Advances in numerical modeling are essential for heat-transfer applications in electronics cooling, renewable energy, and sustainable construction. This review explores key methods like Computational Fluid Dynamics (CFD), the Finite Element Method (FEM), the Finite Volume Method (FVM), and multiphysics modeling, alongside emerging strategies such as Adaptive Mesh Refinement (AMR), machine learning (ML), reduced-order modeling (ROM), and high-performance computing (HPC). While these techniques improve accuracy and efficiency, they also increase computational energy demands, contributing to a growing carbon footprint and sustainability concerns. Sustainable computing practices, including energy-efficient algorithms and renewable-powered data centers, offer potential solutions. Additionally, the increasing energy consumption in numerical modeling highlights the need for optimization strategies to mitigate environmental impact. Future directions point to quantum computing, adaptive models, and green computing as pathways to sustainable thermal management modeling. This study systematically reviews the latest advancements in numerical heat-transfer modeling and, for the first time, provides an in-depth exploration of the roles of computational energy optimization and green computing in thermal management. This review outlines a roadmap for efficient, environmentally responsible heat-transfer models to meet evolving demands.

Chemical vapor deposition (CVD) is a vital process for deposit of thin films of various materials with precise control over the thickness, composition, and properties. Understanding the mechanisms of heat and mass transfer during CVD is essential for optimizing process parameters and ensuring high-quality film deposition. This review provides an overview of recent advancements in heat and mass transfer modeling for chemical vapor deposition processes. It explores innovative modeling techniques, recent research findings, emerging applications, and challenges in the field. Additionally, it discusses future directions and potential areas for further advancement in CVD modeling.

Autonomous vehicles (AVs) have the potential to revolutionize transportation by offering safer, more efficient, and convenient mobility solutions. As AV technology advances, there is a growing need to understand and model traffic dynamics in environments where AVs interact with human-driven vehicles. This review provides a comprehensive overview of the modeling techniques used to simulate and analyze autonomous vehicle traffic. It covers the fundamental principles of AVs, key factors influencing traffic dynamics, various modeling approaches, their applications, challenges, and future directions in AV traffic modeling.