Dipak Kumar Mandal; Vaishali Vaishali; Kritesh Kumar Gupta; Nirmalendu Biswas; Nirmal Kumar Manna; Somnath Santra; Erdem Cüce · 10.1016/j.energy.2025.136744 https://doi.org/10.1016/j.energy.2025.136744
Energy
A Novel Comparative Study of Machine Learning Surrogate Models for Solar Chimney (SC) Plant Performance Evaluation: Thermo-Physical Insights Dipak Kumar Mandal; Vaishali Vaishali; Kritesh Kumar Gupta; Nirmalendu Biswas; Nirmal Kumar Manna; Somnath Santra; Erdem Cuce Finite-volume simulations of the Manzanares solar chimney plant test how collector inlet height, chimney diameter and chimney divergence affect power output at different irradiance levels, and several machine learning surrogates are compared. The best geometry depends on irradiance. At 1000 W/m2, widening the chimney to about 45 m raises power by roughly 318%, and Gaussian process regression predicts performance most accurately.
Machine learning / AI
Numerical / CFD
Solar chimney power plant
2025
Energy
no
2025-05-22 12:00:00 00001782
a novel comparative study of machine learning surrogate models for solar chimney (sc) plant performance evaluation: thermo-physical insights
2025
Energy
Dipak Kumar Mandal; Vaishali Vaishali; Kritesh Kumar Gupta; Nirmalendu Biswas; Nirmal Kumar Manna; Somnath Santra; Erdem Cüce
10.1016/j.energy.2025.136744
Summary
Finite-volume simulations of the Manzanares solar chimney plant test how collector inlet height, chimney diameter and chimney divergence affect power output at different irradiance levels, and several machine learning surrogates are compared. The best geometry depends on irradiance. At 1000 W/m², widening the chimney to about 45 m raises power by roughly 318%, and Gaussian process regression predicts performance most accurately.
Description written by Solar Tower. The original abstract is available from the publisher via the link below.
Open source