• Energy Research

Abstract The current work analyses the thermal (ηth) and effective efficiency (${\eta}_{\mathrm{eff}}$) of a solar thermal air collector (STAC) that has an arc-shaped dimple as a roughness geometry on the absorber plate. Nusselt number (Nu) and friction factor (ff) were computed for roughness geometry during the testing, which was done on STAC. Additionally, for different roughness values, the correlations for Nu and ff were developed and further used in this study. The temperature rise parameter and a parametric design are used to assess these efficiencies. The influence of design variables on STAC performance is analyzed using a numerical model based on thermal and effective evaluations. During the investigation, parameters such as relative roughness height (e/Dh) varied from 0.021 to 0.036, relative roughness pitch (p/e) from 10 to 20, arc angle (α) from 45 to 60°, temperature rise parameter from 0.003 to 0.02 and Reynolds number (Re) from 3000 to 48 000 at a constant solar intensity (I = 1000 W/m2). The ηth and ${\eta}_{\mathrm{eff}}$ are observed to be 85% and 78%, respectively, at the optimum values of roughness parameters, i.e. e/Dh = 0.036, p/e = 10, and α = 60°. The curves have been plotted between each of the roughness parameters and Re in order to evaluate the best ηth and ${\eta}_{\mathrm{eff}}$ . The research emphasizes the usefulness of MATLAB for STAC analysis and optimization, roughness parameters of the suggested collector design, by integrating simulation and experimental data.

Alarming environmental changes and the threat of natural fuel resource extinction are concerning issues in human development. This has increased scientists’ efforts to phase out traditional energy resources and move on to environmentally friendly biofuels. In this study, non-edible castor oil was transesterified with methanol using a manganese-doped zinc oxide (Mn-doped ZnO) nanocatalyst. A heterogeneous nanocatalyst was prepared by means of the the sonochemical method. X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDX), and transmission electron microscopy (TEM) were used to characterize these nanocatalysts. The transesterification reaction was studied under different temperature conditions, different ratios of methyl alcohol to castor oil, and different amounts of the catalyst to identify optimum conditions in which the maximum yield of biodiesel was produced. The maximum biodiesel yield (90.3%) was observed at 55 °C with an oil-to-methanol ratio of 1:12, and with 1.2 g of nanocatalyst. The first-order kinetic model was found to be the most suitable. Several thermodynamic parameters were also determined, such as activation energy, enthalpy, and entropy. We found that this transesterification was an endergonic and entropy-driven reaction. The results showed that the Mn-doped ZnO nanocatalyst could be a suitable catalyst for the heterogeneous catalytic transesterification process, which is essential for biodiesel production.

There are two sorts of energy resources: sustainable power resources and non-sustainable power resources. Due to some negative ecological effects including air pollution, climate change, and resource rot, people are concentrating on using sustainable energy resources to produce electricity. Solar energy, usually referred to as sun-oriented energy, is one of the most frequently researched environmentally beneficial power resources. In order to fulfill the growing demand for energy and increase energy efficiency, new developments and advancements in the field of solar energy are required. There are two sorts of energy resources: sustainable power resources and non-sustainable power resources. Due to some negative ecological effects including air pollution, climate change, and resource rot, people are concentrating on using sustainable energy resources to produce electricity. Solar energy, usually referred to as sun-oriented energy, is one of the most frequently researched environmentally beneficial power resources. In order to fulfill the growing demand for energy and increase energy efficiency, new developments and advancements in the field of solar energy are required. The traditional solar energy cell's inability to create power in the evening is a horrible flaw. This investigation focuses mostly on solar-powered energy and discusses its evolution, improvements, and future perspectives.

The reliability of fuel cells during testing is crucial for their development on test benches. For the development of fuel cells on test benches, it is essential to maintain their dependability during testing. It is only possible for the alarm module of the control software to identify the most serious failures because of the large operating parameter range of a fuel cell. This study presents a novel approach to monitoring fuel cell stacks during testing that relies on machine learning to ensure precise outcomes. The use of machine learning to track fuel cell operating variables can achieve improvements in performance, economy, and reliability. ML enables intelligent decision-making for efficient fuel cell operation in varied and dynamic environments through the power of data analytics and pattern recognition. Evaluating the performance of fuel cells is the first and most important step in establishing their reliability and durability. This introduces methods that track the fuel cell's performance using digital twins and clustering-based approaches to monitor the test bench's operating circumstances. The only way to detect the rate of accelerated degradation in the test scenarios is by using the digital twin LSTM-NN model that is used to evaluate fuel cell performance. The proposed methods demonstrate their ability to detect discrepancies that the state-of-the-art test bench monitoring system overlooked, using real-world test data. An automated monitoring method can be used at a testing facility to accurately track the operation of fuel cells.

Nowadays majority of the college students' physical condition is worrying. They are not physically and also mentally healthy. If so, why? Their selection of foods is not consistent. Thus, they are more likely to suffer from chronic illnesses such as diabetes, hypertension, stress, etc. in the future. Awareness should be created to prevent such diseases before they occur. Physiological parameters measured included Systolic (SBP) and Diastolic (DBP) Blood Pressure, Body mass Index (BMI), Blood Serum Cholesterol (BSC), and percentage of Body Fat (%BF). These parameters are retrieved and classified to check the physical health or predict if any abnormalities are found in the health condition of college students. Therefore, to predict and classify their health status using Breiman's Random Forest (RF) Algorithm is proposed in this paper. Of all the classification methods available, random forests offer the greatest accuracy. Random forest method also handles large data with thousands of variables. When a class is more sparse than further classes in the data it can spontaneously balance the data sets. The outcome shows that the proposed Random Forest algorithm is accurate in predicting and checking the health condition of students. Students' physical condition should be diagnosed through this method. By knowing the healthy body parameters of the students, a physician can know whether they are healthy or not.