A review on artificial intelligence thermal fluids and the integration of energy conservation with blockchain technology

The high degree of convergence between thermal fluid sciences and artificial intelligence (AI) has changed traditional energy management methods. The technology provides energy conservation, fluid dynamics, and heat transfer optimisation solutions. In order to model prediction and increase the effectiveness of thermal fluid application proposals, this review looks at the latest developments in the use of AI-enabled machine learning techniques, such as Artificial Neural Networks (ANNs), Support Vector Machines (SVM), and Deep Learning Hierarchy. In order to support sustainable energy goals, these highlighted machine learning algorithms offer a potent environment for optimising energy flow, temperature regulation, and application stability. Furthermore, diverse reinforcement learning techniques facilitate the adoptive control of intricate thermal applications in real-time settings, while Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are employed for applicational monitoring and real-time data processing. By combining blockchain technology with artificial intelligence, a decentralised framework environment is introduced that offers energy conservation methods that are safe, transparent, honest, and reliable. An unchangeable ledger is provided by the technology, and accountability and traceability are provided by smart contracts. It supports the vital tasks of dynamically monitoring and validating energy consumption across decentralised applications (DApps) in real-time. Additionally, this article offers a thorough examination of recent research, the integration of emerging technologies, and real-world uses of blockchain and artificial intelligence in thermal fluid applications. A cost-effective energy management environment that supports international energy conservation initiatives is created by combining the predictive power of AI with the security features of blockchain technology. In addition, it offers a platform for future study, giving it a starting point for innovation in sustainable energy management.