Nano 2026

Buildings contribute approximately 36% of global final energy consumption and nearly 37% of energy-related CO₂ emissions, creating an urgent need for advanced technologies to improve energy efficiency and support net-zero targets. Nanofluid-based thermal energy systems have emerged as promising solutions for enhancing heat transfer, improving thermal management, and reducing energy consumption in heating, ventilation, and air-conditioning (HVAC), renewable energy, and thermal storage applications. However, their optimization remains challenging due to complex interactions among nanoparticle properties, concentration, flow conditions, and operating parameters. This review presents an Artificial Intelligence (AI)-driven optimization framework for nanofluid-enhanced thermal energy systems in net-zero green buildings. Recent applications of machine learning techniques, including Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and deep learning algorithms, are discussed for thermal performance prediction, parameter optimization, and intelligent energy management. The performance of different nanofluids, including Al₂O₃/water, CuO/water, TiO₂/water, graphene-based, and carbon nanotube (CNT)-based nanofluids, is evaluated in terms of heat transfer enhancement, thermal efficiency, pressure drop, and system reliability. The review further examines AI-assisted optimization of heat exchangers, solar thermal collectors, phase change material (PCM)-based storage systems, and HVAC technologies. Despite significant progress, challenges related to nanofluid stability, pumping power requirements, computational complexity, cost, and large-scale implementation remain. Future research should focus on physics-informed machine learning, digital twins, reinforcement learning, and hybrid AI optimization strategies to develop intelligent, efficient, and sustainable thermal energy systems for next-generation net-zero buildings.

Mr Tesfaye Barza is a Lecturer, Researcher, and International Reviewer at Wolaita Sodo University of Technology — Excellence in Applied Science, Ethiopia. He serves as an Early Career Editorial Board Member for Geomechanics & Geoengineering (Taylor & Francis) and as a Session Chair for ICDTDE 2025 at Jordan University of Science and Technology. His research spans HVAC systems, nanotechnology, CFD, and turbulent flow analysis. Passionate about innovation, Mr. Barza actively pursues cutting-edge research and global collaborations that bridge theory and practical applications, driving sustainable and high-performance engineering solutions worldwide.