A Review on Multistage CFD and AIML Based Hybrid Wind-Hydro Turbine

Achal Ajay Jha; Priyanka Hemant Jain; Wasim Sattar Malkani; Robin Stanley Nadar

A Review on Multistage CFD and AIML Based Hybrid Wind-Hydro Turbine

Author(s)	Achal Ajay Jha, Priyanka Hemant Jain, Wasim Sattar Malkani, Robin Stanley Nadar
Country	India
Abstract	Rapid industrialization has propelled economic expansion while simultaneously intensifying global energy consumption, fossil fuel dependence, and ecological degradation. As the world transitions toward sustainable development and net-zero emissions, innovative, robust, and eco-friendly energy systems are urgently required. This study presents a Hybrid Computational Fluid Dynamics–Artificial Intelligence (CFD–AI) framework for advanced turbine design and performance enhancement. The model integrates high-resolution CFD simulations with AI/ML algorithms to optimize multi-stage hybrid turbines, particularly suited for decentralized renewable microgrids in rural and semi-urban areas with variable resource availability. CFD accurately captures intricate flow behaviors and operational characteristics, while AI/ML models dynamically forecast performance, adapting to changing wind, hydro, and environmental inputs. Predictive efficiency is achieved through supervised learning models such as Random Forest and Gradient Boosting (XGBoost/LightGBM), complemented by Feedforward and Physics-Informed Neural Networks (PINNs) for physics integration and computational cost reduction. Long Short-Term Memory (LSTM) networks handle time-series predictions for power output fluctuations. This synergistic approach enables highly flexible and efficient turbine systems. Integrated energy storage units (e.g., battery banks) further stabilize supply and scalability for localized end-users like homes, schools, and industries. The reviewed literature demonstrates that hybrid CFD–AI strategies can boost output stability by up to 20% compared to conventional designs, significantly reducing reliance on fossil fuels. The research aligns with United Nations Sustainable Development Goals on clean energy, sustainable communities, and climate action, illustrating how emerging technologies can transform industrial challenges into pathways toward a greener, equitable, net-zero future.
Keywords	CFD, AI/ML, Sustainable Development, Net-Zero Energy, Microgrids, Decentralized, Renewable Energy, Hybrid Turbine, Turbine Optimization, Energy Storage, Hybrid Systems, Wind-Hydro Turbine, Gradient Boosting, XGBoost, LightGBM.
Field	Engineering
Published In	Volume 6, Issue 12, December 2025
Published On	2025-12-30

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A Review on Multistage CFD and AIML Based Hybrid Wind-Hydro Turbine

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