Solar Radiation Parameters Research Articles

Features of melting heat transfer in magnetized squeezing radiative flow of ternary hybrid nanofluid

The increasing need for a steady energy supply to enhance efficiency is progressively growing in both residential and manufacturing industries. This demand can be addressed by utilizing advanced technologies like a melting heat generator to produce significant amounts of heat and prevent overheating. Based on the above applications of melting heat, the consequences of melting heat transfer induction on ternary hybrid nanofluid (T-HNF) exposed to solar radiation mechanism in an erratic squeezing flow are considered. The nanoparticles Copper (Cu), Silicon dioxide (SiO2), Zirconium dioxide (ZrO2) are immersed in base fluid Engine oil (EO) resulting in T-HNF (Cu + SiO2 + ZrO2/EO). The model equation also takes into account the entropy generation minimization and Bejan number. Both the spectral collocation technique (SCT) and finite element scheme (FES) are applied to solve the ordinary differential equations (ODEs) through the Mathematica package. Our results reveal that the impact of three-component nanoparticles increases, while the solar radiation parameter raises the energy profile. Also, an increase in the magnetic field deteriorates the velocity distribution. The research has various potential applications, such as in ice and snow melting, solar thermal energy storage, and the food industry.

Hybrid Nanofluid Flow over a Shrinking Darcy-Forchheimer Porous Medium with Shape Factor and Solar Radiation: A Stability Analysis

This research aimed to develop a numerical solution to analyze the effects of solar radiation and nanoparticle shape factors on the flow of a hybrid nanofluid past a shrinking Darcy-Forchheimer porous medium. The base fluid chosen for this study is water (H2O), and the hybrid nanofluid consists of nanoparticles of silver (Ag) and titanium dioxide (TiO2) in four different shapes: bricks, cylinders, platelets, and blades. To account for solar radiation, the energy model incorporated a radiative heat flux, while the momentum problem considers the influence of a magnetic field. The application of an appropriate similarity transformation method converts the partial differential equations (PDEs) model into a system of nonlinear ordinary differential equations (ODEs). The mathematical model is solved using the shooting technique method and the bvp4c solver. The obtained results, along with the effects of the nanoparticle shape factor, solar radiation parameter, shrinking parameter, Darcy-Forchheimer number, and nanofluid volume fraction, are visually presented through figures and tables. It is worth noting that, in our numerical results, we observed the presence of dual solutions when λ < 0. Our findings indicate that the thermal transmittance increases with an increase in the nanoparticle shape factor and solar radiative parameter. Additionally, we observed an escalation in the velocity distribution in relation to the shrinking parameter and nanofluid volume fraction. Before reaching the two solutions, a flow stability analysis revealed that the first branch appears to be the most stable. Overall, these findings provide valuable insights into the behaviour of hybrid nanofluid flow in the presence of solar radiation and porous media.

2019–2020年西安城市PM2.5、气体污染物及气象数据集

Air pollution in China has improved in recent years, but Central Shaanxi region continues to face great pressure and challenges in pollution control due to its unique geographical location and climatic conditions. The regional and complex air pollution, characterized by ozone (O3) and fine particulate matter (PM2.5), is becoming increasingly serious. More meticulous observation data are needed to clarify the causes of air pollution and provide scientific support for control policies. This dataset comprises the data on the concentrations of nitrogen oxides, sulfur dioxide, ozone and carbon monoxide in the atmosphere obtained by gas analyzer in Gaoxinzi Observation Site of the National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in Guanzhong Plain. Weather stations collected data on temperature, humidity, air pressure, solar radiation, wind speed, wind direction and other meteorological parameters around the station. Offline PM2.5 filter membranes were measured and the mass concentrations were analyzed. The observation site is located in the urban area of Xi 'an, Shaanxi Province. The data covers the period from January 2019 to December 2020 with a resolution of 5 minutes for pollution gas data, one minute for meteorological data, and PM2.5 collected every 24 hours. Online instruments are calibrated monthly to ensure data quality, while the offline PM2.5 sampler is calibrated and maintained weekly to ensure its data quality. This dataset can provide fundamental data records for the atmospheric environment in Central Shaanxi Province and offer data support for further pollution evaluation, control, and treatment. Moreover, it can give researchers scientific insights into the processes, casual mechanisms, as well as prevention and control of atmospheric pollution in Central Shaanxi Province.

Short-term forecasting of surface solar incident radiation on edge intelligence based on AttUNet

Solar energy has emerged as a key industry in the field of renewable energy due to its universality, harmlessness, and sustainability. Accurate prediction of solar radiation is crucial for optimizing the economic benefits of photovoltaic power plants. In this paper, we propose a novel spatiotemporal attention mechanism model based on an encoder-translator-decoder architecture. Our model is built upon a temporal AttUNet network and incorporates an auxiliary attention branch to enhance the extraction of spatiotemporal correlation information from input images. And utilize the powerful ability of edge intelligence to process meteorological data and solar radiation parameters in real-time, adjust the prediction model in real-time, thereby improving the real-time performance of prediction. The dataset utilized in this study is sourced from the total surface solar incident radiation (SSI) product provided by the geostationary meteorological satellite FY4A. After experiments, the SSIM has been improved to 0.86. Compared with other existing models, our model has obvious advantages and has great prospects for short-term prediction of surface solar incident radiation.

Thermal performance investigation of membrane-assisted radiant cooling system for localised outdoor cooling hub

Heat waves pose increasing threats to human well-being, especially in urban areas, and staying safe beyond comfort during heat waves is a crucial issue. Localised cooling hubs may provide thermal stress relief in the outdoor environment. For outdoor cooling applications, conventional air conditioning systems are not effective but energy-intensive, and the membrane-assisted radiant cooling system can be a potential alternative, which directly treats the radiant loads while minimising the convective heat loss to the ambient. In the present study, a numerical model is developed to analyse the heat transfer behavior of membrane-assisted radiant cooling for outdoor applications, considering the effect of solar and long-wave radiation and wind parameters. As the transmissivity of the membrane material plays a crucial role in reducing the amount of solar heat, the performances of three types of membranes, i.e., non-selective, mid-infrared selective, and sky-window selective, were compared and analysed at different times on a typical summer day in Hong Kong. The results indicate that the sky-window selective membrane, which allows only transmission of infrared in the 8–15 µm wavelength range, saves energy up to 44 % and 47% compared with mid-infrared selective and non-selective membranes, respectively. In the outdoor application, the equivalent convective heat transfer coefficient of the cooling panel can be reduced in the range of 2.2 W/m2K to 2.6 W/m2K. It is observed that, when the panel is not exposed to direct solar radiation, there is no advantage in using the mid-infrared selective membrane over the non-selective membrane. However, under direct solar radiation, a non-selective membrane-assisted cooling panel demonstrates poor performance, absorbing solar heat flux 2.27 times higher compared to MIR-selective membrane-assisted panel.

Using ensemble machine learning algorithm to predict forest fire occurrence probability in Madhya Pradesh and Chhattisgarh, India

The escalating global threat of forest fires, driven by global warming, requires the development of effective prediction systems to mitigate damages. This research focuses on Madhya Pradesh (MP) and Chhattisgarh (CG) states in central India, where forest fire risk has become particularly pronounced. The primary objectives of the study are to quantify and map the spatial and temporal dynamics of forest fires over the period 2001 to 2020, and to predict future fire risks using satellite derived datasets and machine learning techniques. Through a long-term analysis, the study revealed an alarming increase in the number of forest fire incidents in MP and CG. From an average of 1200 and 1000 during 2001 to 2005, the incidents increased to 2800 and 2100 during 2016 to 2020, in MP and CG respectively. To predict forest fire risk, Random Forest machine learning algorithm was adopted utilizing various satellite derived climatic, topographical, and ecological parameters such as temperature, precipitation, solar radiation, NDVI, soil moisture, litter availability, evapotranspiration and terrain parameters (at monthly scale for 20 years). While forecasting fire probability for 2018–2020, the model achieves high accuracy rate of 86.46 % in MP and 93.78 % in CG. The results highlight significant forest fire likelihood regions in the central MP and the Southern CG, identifying areas requiring enhanced fire management strategies. This study has revealed that NDVI and rainfall have played a positive role in restricting the forest fire, and their negative anomaly amplified the fire risk. The study would help forest planners and administrators to characterise vulnerable areas and prioritise their conservation provisions.

Towards global coverage of gridded parameterization for CLImate GENerator (CLIGEN)

ABSTRACT Stochastic weather generators create time series that reproduce key weather dynamics present in long-term observations. The dataset detailed herein is a large-scale gridded parameterization for CLImate GENerator (CLIGEN) that fills spatial gaps in the coverage of existing regional CLIGEN parameterizations, thereby obtaining near-global availability of combined coverages. This dataset primarily covers countries north of 40° latitude with 0.25° spatial resolution. Various CLIGEN parameters were estimated based on 20-year records from four popular global climate products. Precipitation parameters were statistically downscaled to estimate point-scale values, while point-scale temperature and solar radiation parameters were approximated by direct calculation from high-resolution datasets. Surrogate parameter values were used in some cases, such as with wind parameters. Cross-validation was done to assess the downscaling approach for six precipitation parameters using known point-scale values from ground-based CLIGEN parameterizations. These parameter values were derived from daily accumulation records at 7,281 stations and high temporal resolution records at 609 stations. Two sensitive parameters, monthly average storm accumulation and maximum 30-minute intensity, were shown have RMSE values of 1.48 mm and 4.67 mm hr−1, respectively. Cumulative precipitation and the annual number of days with precipitation occurrence were both within 5% of ground-based parameterizations, effectively improving climate data availability.

Comparison of the variation of solar radiation and atmospheric parameters in Kano before, during and after covid-19 lockdown

This study compared the variation of solar radiation and atmospheric parameters in Kano, Nigeria before, during, and after the COVID-19 (2019, 2020, and 2021) lockdown. Data was obtained from the Nigeria Meteorological Center (NIMET) in Abuja and MINITAB's time series method and procedures were used to analyze the data. Results showed that Kano witnessed an increased amount of solar radiation during COVID-19 (2020) when compared with the values of solar radiation distribution observed before and after COVID-19 (2019 and 2021). Atmospheric ozone increased slightly in Kano during the period of COVID-19 when compared with the periods before and after COVID-19 in the same area. Relative humidity variations were observed to be lower before and during COVID-19 when compared with the values after COVID-19, whereas temperature and pressure variation distributions were maintained approximately within the same range in the years of study (2019, 2020, and 2021). These findings provide an insight into the effects of the COVID-19 lockdown on solar radiation and atmospheric parameters in Kano.

Empirical Models of Respiration and Net Ecosystem Productivity and Their Applications in a Subtropical Coniferous Plantation in China

Net ecosystem exchange (NEE), solar radiation (including photosynthetically active radiation PAR), and meteorological parameters were measured in a subtropical coniferous plantation in China during 2013–2016. Applying the PAR balance principle at a canopy level and analyzing the observation data, an empirical model of respiration (Re, EMRe) considering 3-factor and 2-factor situations was developed and tested for all sky conditions. Generally, the respiration simulations were in reasonable agreement with the observations for the hourly, monthly, and annual sums of respiration. For example, using 3-factor and 2-factor models, the estimated annual sums of daytime and nighttime respiration in 2013–2016 overestimated that which was observed by about 31% and 26%, respectively. Further applications of EMRe and an empirical model of gross primary production (GPP, EMGPP) developed previously at this site, and an empirical model of net ecosystem productivity (NEP, EMNEP) using 3-factor and 2-factor models were obtained (NEP = GPP-Re) and evaluated for all sky conditions. Generally, the simulations of the hourly, monthly, and annual sums of NEP showed reasonable performances. The estimated NEP values overestimated the observations by 22% and 27% for the hourly sums in 2013–2016 when using the 3-factor and 2-factor models, respectively, and 7% and 12% for annual sums in 2013–2015 (2016 data were not used as the CO2 flux measurements had some problems in the 2016 summer). The NEP estimations were evidently improved when more factors (e.g., dark respiration) influencing Re were considered in the daytime respiration compared to those without considering these factors. To simplify the numerous and complicated CO2 processes in the simulations of Re and NEP, the PAR energy method was applied to capture and describe its main processes and energy interactions. The PAR energy method was suitable for studying the energy relationships associated with CO2 processes and developing empirical models for the simulations of GPP, Re, and NEP. These models were useful tools to investigate the multiple interactions and mechanisms between CO2, other atmospheric compositions, and PAR. Thus, the energy method is suggested to be applied to carbon balance.

Al2O3-Cu\Ethylene Glycol-Based Magnetohydrodynamic Non-Newtonian Maxwell Hybrid Nanofluid Flow with Suction Effects in a Porous Space: Energy Saving by Solar Radiation

Nanotechnology is well-known for its versatile and general thermal transport disciplines, which are used in semiconductors, spacecraft, bioengineering, functional electronics, and biosensors. As a result, process optimization has attracted the interest of scientists and technologists. The main aim of the current analysis is to explore the enhancement of energy/heat transfer via the dispersion of cylindrical-shaped nanoparticles of alumina and copper in ethylene glycol as a base fluid using a non-Newtonian Maxwell fluid model. In the current study, the effects of solar radiation, plate suction, and magnetohydrodynamics on a Maxwell hybrid nanofluid are encountered. The flow is induced by linearly stretching a sheet angled at ξ=π/6, embedded in a porous space. The proposed problem is converted into a mathematical structure in terms of partial differential equations and then reduced to ordinary differential equations by using appropriate similarity variables. In the similarity solution, all the curves for the velocity field and temperature distribution remain similar, which means that the symmetry between the graphs for the velocity and temperature remains the same. Therefore, there is a strong correlation between similarity variables and symmetry. The obtained model, in terms of ordinary differential equations, is solved using the built-in numerical solver bvp4c. It is concluded that more nanoparticles in a fluid can make it heat up faster, as they are typically better at conducting heat than the fluid itself. This means that heat is transferred more quickly, raising the temperature of the fluid. However, more nanoparticles can also slow the flow speed of the fluid to control the boundary layer thickness. The temperature field is enhanced by increasing the solar radiation parameter, the magnetic field parameter, and the porous medium parameter at an angle of ξ=π/6, which serves the purpose of including radiation and the Lorentz force. The velocity field is decreased by increasing the values of the buoyancy parameter and the suction parameter effects at an angle of ξ=π/6. The current study can be used in the improvement of the thermal efficiency of nanotechnological devices and in renewable energy sources to save energy in the energy sector. The present results are compared with the published ones, and it is concluded that there is excellent agreement between them, which endorses the validity and accuracy of the current study.

Optimal design and off-design performance improvement for power-to-methane system integrating solid oxide electrolysis cell with methanation reactor

Power-to-methane (PtM) is a prospective solution to the mismatching between the supply and consumption of renewable energy resources (RES) by converting renewable power into methane. However, the continuous fluctuation of RES causes the PtM system to deviate from the design condition in the vast majority of cases, and thus it is significantly vital to study the operating characteristics of the PtM system under off-design conditions. This paper proposes a comprehensive investigation framework from design to off-design steps for the performance improvement of a PtM system combining solid oxide electrolysis cell with methanation reactor, and solar energy is selected as renewable energy input. Firstly, the system with the total exergy efficiency (ηEX,tot) of 11.83% and levelized cost of exergy (LCOE) of 150.76 $/MWh is selected as the optimal design condition based on the homogeneous assessment from both thermodynamic and economic aspects, by means of Non-dominated sorting genetic algorithm-II. Then, based on the optimal design point, the off-design performances are quantitatively investigated under varying solar radiation and key operating parameters, in terms of synthetic natural gas (SNG) yield and ηEX,tot. The results indicate that with the increment in solar radiation, the SNG yield rises, while the ηEX,tot increases first and then decreases. Finally, the multi-objective optimization based on the Artificial Neural Network models is implemented for the system under off-design conditions to acquire the best trade-off between hourly SNG yield and ηEX,tot. The off-design optimization solutions reveal that the hourly optimal SNG yield is located in the range of 275.06–946.53 kW, achieving a total annual SNG yield of 1697 MWh/y, and the hourly optimal ηEX,tot mainly varies in the range of 10.40–11.40%.

Estimation of evaporation from the water surface using the norm operator

Due to the lack of precipitation in recent years, some regions of Turkey are in danger of drought. This situation increases the importance of planning water resources and makes it necessary to develop water budget calculations. One of the important steps in water budget calculations is the correct estimation of the amount of evaporation. For this reason, a different method has been developed for evaporation estimation and the applicability of this developed method has been tested with the meteorological parameters of Lake Eğirdir, one of most important freshwater resources of Turkey. Eğirdir Lake is located within the borders of Isparta province in the Mediterranean Region, Turkey. Firstly, evaporation estimation models were developed by artificial neural networks (ANN) method using 490 days of air temperature, water temperature, sunshine duration, and solar radiation parameters of Lake Eğirdir. After the meteorological parameters were transformed into a dimensionless form through normalization, the norm function was applied to these parameters as a part of the modeling process. The values obtained by the function are used as input parameters in the N-ANN method. In both cases, the pan evaporation values obtained with different network structures were compared and it was seen that the N-ANN models developed with the norm operator in general gave more appropriate results.

Scrutinization of Solar Thermal Energy and Variable Thermophysical Properties Effects on Non-Newtonian Nanofluid Flow

Nanofluids generate high values of convection heat transfer coefficients, low specific heat, and density, which improve the solar thermal energy performance by making it work effectively. By utilizing nanotechnology and solar thermal radiation, the modern world is moving in the direction of new technologies. Therefore, this research is communicated to explore the significance of solar thermal energy, variable properties on non-Newtonian nanofluid flow. However, to exemplify the fluid transport features of the Casson nanofluid (CF), the Buongiorno nanofluid model was utilized. Also, the Lie-group technique is used in the framework to develop similarity variables that will be used to reduce the number of independent variables in partial differential equations (PDEs) and is solved numerically by using the weighted residual Galerkin method (WRGM). The graphical findings revealed that when the variable viscosity parameter is increased, the fluid temperature decreases, while the presence of the solar radiation parameter has the opposite impact. Additionally, when the non-Newtonian parameter approaches infinity, the Casson fluid obeys the viscosity law. The report of this study will be of benefit to thermal and chemical engineering for nanotechnology advancement. KEYWORD: Solar Thermal Energy, Nanofluids, Non-Newtonian, weighted residual Galerkin method (WRGM).

Rotationally symmetric hybrid-nanofluid flow over a stretchable rotating disk

Owing to the advanced thermophysical properties of hybrid nanofluids and stabilizing nature of surface stretching, the problem of rotationally symmetric flow and heat transfer induced over a rotating disk is extended to the case of a stretchable disk, considering Cu and TiO2 nanoparticles suspended in water. In addition, this study deals with four distinct shapes (sphere, brick, cylinder, platelet) of Cu and platelet-shaped TiO2 nanoparticles to elucidate the shape effects. Different from the traditionally applied external vertical magnetic field, the flow is investigated under the influence of a horizontal magnetic field. Moreover, the impact of solar radiation parameters is taken into account to inspect the radiated hybrid nanoparticles. For the same sense and weak & opposite sense (−0.161≤β≤0, here β signify the angular velocity ratio of fluid to disk ) of fluid and disk rotation, solutions are obtained in all cases. However, in the case of strong and opposite sense of rotation, physically acceptable solutions exist after a critical value of the stretching parameter (for example, when β=−0.8,Scr≈0.6). These numerical results have significant engineering applications to fluid flows in rotating machineries such as rotating heat exchangers, rotating disk air cleaners and gas turbine engines.

English

Renewable sources are essential in fulfilling affordable, clean, and sustainable energy requirements. Consistent wind speed forecasting and accurate solar radiation prediction are required to minimize economic losses and enhance the security of power usage. Predicting solar radiation and wind speed is difficult due to their uncertain behaviour. Motivated by these aspects, this study implemented an Internet of Things (IoT) and artificial neural network (ANN) based system for predicting solar radiation and wind speed. Initially, IoT and long-range (LoRa) enabled hardware was developed for obtaining real-time data. In this study, customized hardware was developed with the amalgamation of LoRa, WI-Fi and many sensors. Solar grid and windmill sensor nodes are customized hardware deployed to collect real-time data to create datasets that predict solar radiation and wind speed using wind speed, solar radiation, humidity, temperature, voltage, and current parameters. For the prediction, in this study, we have employed the ANN techniques, i.e., Levenberg Marquardt (LM) and Bayesian regularization (BR) methods. We have found that the proposed ANN framework using the LM algorithm provides accuracy in the prediction compared to the BR algorithm with less root mean square error (RMSE), mean square error (MSE), fast convergence speed, and more significant Correlation coefficient (R).

Perancangan Alat Ukur Portable Datalogger Pembangkit Listrik Tenaga Surya

Information about the output power of solar panels and the intensity of solar radiation is needed in the PLTS system to determine the characteristics of the potential power generated by the panels and estimate the amount of load attached. The purpose of this study was to design and manufacture a portable parameter measuring instrument and datalogger module on a microcontroller-based solar panel. Arduino Uno and ThingSpeak WebServer where with this measuring instrument solar panel parameters such as input parameters in the form of solar radiation, ambient (environment) temperature and output parameters in the form of voltage, current, latitude panel position will be measured, stored and displayed in graphical form in realtime. The system being built consists of three main parts: namely the sensor as input which will measure solar radiation data, temperature or temperature, current, voltage, latitude and longitude position of the Arduino Uno solar panel module which will acquire measurement data from sensors and the Labview application which will store and display data in realtime. The results of this study indicate that the measured current error is 1.68% and the lux error is 1.95%.

Efficient solar absorber based on VO2-W-SiO2 hybrid metamaterials

The broad spectrum near unity solar absorber shows important significance in the solar energy conversion. In this work, a polarization independent broadband and wide-angle metamaterials solar absorber consisted of VO2 periodic arrays and alternately stacked SiO2/W layers is designed. To explore the absorption of solar radiation and proper geometric parameters, numerical studies are carried out on the proposed structure. And the absorption of the designed structure is above 90% in the spectra range of 300-2000 nm with an average absorption of 95%. Moreover, the proposed structure possesses excellent solar absorptivity and insensitivity to the incident angle and polarization of sunlight. The average integrated absorption remains 90% with the light incident angle of 50°. Hence, the proposed broadband solar absorber has potentially valuable applications in thermophotovoltaic conversion, thermal emitters, solar cell, and so on.

Sunshine and Temperature Based Models for Estimating Global Solar Radiation in Maiduguri, Nigeria

Nine existing sunshine based models and three existing temperature based models were evaluated and compared to ascertain the most suitable models for estimating global solar radiation in Maiduguri; the most suitable sunshine based and most suitable temperature based models were also compared. The measured monthly average daily global solar radiation, sunshine hours, maximum and minimum temperature meteorological parameters during the period of thirty one years (1980 – 2010) was utilized and the evaluated models were tested statistically using validation indicators of coefficient of determination, Mean Bias Error, Root Mean Square Error, Mean Percentage Error, t – test, Nash – Sutcliffe Equation and Index of Agreement. The results indicated that the linear exponential sunshine based model and the logarithmic temperature based model were found more accurate for global solar radiation estimation in Maiduguri as compared to other evaluated models. Furthermore, the logarithmic temperature based model was found more accurate for estimating global solar radiation as compared to the linear exponential sunshine based model, and this was testified from the figure showing the comparison between the recommended sunshine based and temperature based models in which the recommended temperature based model depicts the best fitting with the measured global solar radiation data.

Development criterion of estimating hourly global solar radiation for all sky conditions in China

The high quality hourly global solar radiation data becomes more and more important under the trend of low building energy consumption and carbon emission. The models based on daily global solar radiation and hourly meteorological parameters are two kinds of efficient methods to compensate for measured data deficiency. However, the applicable climate and sky conditions of two models are needed to be clarified. Based on 45 stations in China, the coupling correlations between hourly global solar radiation and meteorological parameters are obtained by correlation analysis. The eight decomposition models and four models based on the main hourly meteorological parameters are developed and evaluated for climates and sky conditions using the mean absolute bias error, relative root mean squared error, and Nash-Sutcliffe efficiency coefficient. Considering the advantages of two kinds of models, the criterion of estimating hourly global solar radiation data is developed. The results show that hourly sunshine duration and hourly surface temperature are the main factors reflecting the change of hourly global solar radiation. In humid and sub-humid climates, the hourly relative humidity can further characterize the fluctuation of hourly global solar radiation. CPR model performs best among decomposition models, especially in sunny sky conditions. The meteorological parameters models own higher accuracy in cloudy conditions. In regions with hourly sunshine duration measurement, CPR model is recommended when daily clearness index is higher than 0.7 and lower than 0.2. When daily clearness index varies from 0.2 to 0.7, the model should be established by selecting hourly sunshine duration and hourly surface temperature in arid and semi-arid climates and hourly sunshine duration, hourly surface temperature and hourly relative humidity in humid and subhumid climates, respectively. In regions without hourly sunshine duration measurement, CPR model should be used regardless of weather conditions and climate types.

The Effects of Reduced Gravity and Radiative Heat Transfer on the Magnetohydrodynamic Flow Past a Non-Rotating Stationary Sphere Surrounded by a Porous Medium

In the present study, the effects of reduced gravity and solar radiation on the magnetohydrodynamics (MHD) fluid flow and heat transfer past a solid and stationary sphere embedded in a porous medium are investigated. A model describing the considered configuration is put in dimensionless form using appropriate dimensionless variables and then transformed to primitive form for a smooth algorithm on a computing tool. A primitive form of the model is solved by employing the finite difference method. Solutions for variables of interest, such as velocity distribution and temperature field, along with their gradients, are depicted in graphs and tables. The main goal of the paper is to study the physical impact of reduced gravity on heat transfer and fluid flow around a sphere surface inserted in a porous medium in the presence of an applied magnetic field and solar radiation. The effects of the governing parameters, which are the reduced gravity parameter, magnetic field parameter, radiation parameter, porous medium parameter, and the Prandtl number, are discussed and physically interpreted. The displayed solutions indicate that velocity rises with the reduced gravity and solar radiation parameters but decreases with augmenting the Prandtl number, magnetic field parameter, and porous medium parameter. It is deduced from the presented results that the temperature becomes lower by increasing the values of the reduced gravity parameter and the Prandtl number, but, on the other hand, it becomes higher by increasing the values of the magnetic field, the porous medium, and the radiation parameters at all the considered positions of the surface of the sphere. A comparison between the present and already published results is performed to check the validity of the proposed numerical model.