional methods, or practical applications of ML/AI in real-world engineering problems.
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Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
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