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.

The article provides a comprehensive review of the latest developments in the field of laser surface hardening (LSH) and its modeling techniques. LSH is a crucial process for enhancing the surface properties of metals, particularly their hardness and wear resistance, without compromising their bulk properties. This review highlights the fundamental principles of LSH, the types of lasers used, and the key parameters influencing the hardening process. It delves into various modeling approaches, including finite element method (FEM) simulations, analytical models, and empirical models (using statistical methods), emphasizing the integration of advanced computational techniques such as machine learning and artificial intelligence to improve the accuracy and efficiency of LSH simulations. The review also explores practical applications across different industries, showcasing how LSH models have been used to solve real-world challenges in the automotive, aerospace, and tool manufacturing sectors. Finally, it addresses current limitations and outlines future research directions, suggesting potential areas for further advancements in the modeling and application of LSH processes.

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.

High-entropy alloys (HEAs) are a novel class of metallic materials composed of five or more principal elements in near-equimolar ratios. This unconventional composition leads to high configurational entropy, which promotes the formation of solid solution phases with enhanced mechanical properties, thermal stability, and corrosion resistance. Phase stability plays a critical role in determining their structural integrity and performance. This study provides a focused review of HEA phase transitions, emphasizing the role of lattice gas models in predicting phase behavior. By integrating statistical mechanics with thermodynamic principles, lattice gas models enable accurate modeling of atomic interactions, phase segregation, and order-disorder transformations. The combination of computational simulations (e.g., Monte Carlo, molecular dynamics) with experimental validation (e.g., XRD, TEM, APT) improves predictive accuracy. Furthermore, advances in data-driven methodologies facilitate high-throughput exploration of HEA compositions, accelerating the discovery of alloys with optimized phase stability and superior mechanical performance. Beyond structural applications, HEAs demonstrate potential in functional domains, such as catalysis, hydrogen storage, and energy technologies. This review brings together theoretical modeling—particularly lattice gas approaches—and experimental validation to form a unified understanding of phase behavior in high-entropy alloys. By highlighting the mechanisms behind phase transitions and their implications for material performance, this work aims to support the design and optimization of HEAs for real-world applications in aerospace, energy systems, and structural materials engineering.