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The battery temperature is one of the important factor that affects the performance of the battery and lifetime. To overcome these issues, the need and usage of an effective battery thermal management system (BTMS) is increased in recent years. The results of BTMS with conventional heat transfer fluids appears in many articles but the very few researchers developed and studied the performance of refrigerant based BTMS. In this research, a novel BTMS is developed and detailed study is conducted to analyze the impact of CuO based refrigerant (R134a) on its performance. Different quantity of CuO nanoparticles mixed with base refrigerant (R134a) and used as a heat transfer fluid. Initially, five different volume proportions (i.e. ɸ – 0.01, 0.02, 0.03, 0.04 and 0.05%) of CuO-R134a nano refrigerants are prepared and the thermophysical properties are studied. The test results reveal that the peak thermal conductivity and viscosity of about 1.28 W/mK and 0.00047 Pa-S is obtained for refrigerant with 0.05 vol.% CuO nanoparticle at 325 K and 300 K respectively. The average cell temperature of about 51 °C, 37 °C, 33 °C and 32 °C is observed for battery without cooling system, BTMS with R134a, 0.01% and 0.05% CuO/R134a respectively. From the results of this study, it can be suggested that the CuO- R134a nano refrigerant will be a promising cooling medium in the battery cooling system.

Approximate (Inexact) computing is best suitable for the applications where they can accept the results with acceptable loss. We have also introduced three new models of inaccurate 4-2 compressors in this paper, using the current inaccurate 4-2 compressors. All proposed work results are measured in a standard CMOS technology of 90 nm and compared to previously published work. The proposed compressors provide the promised results in terms of error and electrical performance as compared with its counterpart. In order to validate our results, we have also performed image sharpening and image smoothening by using our proposed multipliers. The Structural Similarity Index Matrix (SSIM) of proposed multipliers are measured and interesting results were found.

This study aims to investigate the performance of the Photovoltaic Thermal (PVT) collector based hydrogen production system. For this purpose, a solar assisted water splitting system is fabricated. This system comprises the array of photovoltaic (PV) cells with 0.303 m2 surface area, a spiral flow thermal collector with 12.7 mm outer diameter, 10.26 mm internal diameter, 10 m length copper tube and Hoffman voltameter. The results have been taken for three different mass flow rates (0.008, 0.01 and 0.011 kg/s) and compared with the reference PV module. This study results clearly show that the collector outlet temperature, output voltage and output power increase as the flow rate increases and the PV module temperature decreases with an increase of flow rate. The maximum thermal and electrical efficiency of 33.8% and 8.5% are observed for water based PVT solar collector with 0.011 kg/s flow rate at 12.00. It is also noted that the hydrogen yield rate increases significantly with an increase in flow rate. The highest hydrogen yields of 17.1 ml/min are obtained at a fluid flow rate of 0.011 kg/sec at 12.00.

© 2022 Elsevier LtdIn order to meet the energy demand and reduce the environmental pollution, it is necessary utilize photovoltaic (PV) module to produce electrical energy. There are many factors such as solar radiation, PV module material, wind energy, soiling factor, shading effect, geographical location, cell temperature is affecting the performance of the PV module. In this context, the performance of the multi crystalline based two PV plants has been examined. In this work, test is carried out in two solar plants installed at Kastoria, Greece (500kW, ground mounted) and Kocaeli, Turkey (110kW, roof mounted) and the results are compared with PVSYST software simulation results. From the results, it is found that the temperature losses for Kastoria and Kocaeli plants are obtained as 7.9% and 7.6% respectively. This leads to increase the performance ratio of 79% and 72.2% for Kastoria and Kocaeli plants respectively. Similarly, the maximum energy output is obtained for PVGIS database for all three models (Liu Jordan model, Hay Davies model and Perez model). The Internal Rate of Return for Kocaeli plant is 7.08% and for Kastoria plant is 6.71%. In addition, the Net Present Value for Kastoria and Kocaeli plant is obtained as 821900€ and 174072€ respectively.

In this article, an experimental work was carried out to investigate the thermal conductivity of CuO/water and Al2O3/water nanofluids with and without surfactants. Three different volume concentrations of nanofluids (0.05, 0.1 and 0.2%) prepared by using single step preparation method. Sodium dodecylbenzene sulfonate used as a surfactant in this study. The analysis is carried out on the effects of sonication time and thermal conductivity of the prepared nanofluids by varying temperature from 30°C to 60°C. The obtained results indicate that the thermal conductivity of nanofluids increased with an increase of sonication time, temperature and the amount of surfactant. Also, the maximum thermal conductivity value was obtained for 0.2% volume concentration of CuO/water nanofluid with SDBS surfactant.

This article reports the effects of CuO/water based coolant on specific fuel consumption and exhaust emissions of four stroke single cylinder diesel engine. The CuO nanoparticles of 27 nm were used to prepare the nanofluid-based engine coolant. Three different volume concentrations (i.e 0.05%, 0.1%, and 0.2%) of CuO/water nanofluids were prepared by using two-step method. The purpose of this study is to investigate the exhaust emissions (NOx), exhaust gas temperature and specific fuel consumption under different load conditions with CuO/water nanofluid. After a series of experiments, it was observed that the CuO/water nanofluids, even at low volume concentrations, have a significant influence on exhaust emissions. The experimental results revealed that, at full load condition, the specific fuel consumption was reduced by 8.6%, 15.1% and 21.1% for the addition of 0.05%, 0.1% and 0.2% CuO nanoparticles with water, respectively. Also, the emission tests were concluded that 881 ppm, 853 ppm and 833 ppm of NOx emissions were observed at high load with 0.05%, 0.1% and 0.2% volume concentrations of CuO/water nanofluids, respectively.

Artificial intelligence techniques have become powerful alternatives to conventional modeling techniques in different engineering disciplines. They have been applied for modeling, control, prediction, optimization, forecasting, and identification of complex systems. In this paper, a novel optimized artificial intelligence method is developed to predict the performance of Photovoltaic/Thermal Collector (PVTC) incorporated with Electrolytic Hydrogen Production (EHP) system in terms of power output of PV, PV surface cell temperature, output temperature of cooling fluid, thermal and electrical efficiency, and hydrogen production yield. A new metaheuristic algorithm called mayfly based optimization (MO) algorithm has been implemented with Random Vector Functional Link (RVFL) network to maximize the prediction accuracy. The proposed hybrid artificial intelligence model was trained and tested using experimental data. The experiments were conducted outdoors for the proposed PVTC-EHP system operating with two different cooling fluids, namely, air and water under Indian weather conditions, and their results were compared with the predicted RVFL-MO and conventional RVFL results. Moreover, five statistical criteria were used to evaluate the performance of the investigated algorithms. The experimental results showed the hybrid PVTC-EHP system can produce a daily accumulated PV output power and hydrogen production yield of 1.66 kW/day and 3.60 kg/day for water-based PVTC-EHP system and 1.22 kW/day and 4.41 kg/day for air-based PVTC-EHP system, respectively, at a mass flow rate of 0.66 kg/min. Moreover, the statistical measures showed a perfect fit between the experimental and the proposed prediction model results. The results revealed that the root mean square error for the training phase of the RVFL and RVFL-MO was 0.25 and 0.65, respectively, while it was 1.63 and 2.04 for the testing phase, which reveals the important role of MO in determining the best parameters of RVFL network that maximize its prediction performance.

TIn this present study, the forced convection heat transfer performance of different fluids, namely, Al2O3-water, and CuO-water nanofluid has been studied experimentally in an automobile radiator. Three different concentration of nanofluid in the range of 0-1.0 vol.% have been prepared by addition of Al2O3 and CuO nanoparticles into water. The test fluid flow rate can be varied in the range of 2 l/min to 5 l/min to have a fully turbulent regime. Obtained results demonstrate that the fluid circulating rate can improve the heat transfer performance. The heat transfer performance of CuO-water nanofluid was found better than the other heat transfer fluids. Furthermore, the Nusselt number is found to increase with the increase in the nanoparticle concentration and nanofluid velocity.

In recent years fluids containing suspension of nanometer sized particles have been an active area of research due to their enhanced thermo physical properties over the base fluids like water, oil etc. Nanofluids possess immense potential applications to improve heat transfer and energy efficient in several areas including automobile, micro electronics, nuclear, space and power generation. Nowadays most of the researchers are trying to use the nanofluids in automobile for various applications such as coolant, fuel additives, lubricant, shock absorber and refrigerant. The goal of this paper is to create the awareness on the promise of nanofluids and the impact it will have on the future automotive industry. This paper also presents a comprehensive data of nanofluids application in automobile for various aspects.

In this paper, the PV/T (Photovoltaic thermal unit) system is investigated experimentally to examine the thermal, electrical and overall efficiency by circulating Al2O3/water nanofluid of 1wt% and 2wt% with an optimum flow rate of 40L/H. The overall efficiency of PVT system is largely influenced by various factors such as heat due to photovoltaic action; energy radiated at the infrared wavelength of the solar spectrum, solar irradiance, mounting structure, tilt angle, wind speed direction, Ambient temperature and panel material composition. However, the major factor is considered in this study to extract the heat generated in the PV panel by using nanofluid as a coolant to increase the overall system efficiency. Therefore, the result shows that by using 2 wt% Al2O3/water nanofluid the electrical efficiency, thermal efficiency and overall efficiency of the PVT system enhanced by 13%, 45%, and 58% respectively compared with water.