MODERN CHALLENGES AND PROSPECTS IN MODELING COMPLEX HEAT AND MASS TRANSFER PROCESSES
Keywords:
Coupled heat and mass transfer, multiscale modeling, phase-change dynamics, advanced materials, microwave-assisted vacuum drying, surrogate modeling, physics-informed machine learning, uncertainty quantification, digital twins, computational fluid dynamics, model validation.Abstract
Reliable modeling of coupled heat and mass transfer is a cornerstone of technological progress in energy systems, advanced manufacturing, environmental engineering, and biomedical applications. However, as modern systems increasingly exhibit multiscale behavior, material heterogeneity, and strong interphysics interactions, the limitations of traditional modeling strategies are becoming increasingly apparent. This paper presents a detailed analysis of the key challenges facing this field, along with a structured assessment of new computational and methodological approaches developed to address them. The analysis focuses on several critical obstacles, including the exponential growth in complexity in multiscale formulations, accurate representation of phase transition boundaries, modeling and characterization of advanced and unconventional materials, and persistent shortcomings in high-quality validation data. Based on a synthesis of recent advances in high-performance computing, physics-based machine learning, and hybrid modeling methods, this paper identifies promising avenues for overcoming these limitations. Results from representative case studies demonstrate that surrogate and reduced-order models can provide an order of magnitude increase in computational efficiency, while comparative evaluations highlight the rapidly expanding role of data-driven methods in predictive modeling. We argue that future progress in heat and mass transfer modeling will depend on the development of integrated systems that tightly couple fundamentally based models with data-driven components, incorporate rigorous uncertainty quantification, and enable the creation of experimentally validated digital twins. This integration signals a fundamental shift from purely physical simulations to adaptive, intelligent computing systems capable of continuous improvement.
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