Investigation of Synergistic Machine Learning–Driven Thermofluidic Nano-Enhanced Cooling Architectures for Large-Scale Photovoltaic Arrays in Hyper-Arid Environments: A Mohammed bin Rashid Al Maktoum Solar Park Case Study

Large-scale photovoltaic installations in hyper-arid environments face critical thermal management challenges that significantly reduce efficiency and operational lifespan. This study investigates the integration of machine learning- driven optimization with advanced thermofluidic nano-enhanced cooling systems to address these challenges using the Mohammed bin Rashid Al Maktoum Solar Park as a representative case study. The research demonstrates that hybrid Al2O3/TiO2 nanofluid cooling systems achieve temperature reductions of 22.5°C and efficiency improvements of 14.8% compared to uncooled systems. Machine learning optimization frameworks utilizing artificial neural networks achieve prediction accuracies exceeding 94% while enabling real-time adaptive control of cooling parameters. Implementation at the 3,660 MW Mohammed bin Rashid Al Maktoum Solar Park could generate additional clean energy exceeding 1,095 GWh annually while reducing water consumption by 17% and carbon emissions by 650,000 tonnes CO2 yearly. The integrated approach addresses critical operational challenges in extreme environments where ambient temperatures exceed 50°C and dust accumulation reduces efficiency by 15-25%. Economic analysis reveals payback periods of 2-4 years through enhanced energy generation, reduced maintenance costs, and extended equipment lifespan. Environmental benefits include substantial water conservation, ecosystem compatibility enhancement, and accelerated decarbonization through improved photovoltaic performance.  Key Contribution: This research demonstrates that synergistic integration of machine learning optimization with thermofluidic nano- enhanced cooling can achieve 14.8% efficiency improvements in hyper-arid photovoltaic installations while reducing operational costs and environmental impact.