No abstract

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

The present study introduces a fuzzy logic-based expert system (FLES) for evaluating the thermal performance of corrugated plate solar air collector (CPSAC) under different climatic conditions of North Eastern India. The FLES model consists of subtractive clustering with Takagi-Sugeno-Kang fuzzy logic (TSK-FL) model. Work considered mass flow rate (m) 0.0039-0.018 kg/s, collector tilt angles (θ) 30-60°, solar radiation (Q) 230-1086 W/m 2 , ambient temperature (T) 21-34 °C as input and efficiency (η), exergetic efficiency (II), temperature rise (∆T), and pressure drop (∆P) as output parameters for the study. First, 270 trails of experimentation have been carried out on CPSAC by varying the input parameter to get a historical database. Second, modeling of the historical data and optimization of CPSAC parameters have been performed. Third, the parametric analysis is also performed to study the effect of parameters on the thermal performance of CPSAC. Parametric results reveal that η increases with m, while up to a certain value of θ, Q, T. At last, the effectiveness and accuracy of the model is judged via various validation tests with experimental data, published data, and artificially generated data. It is observed that FLES model predicts accuracy results with an accuracy of ≈ 97.5% and optimal conditions are at m = 0.00785 kg/s, θ = 45°, Q = 727 W/m 2 , and T = 29.6 °C, and the corresponding outputs are η = 35.9%, II = 12.8%, ∆T = 34.7 °C and ∆P = 48.8 Pa.

The modeling and optimization of a flat plate solar air collector are investigated experimentally under the climatic conditions of Northeastern India using the integrated fuzzy method (IFM). The IFM consists of a combined subtractive clustering with a fuzzy (Takagi-Kang) method. The subtractive technique is used to find the most favorable IF-THEN rules while the fuzzy method is to optimize/predict the solar air collector parameters. Various governing parameters, such as the mass flow rate of air, collector tilt angle, solar radiation, and ambient temperature, are used as the input parameters while the thermal efficiency, exergetic efficiency, temperature rise, and pressure drop are the output parameters. First, experiments on solar air collectors are performed by varying the input parameters. Then, optimization, prediction, and parametric analyses are conducted. Finally, the proposed results are validated using confirmatory tests with the experimental data, published data, and artificially generated data. The accuracy of the obtained result for the solar air collector is found to be ∼97.5% and the best possible set of governing parameters are a mass flow rate of 0.00785 kg/s, tilt angle of 45°, solar radiation of 450 W/m2, and a temperature of 27 °C. The corresponding outputs are the efficiency at 28.88% and the exergetic efficiency at 5.15%.

No abstract