Guest Editors
Prof. Dr. M. M. Bhatti
Email: mmbhatti@korea.ac.kr
Affiliation: Material Science Innovation and Modelling (MaSIM) Research Focus Area, North-West University, Mmabatho, South Africa
Homepage:
Research Interests: computational fluid dynamics, heat and mass transfer
Dr. Sardar Muhammad Bilal
Email: sbilal@pmu.edu.sa
Affiliation: Mechanical Engineering, Prince Mohammad Bin Fahd University, Al-Khobar, Saudi Arabia
Homepage:
Research Interests: computational fluid dynamics, FEM, FDM, machine learning and deep learning, fluid structure interaction, internal and external flows, turbulent flow modelling, energy strogaing, phase change materials
Summary
The flow of rheological liquids is a key factor in the performance optimization of materials processing systems, as it governs both energy transport and thermal regulation within the considered or involved processing units. Effective fluid management enables controlled heat removal, limits excessive thermal loads, and accordingly it can enhance the efficiency and stability of many methods typically used to produce various materials .
In particular, predicting how the complexity of many materials used in mechanical, chemical, and thermal processing applications influences process outcomes generally requires advanced computational frameworks capable of resolving strongly coupled momentum and heat transfer phenomena. Accurate modeling of such aspects is therefore essential for improving product quality and process efficiency.
Conventional computational fluid dynamics (CFD) techniques provide detailed insight into flow structures and heat transfer mechanisms, while significantly reducing experimental effort, cost, and development time, particularly for transient and multiscale problems. Despite their strengths, however, purely physics-based simulations often face significant challenges when applied to highly nonlinear systems, large parameter spaces, and real-time process control such as those encountered in the field of material processing.
Notably, recent advances in machine learning, especially neural network-based approaches, offer new opportunities to overcome these limitations. When combined with CFD, data-driven methods can accelerate simulations, optimize control parameters, and enhance predictive accuracy, enabling efficient exploration of complex design spaces and near real-time process prediction.
This special issue invites original contributions on thermofluidic phenomena in materials processing systems that leverage hybrid CFD and machine learning methodologies. The focus is on addressing current challenges in high-fidelity simulation and data-driven modeling of complex rheological flows and heat transfer processes relevant to the main area of materials processing.
Keywords
multiphase flow, mathematical modelling, finite element approach, fluid structure interaction, flow characterization, machine and deep learning algorithms, multiphysics simulations, finite difference approach, cavitation, buoyantly driven flows, thermal management
Login
Register
Submit a Paper
Propose a Special lssue