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AbstractNumerical study of the influence of Prandtl number on free convection flow phenomena in a solar collector having glass cover plate and sinusoidal absorber is done. The working fluid is water-Al2O3 nanofluid. The cover plate has initially constant temperature Th, while bottom absorber is at temperature Tc, with Th > Tc. The remaining walls are considered adiabatic. By Penalty Finite Element Method the governing differential equations with boundary conditions are solved. The effect of the Prandtl number on the flow pattern and heat transfer has been depicted. Comprehensive average Nusselt number, average temperature and mean velocity inside the collector are presented as a function of the governing parameter mentioned above. The highest Pr causes the greatest heat transfer. The enhancing performance of heat transfer rate is more effective for the water-Al2O3 nanofluid than the base fluid.

En este trabajo, se ha investigado el impacto de seis capas diferentes de recubrimiento antirreflectante (ARC) utilizando el software de simulación PC1D. La simulación muestra que el rango de 500–700 nm sería adecuado para diseñar un ARCO. Diseñando un ARCO de nitruro de silicio de una sola capa (Si3N4) para una longitud de onda de 600 nm y con un espesor de 74.257 nm, se ha simulado una célula solar de silicio con una eficiencia del 20.35%. Le sigue muy de cerca una célula solar de silicio con una eficiencia del 20,34% con una capa de ARCO de óxido de zinc (ZnO) de 74,87 nm de espesor. Se ha observado un aumento significativo en la eficiencia al aplicar ARC con respecto a no aplicar ningún tipo de ARC. Después de un modelado eficiente de las células solares, se está logrando una eficiencia óptima del 20,67% mediante el uso de la pasivación superficial de SiO2 y la capa de ARCO de Si3N4. Los efectos sobre la tensión, la corriente, la eficiencia fotovoltaica, la reflectividad y la eficiencia cuántica externa debidos a los ARC también están representados en este trabajo. Dans ce travail, l'impact de six couches différentes de revêtement antireflet (ARC) a été étudié à l'aide du logiciel de simulation PC1D. La simulation montre que la plage de 500–700 nm serait appropriée pour concevoir un ARC. En concevant un ARC de nitrure de silicium monocouche (Si3N4) pour une longueur d'onde de 600 nm et une épaisseur de 74,257 nm, une cellule solaire en silicium avec une efficacité de 20,35% a été simulée. Très étroitement suivie par une cellule solaire en silicium à 20,34 % d'efficacité avec une couche d'ARC en oxyde de zinc (ZnO) de 74,87 nm d'épaisseur. Une augmentation significative de l'efficacité a été observée en appliquant L'ARC par rapport à l'absence d'application de tout type d'ARC. Après une modélisation efficace des cellules solaires, une efficacité optimale de 20,67 % est obtenue en utilisant la passivation de surface SiO2 et la couche D'ARC Si3N4. Les effets sur la tension, le courant, l'efficacité photovoltaïque, la réflectivité et l'efficacité quantique externe dus aux ARC sont également représentés dans ce travail. In this work, the impact of six different anti-reflection coating (ARC) layers has been investigated using PC1D simulation software. Simulation shows that the range of 500–700 nm would be suitable for designing an ARC. Designing a single-layer silicon nitride (Si3N4) ARC for 600 nm wavelength and with a thickness of 74.257 nm, a silicon solar cell with 20.35% efficiency has been simulated. Very closely followed by a 20.34% efficient silicon solar cell with 74.87 nm thick zinc oxide (ZnO) ARC layer. Significant increase in efficiency has been observed by applying ARC in respect to not applying any kind of ARC. After efficient solar cell modeling, optimum efficiency of 20.67% is being achieved by using SiO2 surface passivation and Si3N4 ARC layer. The effects on voltage, current, photovoltaic efficiency, reflectivity and external quantum efficiency due to ARCs are also represented in this work. في هذا العمل، تم التحقيق في تأثير ست طبقات مختلفة من الطلاء المضاد للانعكاس (ARC) باستخدام برنامج محاكاة PC1D. تظهر المحاكاة أن النطاق من 500–700 نانومتر سيكون مناسبًا لتصميم القوس. تصميم قوس نيتريد السيليكون أحادي الطبقة (Si3N4) بطول موجي 600 نانومتر وبسمك 74.257 نانومتر، تمت محاكاة خلية شمسية من السيليكون بكفاءة 20.35 ٪. تليها عن كثب خلية شمسية من السيليكون فعالة بنسبة 20.34 ٪ مع طبقة قوسية من أكسيد الزنك بسماكة 74.87 نانومتر (ZnO). لوحظت زيادة كبيرة في الكفاءة من خلال تطبيق القوس فيما يتعلق بعدم تطبيق أي نوع من القوس. بعد نمذجة الخلايا الشمسية بكفاءة، يتم تحقيق الكفاءة المثلى بنسبة 20.67 ٪ باستخدام تخميل سطح SiO2 وطبقة Si3N4 القوسية. يتم تمثيل التأثيرات على الجهد والتيار والكفاءة الكهروضوئية والانعكاسية والكفاءة الكمية الخارجية بسبب ARCs أيضًا في هذا العمل.

More and more renewable energy sources are integrated into power grids, leading to a power electronic-based low-inertia power system. The grid-forming (GFM) inverter is an effective method for improving the inertia of the system. However, with the increased GFM inverters in the system, how the multiple control parameters affect the frequency response is still not clear. In this study, first, the power-phase model of the power grid is established; then, a small-signal distributed frequency model of the GFM inverter-based power system is established associating with the power-phase model of the power grid and the power-frequency model of the GFM inverter. Based on the proposed model, the influence of the multiple parameters to the frequency response is analyzed. It is concluded that both the inertia and damping coefficient affect the settling time, overshoot, and oscillation of the frequency. Finally, the simulation results verify the proposed model and the conclusion.

This paper presents a finite control-set model predictive control (FCS-MPC) based technique to reduce the switching loss and frequency of the on-grid PV inverter by incorporating a switching frequency term in the cost function of the model predictive control (MPC). In the proposed MPC, the control objectives (current and switching frequency) select an optimal switching state for the inverter by minimizing a predefined cost function. The two control objectives are combined with a weighting factor. A trade-off between the switching frequency (average) and total harmonic distortion (THD) of the current was utilized to determine the value of the weighting factor. The switching, conduction, and harmonic losses were determined at the selected value of the weighting factor for both the proposed and conventional FCS-MPC and compared. The system was simulated in MATLAB/Simulink, and a small-scale hardware prototype was built to realize the system and verify the proposal. Considering only 0.25% more current THD, the switching frequency and loss per phase were reduced by 20.62% and 19.78%, respectively. The instantaneous overall power loss was also reduced by 2% due to the addition of a switching frequency term in the cost function which ensures a satisfactory empirical result for an on-grid PV inverter.

Ionic liquids (ILs) are molten salts that are entirely composed of ions and have melting temperatures below 100 °C. When immobilized in polymeric matrices by sol–gel or chemical polymerization, they generate gels known as ion gels, ionogels, ionic gels, and so on, which may be used for a variety of electrochemical applications. One of the most significant research domains for IL-based gels is the energy industry, notably for energy storage and conversion devices, due to rising demand for clean, sustainable, and greener energy. Due to characteristics such as nonvolatility, high thermal stability, and strong ionic conductivity, IL-based gels appear to meet the stringent demands/criteria of these diverse application domains. This article focuses on the synthesis pathways of IL-based gel polymer electrolytes/organic gel electrolytes and their applications in batteries (Li-ion and beyond), fuel cells, and supercapacitors. Furthermore, the limitations and future possibilities of IL-based gels in the aforementioned application domains are discussed to support the speedy evolution of these materials in the appropriate applicable sectors.

AbstractThis study is aimed to apply dry anaerobic digestion (DAD) for methane (CH4) enriched biogas production from unsorted organic municipal solid waste (MSW). Cumulative biogas production was monitored for 35 days of operation in batch digesters at fixed feedstock to inoculum (F/I) ratio 2. Anaerobic sludge (AS) and cow manure (CM) were used as inoculum in single and mixed modes. Several process parameters such as inoculum flow pattern (single layer, multilayer, and spiral), digestion temperature (25 to 40 °C), inoculation modes (single and mixed mode), and inoculation proportion (AS:CM = 1:1, 1:2, 1:3, and 2:1) were investigated to determine the optimum DAD conditions to maximize the CH4 laden biogas yield. The study of inoculum flow pattern showed that digester with multilayer inoculum configuration generated the maximum 555 mL cumulative biogas with the production rate of 195 mL/day (at 25 °C). Biogas production rate and cumulative biogas production were found to increase with a rise in temperature and the maximum values of 380 mL/day and 1515 mL respectively were observed at 37 °C. The mixed mode of inoculation containing AS and CM augmented the biogas yield at previously optimized conditions. Final results showed that digester with multilayer inoculum flow pattern at 37 °C produced 1850 mL cumulative biogas with 1256.58 mL CH4/kg volatile solid (VS) when the mixed inoculum was used at the AS:CM—1:2 ratio. Biogas production with this significant amount of CH4 justifies the use of the DAD process for energy (biogas) generation from widely available biomass feedstock (MSW), offering various advantages to the environment.

This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.