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Performance prediction of concentrated photovoltaics with mini-channel and elastic cylinders using ANFIS and sobol sensitivity analysis

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  • Farahani, Somayeh Davoodabadi
  • Hossieni, Mahdi
  • Alizadeh, As'ad

Abstract

This study predicts the performance of a concentrated photovoltaic system with a mini-channel and an elastic cylinder, using an Adaptive Neuro-Fuzzy Inference System and Sobol sensitivity analysis. The concentration ratio of CPV is 10. This study analyzed how different cylinder configurations (stationary vs. elastic) and shapes (circular, rhombus, and square) affect system efficiency under varying radiation intensities and Reynolds numbers (Re). ANSYS Fluent, which applies the finite volume method, was used for modeling. Utilizing a mini-channel can augment the ηel by approximately 3–7.1 % compared to the base case. The results indicate that an increase in radiation intensity raises the solar cell temperature, which leads to a decrease in electrical efficiency (ηel). An increment in the Re of the airflow in the mini-channel improves the ηel by about 4.36 %. The presence of stationary and elastic cylinders can enhance electrical efficiency by 8–12.5 % compared to the base case. Using two elastic cylinders yielded a more even temperature distribution on the CPV. Changing the shape of the cylinder from circular to square and rhombus in the static and elastic states has improved the thermal and electrical efficiency compared to the circular state, and the greatest improvement is attributed to the rhombus-shaped cylinder. Additionally, the ηel was estimated using the ANFIS neural network, and the obtained R-square value of 0.99374 indicates a good estimation of the ηel.

Suggested Citation

  • Farahani, Somayeh Davoodabadi & Hossieni, Mahdi & Alizadeh, As'ad, 2026. "Performance prediction of concentrated photovoltaics with mini-channel and elastic cylinders using ANFIS and sobol sensitivity analysis," Renewable Energy, Elsevier, vol. 256(PA).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pa:s0960148125015538
    DOI: 10.1016/j.renene.2025.123889
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    References listed on IDEAS

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    1. Masalha, Ismail & Alahmer, Ali & Alsabagh, Abdel Salam & Badran, Omar & Masuri, Siti Ujila, 2026. "Predictive analysis of porous media–cooled photovoltaic panels using gradient-boosting machine learning models," Renewable Energy, Elsevier, vol. 260(C).

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