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Solar energy is a widely used, cost-effective, environmentally friendly, clean, and carbon-free renewable energy source that reduces reliance on coal, electricity, and other sources. It is considered the most valuable among all renewable energy sources available. Solar energy is widely used for a range of applications, including water heating in domestic and industrial settings, with flat-plate solar collectors (FPSCs) being a popular choice due to their simple design, ease of installation and operation, and lower maintenance requirements compared to other types of solar collectors. This review attempts to provide attention to the up-to-date technological advancement and enhancement in the performance of water heater using design features of FPSC such as flow tube design and shape of collector, absorber coating and material, glazing material, and insulation used. This review aims to offer a useful method for assessing and contrasting various methods of heat transfer enhancement, ultimately leading to the selection of the most suitable option. Additionally, the study highlights the research gaps and suggests potential areas for future improvement, which is expected to assist researchers in advancing this technology further. [ABSTRACT FROM AUTHOR]

Hybrid energy in electric vehicles has been globally accepted as the fossil fuels will be exhausted by the middle or end of 21st century. The hybrid vehicles have emerged as a major source of clean and renewable energy to support the never-ending demand of vehicles. The main idea of the paper is the use of hybrid energy in working of e-rickshaw, which has become the most favorite area of research in automobile industry, recently. The proposed model is powered by lead-acid battery and this battery is charged by either using electric plug or solar panel. In the present study, hardware model has been analyzed e-rickshaw in terms of different parameters like mileage, charging and discharging time of battery, charging current, discharging current from the performance as well as security point of view. The hardware model has been developed with Photovoltaic (PV) module, capacitor bank, and Arduino circuit. Here, a modified e-rickshaw has been developed which has improved vehicle performance as compared to existing conventional e-rickshaw. The mileage has improved from 80 to 120.61 km and starting current has been reduced from 57 to 41 A on full load conditions. This has enhanced the battery life and Arduino circuit was implemented to enhance security features. The proposed model of e-rickshaw has improved vehicle performance as compared to existing conventional e-rickshaw in terms of mileage, starting current, battery life, and security feature of the vehicle. [ABSTRACT FROM AUTHOR]

Solar air heater (SAH) is an important device for solar energy utilization which is used for space heating, crop drying, timber seasoning etc. Its performance mainly depends on system parameters, operating parameters and meteorological parameters. Many researchers have been used these parameters to predict the performance of SAH by analytical or conventional approach and artificial neural network (ANN) technique, but performance prediction of SAH by using relevant input parameters has not been done so far. Therefore, relevant input parameters have been considered in this study. Total ten parameters were used such as mass flow rate, ambient temperature, wind speed, relative humidity, fluid inlet temperature, fluid mean temperature, plate temperature, wind direction, solar elevation and solar intensity to find out the relevant parameters for ANN prediction. Seven different neural models have been constructed using these parameters. In each model 10 to 20 neurons have been selected to find out the optimal model. The optimal neural models for ANN-I, ANN-II, ANN-III, ANN-IV, ANN-V, ANN-VI and ANN-VII were obtained as 10-17-1, 8-14-1, 6-16-1, 5- 14-1, 4-17-1, 3-16-1 and 2-14-1, respectively. It has been found that ANN-II model with 8-14-1 is the optimal model as compared to other neural models. Values of the sum of squared errors, mean relative error, and coefficient of determination were found to be 0.02138, 1.82% and 0.99387, respectively, which shows that the ANN-II developed with mass flow rate, ambient temperature, inlet and mean temperature of air, plate temperature, wind speed and direction, relative humidity, and relevant input parameters performed better. The above results show that these eight parameters are relevant for prediction. [ABSTRACT FROM AUTHOR]

For economic growth of nation, the energy plays an important role. The excessive use of fossil fuels results the increase in global warming and depleting the resources. Due to this reason, the renewable energy sources are creating more attraction for researchers. In renewable energy sector, solar energy is the most abundant and clean source of energy. In solar thermal systems, solar air heater (SAH) is the main system which is used for heating of air. As it is simple in construction and cheaper in cost, it is of main interest for the researchers. The concept of first law and second law of thermodynamics is used for the study of the energy and exergy analysis respectively. The energy analysis is of great importance for the study of process effectiveness while the exergetic analysis is another significant concept to examine the actual behavior of process involving various energy losses and internal irreversibility. For efficient utilization of solar energy, the exergy analysis is very important tool for optimal design of solar air heaters. The aim of the present work is to review the works related to energy and exergy analysis of various types of solar air heaters and to find out the research gap for future work. [ABSTRACT FROM AUTHOR]

The objective of present work is to predict the thermal performance of wire screen porous bed solar air heater using artificial neural network (ANN) technique. This paper also describes the experimental study of porous bed solar air heaters (SAH). Analysis has been performed for two types of porous bed solar air heaters: unidirectional flow and cross flow. The actual experimental data for thermal efficiency of these solar air heaters have been used for developing ANN model and trained with Levenberg-Marquardt (LM) learning algorithm. For an optimal topology the number of neurons in hidden layer is found thirteen (LM-13).The actual experimental values of thermal efficiency of porous bed solar air heaters have been compared with the ANN predicted values. The value of coeffi- cient of determination of proposed network is found as 0.9994 and 0.9964 for unidirectional flow and cross flow types of collector respectively at LM-13. For unidirectional flow SAH, the values of root mean square error, mean absolute error and mean relative percentage error are found to be 0.16359, 0.104235 and 0.24676 respectively, whereas, for cross flow SAH, these values are 0.27693, 0.03428, and 0.36213 respectively. It is concluded that the ANN can be used as an appropriate method for the prediction of thermal performance of porous bed solar air heaters. [ABSTRACT FROM AUTHOR]