Authors : Purnima Rai; Anindita Kundu; Sakshi Pillai Nair

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/4wku9ns9

Scribd : https://tinyurl.com/2yk3umnj

DOI : https://doi.org/10.38124/ijisrt/26jun1647

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Thermal-fluid engineering has traditionally relied on experimental investigations and numerical techniques such as the Finite Difference Method (FDM), Finite Element Method (FEM), Finite Volume Method (FVM), and Computational Fluid Dynamics (CFD) for the analysis of heat transfer and fluid flow phenomena. While these approaches provide physically consistent and reliable solutions, increasing system complexity and computational demands have motivated the integration of Artificial Intelligence (AI) technologies into engineering workflows. Recent advances in machine learning, deep learning, Physics-Informed Neural Networks (PINNs), Explainable Artificial Intelligence (XAI), and Digital Twins have significantly enhanced predictive capabilities, optimization efficiency, and real-time decision support. However, fully autonomous AI systems often face challenges related to interpretability, physical consistency, and engineering trustworthiness. This review examines the emerging paradigm of Human–AI collaborative intelligence for thermal-fluid systems, where human expertise and artificial intelligence operate synergistically to improve modeling, prediction, optimization, and engineering decision-making. The evolution of thermal-fluid modeling approaches, major computational frameworks, AI technologies, and representative engineering applications are comprehensively discussed. Furthermore, a Human–AI collaborative intelligence framework is presented to highlight the complementary roles of engineering knowledge and computational intelligence. Challenges associated with data availability, model reliability, explainability, Digital Twin integration, and human–AI interaction are critically analyzed, and future research opportunities are identified. The review demonstrates that Human–AI collaborative intelligence has the potential to establish a new generation of transparent, efficient, and trustworthy thermal-fluid engineering systems capable of supporting advanced industrial and scientific applications.

Keywords : Human–AI Collaborative Intelligence; Thermal-Fluid Engineering; Computational Fluid Dynamics; Physics-Informed Neural Networks; Explainable Artificial Intelligence; Digital Twins; Machine Learning; Engineering Decision Support.

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