The effects of varying the location of antenna feed gaps on mutual coupling between orthogonal circular loops

This work aims to investigate how varying the location of antenna feed gaps affect the mutual coupling between orthogonal circular loops. Since the orthogonal circular loops, which is a type of loop antenna array, provide better gain than a single loop antenna, investigating the mutual coupling on the array could contribute to the improvement of its radiation. Using the Method of Moment (MoM)-based simulation tool that computes the Z-parameters, which are the mutual impedances and self-impedances, this paper provides clearer three-dimensional illustrations and advances intuitive insights that would facilitate better understanding of the mutual coupling phenomenon. Prior studies mostly presented two-dimensional illustrations derived from analytical solutions on mutual coupling. The elements of the Z-matrix, namely mutual impedances, Z 12 and Z 21, and the self-impedances, Z 11 and Z 22, are clearly plotted against the rotational angles and carefully analyzed in order to effectively discuss the effects on mutual coupling when the relative locations of the feed gaps along the circular loops are varied. © 2020 IEEE

Numerical examination on two-equations turbulence models for flow across NACA 0012 airfoil with different angle of attack

Selection of an appropriate and efficient turbulence models is important for fast and accurate computation in fluid dynamics. In order to investigate the computational efficiency of turbulence models, numerical examination based on two-equations turbulence models for flow across NACA 0012 airfoil was carried out by using ANSYS Fluent at various angle of attack (-12o to 20o) and at a Reynold number of 3 × 106. The case study is chosen as its transition from viscid to inviscid flow region which would put a strain on computational performance of turbulence models. The two-equation models being investigated are Standard k-ε model, RNG k-ε model, k-ε Realizable model, Standard k-ω model, k-ω BSL model and k-ω SST model. The drag, lift and pressure coefficient between simulation and experimental results are compared. The convergence rate of these turbulence models is collated as well. The contours of static pressure and velocity magnitude was simulated, and boundary layer separation was noticed from 10° angle of attack. In general, the predicted data have good agreement with experimental data. Amongst the investigated models, k-ω SST model showed the best agreement with experimental result meanwhile RNG k-ε model showed the slowest convergence rate among all the turbulence models. © 2020, Penerbit Akademia Baru. All rights reserved

Cloud optical depth retrieval via sky\u27s infrared image for solar radiation prediction

Photovoltaic (PV) system is developed to harness solar energy as an alternative energy to reduce the dependency on fossil fuel energy. However, the output of the PV system is not stable due to the fluctuation of solar radiation. Hence, solar radiation prediction in advanced is needed to make sure the tap changer in PV system has enough time to respond. In this research, the cloud base temperature is identified from the sky\u27s thermal image. From the cloud base temperature, cloud optical depth (COD) is calculated. Artificial neural network (ANN) models are established by using different combinations of current solar radiation and COD to predict the solar radiation several minutes in advanced. R-squared value is used to measure the accuracy of the models. For prediction in advanced for every minute, with COD as input, always show the highest R-squared value. The highest R-squared value is 0.8899 for the prediction for 1 minute in advanced and dropped to 0.5415 as the minute of prediction in advanced increase to 5. This shows that the proposed methodology is suitable for prediction of solar radiation for short term in advanced. © 2019 Penerbit Akademia Baru

Cloud optical depth retrieval via sky’s infrared image for solar radiation prediction

Photovoltaic (PV) system is developed to harness solar energy as an alternative energy to reduce the dependency on fossil fuel energy. However, the output of the PV system is not stable due to the fluctuation of solar radiation. Hence, solar radiation prediction in advanced is needed to make sure the tap changer in PV system has enough time to respond. In this research, the cloud base temperature is identified from the sky’s thermal image. From the cloud base temperature, cloud optical depth (COD) is calculated. Artificial neural network (ANN) models are established by using different combinations of current solar radiation and COD to predict the solar radiation several minutes in advanced. R-squared value is used to measure the accuracy of the models. For prediction in advanced for every minute, with COD as input, always show the highest R-squared value. The highest R-squared value is 0.8899 for the prediction for 1 minute in advanced and dropped to 0.5415 as the minute of prediction in advanced increase to 5. This shows that the proposed methodology is suitable for prediction of solar radiation for short term in advanced