Artificial Intelligence Assisted Digital Twin Frameworks for Heat Transfer and Fluid Flow Modeling and Optimization

Abstract

The increasing complexity of thermal-fluid systems has created a growing demand for intelligent computational frameworks capable of real-time monitoring, prediction, optimization, and decision support. In this context, Artificial Intelligence (AI)- assisted Digital Twin technology has emerged as a transformative approach that integrates physical systems, computational models, sensor networks, and data-driven intelligence within a unified virtual environment. This review presents a comprehensive examination of AI-assisted Digital Twin frameworks for heat transfer and fluid flow applications. The fundamental principles of thermal-fluid modeling, including governing transport equations, dimensionless parameters, and computational methodologies, are first discussed. Subsequently, the architecture of Digital Twin systems and the role of Artificial Intelligence techniques such as Machine Learning, Deep Learning, Explainable Artificial Intelligence, and PhysicsInformed Neural Networks are critically analyzed. The review further compares major computational frameworks, including Computational Fluid Dynamics, Finite Difference Methods, Finite Element Methods, Finite Volume Methods, and PhysicsInformed Neural Networks, highlighting their respective strengths and limitations in Digital Twin implementation. Representative applications in heat exchangers, HVAC systems, energy systems, manufacturing processes, microfluidics, and smart infrastructure are also examined. Finally, key challenges related to data quality, computational complexity, model integration, and explainability are discussed, together with emerging research directions toward autonomous and self-learning Digital Twin ecosystems. The findings indicate that the integration of physics-based modeling and Artificial Intelligence offers significant potential for advancing intelligent thermal-fluid systems, enabling enhanced operational efficiency, predictive capability, and sustainable engineering decision-making.