Thermal performance prediction of a V-trough solar water heater with a modified twisted tape using ANFIS, G.L.R., R.T. and SVM models of machine learning

A Saravanan; S Rama Sree; M Sreenivasa Reddy; Elumalai Pv; Krishnasamy Karthik; Ashok Kumar Cheeli; Nasim Hasan

doi:10.1038/s41598-024-75907-y

Thermal performance prediction of a V-trough solar water heater with a modified twisted tape using ANFIS, G.L.R., R.T. and SVM models of machine learning

Sci Rep. 2024 Nov 8;14(1):27206. doi: 10.1038/s41598-024-75907-y.

Authors

A Saravanan¹, S Rama Sree², M Sreenivasa Reddy³, Elumalai Pv³, Krishnasamy Karthik⁴, Ashok Kumar Cheeli⁵, Nasim Hasan⁶

Affiliations

¹ Department of Mechanical Engineering, Aditya University, Surampalem, 533 437, Andhra Pradesh, India. saran_thermal@yahoo.co.in.
² Department of Computer Science and Engineering, Aditya University, Surampalem, 533 437, India.
³ Department of Mechanical Engineering, Aditya University, Surampalem, 533 437, Andhra Pradesh, India.
⁴ Deparment of Mechanical Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, 600 062, India.
⁵ Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderbad, India.
⁶ Department of Mechanical Engineering, Mettu University, Metu, Ethiopia. nasimhasan78@gmail.com.

Abstract

Four distinct neural models were used to evaluate the efficiency of a V-trough solar water heater (VTSWH) equipped with square-cut twisted tape (SCTT) and V-cut twisted tape (VCTT) at two different twist ratios, 3 and 5. The objective of this study was the use of ANFIS (Adaptive Neuro-Fuzzy Inference System), G.L.R. (Generalised linear regression), R.T. (Regression tree), and SVM (Support Vector Machine). A total of 162 data sets were acquired for these models through a variety of trials. Outdoor experiments were done using a twist ratio of Y = 3 and Y = 5, using both SCTT and VCTT. The models included eight distinct variables: ambient temperature, water mass flow rate, water intake temperature, water exit temperature, absorber plate temperature, tube temperature, solar intensity, and twist ratio. The dependent variables in this study are the Nusselt number (Nu), friction factor (FF), and efficiency (η). 130 datasets were chosen for training purposes, while 32 were used for testing. Using the ANFIS, G.L.R., R.T., and SVM techniques, the correlation coefficient (R²) values for Nusselt number were 0.9990, 0.9961, 0.9562, and 0.9280 for friction factor 0.9966, 0.9683, 0.9810, and 0.9560, and for efficiency 0.9997, 0.9976, 0.9845, and 0.9614, respectively. Comparing all models shows that ANFIS is the most effective of the four strategies studied. The ANFIS model outperformed the other models regarding Nu, FF, and η, with RMSE values of 0.0805, 0.0.0004, and 0.4534. According to the above data, the VTSWH thermal performance predicted using the ANFIS approach has the highest accuracy.

Keywords: Adaptive neuro-fuzzy inference system; Generalised linear regression; Machine learning; Regression tree; Solar water heater.