Modern Approaches to Enhance Thermal Efficiency: Computational Fluid Dynamics (CFD) Methods and Machine Learning Applications

Dear Colleagues,

Buoyant fluid flow entails the movement of fluid induced by temperature gradients, leading to heat transfer. Here, the fluid movement could be driven by density gradients from temperature variations, known as free (natural) convection, or by forced convection where fluid movement is generated by external mechanisms such as pumps or fans, and finally through mixed convection mode where both buoyancy and external effects contribute simultaneously to the fluid flow and resulting heat transfer. These processes could be observed in a variety of applications, including industrial heating systems, power plants, and natural phenomena such as air circulation and ocean currents. Numerical simulation and modeling of convective fluid flow and heat transfer in thermal systems are crucial for examining intricate physical processes where analytical solutions are sometimes challenging or unattainable. The modeling of the convective heat transfer process is governed by coupled and nonlinear partial differential equations that require sophisticated numerical techniques to solve. The CFD simulation, combined with ML techniques of convective transport, provides numerous benefits as compared to experimental visualizations, including cost-effectiveness, time efficiency, comprehensive insights, adaptability, and enhanced safety. Nonetheless, numerical modeling also encounters difficulties related to intricate geometries that mimic real-world problems, the specification of boundary conditions due to unclear physical parameters, and instabilities in numerical schemes that result in divergence or oscillation of solutions.

The reported difficulties in CFD simulations highlight promising potential that can be addressed with the recent advancements in the field of machine learning and deep learning. Machine learning methods can be utilized in a wide spectrum of CFD applications such as enhancing turbulence modeling, optimizing mesh generation, and making real-time forecasts. The integration between computational fluid dynamics on the one hand and machine learning methods on the other hand can help in overcoming the computational power limitation and predicting complex flow events and many other possible applications. This research direction provides researchers with opportunities for proposing new solutions that benefit from the physical precision of CFD and the high prediction accuracy of machine learning methods.

To address the above challenges dealing with the flow and heat transfer rates in finite or infinite domains, this Special Issue on “Modern Approaches to Enhance Thermal Efficiency: Computational Fluid Dynamics (CFD) Methods and Machine Learning Applications” has been devoted to exhibiting novel research ideas.

In this regard, I am delighted to invite you to contribute new and innovative ideas on buoyant flow and thermal analysis to this high-impact Special Issue. The potential topics of interest for this Special Issue include, but are not limited to, the following areas related to:

• Convection (free/forced/mixed convection);
• Heat and mass transport;
• MHD flow;
• Radiation heat transfer;
• Nanofluid flow and heat transfer;
• Computational methods for fluid flow and thermal transport;
• CFD;
• Geometrical impacts on thermal transport;
• Stability;
• Experimental analysis;
• Enhancement of heat transfer in engineering devices;
• Application of Artificial Neural Network (ANN);
• Application of Machine Learning (ML) in CFD;
• Flow and transport in porous media;
• Double-diffusive convection;
• Analytical techniques;
• Entropy minimization.

Prof. Dr. Sankar Mani
Dr. Ahmad Salah
Guest Editors

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• heat and mass transfer
• nanofluids and hybrid/ ternary nanofluids
• entropy minimization
• porous media
• magnetic field
• computational techniques
• bio-convection
• nanofluids
• machine learning models