Optimization of a solar air heater with phase change materials: Experimental and ‎numerical study

In this paper, a solar air heater (SAH) with phase change material (PCM)-based energy storage is ‎investigated. Paraffin was placed underneath the absorber plate as the PCM. A transient two-‎dimensional laminar model was used in the Ansys Fluent 17 software to study the effects of different ‎parameters on the performance of the SAH, such as the air mass flow rate, the amount of paraffin, and ‎the thermal conductivity of the paraffin. The performance of the SAH was optimized by considering ‎two objectives simultaneously: thermal energy efficiency and maximum nocturnal temperature ‎difference between the inlet and the outlet of the SAH. To validate the numerical model, a SAH with ‎a 2-cm paraffin layer and the same dimensions as the numerical model was built and tested. The ‎results of the simulation showed good agreement with the experimental results.

A nanoflare model for active region radiance: application of artificial neural networks

Context. Nanoflares are small impulsive bursts of energy that blend with and possibly make up much of the solar background emission. Determining their frequency and energy input is central to understanding the heating of the solar corona. One method is to extrapolate the energy frequency distribution of larger individually observed flares to lower energies. Only if the power law exponent is greater than 2, is it considered possible that nanoflares contribute significantly to the energy input. Aims. Time sequences of ultraviolet line radiances observed in the corona of an active region are modelled with the aim of determining the power law exponent of the nanoflare energy distribution. Methods. A simple nanoflare model based on three key parameters (the flare rate, the flare duration time, and the power law exponent of the flare energy frequency distribution) is used to simulate emission line radiances from the ions Fe XIX, Ca XIII, and Si iii, observed by SUMER in the corona of an active region as it rotates around the east limb of the Sun. Light curve pattern recognition by an Artificial Neural Network (ANN) scheme is used to determine the values. Results. The power law exponents, alpha 2.8, 2.8, and 2.6 for Fe XIX, Ca XIII, and Si iii respectively. Conclusions. The light curve simulations imply a power law exponent greater than the critical value of 2 for all ion species. This implies that if the energy of flare-like events is extrapolated to low energies, nanoflares could provide a significant contribution to the heating of active region coronae.Comment: 4 pages, 5 figure

Optimal tilt angles for solar collectors facing south at Fez city (Morocco)

The main objective of this paper is to determine the optimal tilt angles for south-facing solar collectors at Fez city in Morocco. This is this would allow collectors to receive the maximum of the incident solar energy. Thirteen models were used to calculate the global solar radiation reaching an inclined surface facing south. To determine the optimal tilt angle we varied the inclination angle from -20° to 90° by a step of 5°. The performances of each model are evaluated by comparing the calculated and measured global solar radiation on a surface facing south and tilted by 34°. Results of the best found model show that the optimum tilted angle is varying between 62° (December) and -6° (June). Also, the yearly-average optimal tilt angle is found to be 32° for Fez. Keywords: Optimum tilt angle; global solar radiation; diffuse solar radiation; Fez (Morocco)

Application of Artificial Intelligence for Modeling the Internal Environment Condition of Polyethylene Greenhouses

Accurate temperature prediction and modeling are critical for effective management of agricultural greenhouses. By optimizing control and minimizing energy waste, farmers can maintain optimal environmental conditions, leading to improved crop yields and reduced financial losses. In this study, multiple models, including Multiple Linear Regression (MLR), Radial Basis Function (RBF), and Support Vector Machine (SVM), were compared to predict greenhouse air temperature. External parameters, such as air temperature (Tout), relative humidity (Hout), wind speed (W), and solar radiation (S), were used as inputs for these models, and the output was the inside temperature. The results showed that the RBF model with the LM (Levenberg–Marquardt) learning algorithm outperformed the other models, achieving the lowest error and the highest coefficient of determination (R2) value. The RBF model produced RMSE, MAPE, and R2 values of 1.32 °C, 3.23%, and 0.931, respectively. These results demonstrate that the RBF model with the LM learning algorithm can reliably predict greenhouse air temperatures for the next two hours. The ANN model can be applied to optimize time management and reduce energy losses, improving the overall efficiency of greenhouse operations

Computation of irradiance in a solar still by using a refined algorithm

A refined solar algorithm from the ESP-r system has been used to calculate the distribution of solar irradiation inside a basin-type solar still. In the approach, surface finish, view factors and multiple reflections are taken into consideration in the computation of the solar radiation that reaches the surface of the saline water in the distillation system. The algorithm was applied to a solar still tested at the University of Strathclyde in Glasgow (55 520 N, 4 150 W). Under the prevailing meteorological conditions, it was found that previous models overestimated the computed solar load on the saline water surface. The present modelling approach is demonstrated to exhibit a higher degree of accuracy than previous methods for irradiance distribution prediction, yielding new insights into approaches to solar still performance improvement. The modelling outcomes are presented and discussed

Estimation of monthly pan evaporation using support vector machine in Three Gorges Reservoir Area, China

Pan evaporation plays a critical role in estimating water budget and modeling crop water requirements. However, it has been measured at a very limited number of meteorological stations. Estimation of pan evaporation from measured meteorological variables offers an important alternative and drawn increasing attention in the recent years. This paper investigated the performance of support vector machine (SVM) in the estimation of monthly pan evaporation using commonly measured meteorological variables in Three Gorges Reservoir Area in China. Evaluation suggested that SVM models showed remarkable performances and significantly outperformed the empirical model. The SVM model with polynomial as kernel function outperformed that with radial basis function. In the case of unavailable measurements of pan evaporation and meteorological variables to construct the SVM model, pan evaporation can be well-estimated by SVM model developed using data at other sites. The results indicated that the SVM method would be a promising alternative over the traditional approaches for estimating pan evaporation from measured meteorological variables

Parametric optimization of daylight and thermal performance through louvers in hot and dry climate of Tehran

This article describes a parametric research simulated different louver conditions performance under given climate. The DIVA (Design, Iterate, Validate and Adapt) plug-in for Rhinoceros/Grasshopper software is used as the main tool, given its ability to effectively calculate daylight metrics (using the Radiance/Daysim engine) and energy consumption (using the EnergyPlus engine). The optimization process is carried out parametrically controlling the shadings’ geometries. Genetic Algorithms (GA) embedded in the evolutionary solver Octopus is adopted in order to achieve close to optimum results by controlling iteration parameters. The results of the paper show that there are meaningful optimum parameters which may help for better thermal performance through louvers in hot and dry climate of Tehran. The results indicate impressive efficiency in building industry in contemporary architecture of developing countries especially in Iran and west of Asia.Keywords: Daylight, Thermal, louver, Shading, Optimizatio

Impact of Sunshine Duration and Clearness Index on Diffuse Solar Radiation Estimation in Mountainous Climate

In this paper, measured data of solar radiation was applied to develop forty-three (43) empirical models for estimation of monthly average diffuse solar radiation using clearness index, sunshine duration and a combination of them as predictors. The data covered a period of two years from May 2015 to April 2017 and was measured at Mehran University of Engineering and Technology, Hyderabad, Pakistan. Through a comprehensive statistical performance analysis, 43 dimensional models developed were tested for constructing the most accurate regression model to predict the monthly mean daily diffuse solar radiation in Hyderabad, Pakistan. On the whole, the model 42 – a hybrid of sunshine duration and clearness index predictors of diffuse fraction outperformed the remaining models proposed in this study. The best model (model 42) was then compared with 5 models and 5 measured data of diffuse solar radiation available in the literature and the NASA database by applying statistical indicators such as MBE, MPE, RMSE, RRMSE, R2 and GPI. Through the analysis, the hybrid of sunshine duration and clearness index predictors of diffuse fraction model (model 42) was selected as the most appropriate model. The study concluded that the proposed hybrid model can serve as a baseline for the design of photovoltaic systems and estimate the monthly mean daily diffuse solar radiation on the horizontal surface for Hyderabad, Pakistan and other locations with similar local climate conditions.Citation: Nwokolo, S.C. and Otse, C.Q. (2019). Impact of Sunshine Duration and Clearness Index on Diffuse Solar Radiation Estimation in Mountainous Climate. Trends in Renewable Energy, 5, 307-332. DOI: 10.17737/tre.2019.5.3.0010