Paramount two-parameter Weibull function has been extensively used to assess the wind energy potential. The performance contrast of four statistical methods, i.e., energy pattern factor method, least squares regression method, method of moments and mean standard deviation method in estimating extensively used Weibull parameters for wind energy application at four selected locations of northern Ethiopia has been studied. The contrast of statistical methods is compared through relative percentage error, root mean square error, mean percentage error, mean absolute percentage error, Chi-square error and analysis of variance (or) efficiency of the methods used. Test results evidently revealed that, least squares regression method presents better performance than other methods selected in the investigation. The least efficient methods to fit the Weibull distribution curves for the assessment of wind speed data especially for four selected locations are energy pattern factor method, method of moments and mean standard deviation. From the actual data analysis, it is found that if wind speed distribution matches well with the Weibull function, the above three methods are applicable, but if not, least squares regression method can be considered based on the cross checks including energy potential and cumulative distribution function.

This paper discusses the stabilization of clayey subgrade when mixed with coir fibres (CF) randomly and fly ash (FA). The tests were carried out on the locally available clay soil with different...

Wind energy is one of the most cost-effective forms of renewable energy source with significant increment in yearly installed capacities all around the world. In this study, three commercial wind turbines, namely POLARIS P15-50, POLARIS P50-500 and VESTAS V110-2.0, were chosen as large-scale wind energy conversion systems (WECSs) for technical assessment of electric power generation in eight selected locations spreading across the Tigray region of Ethiopia. The economic evaluations of these three WECSs for electric power generation in the selected locations were estimated using present value of cost (PVC) method. These results showed that the highest capacity factor is obtained as 7.873 % using VESTAS V110-2.0 at Mekele, while the lowest as 0.002 % using POLARIS P15-50 at Shire. Average minimum cost per kWh obtained at Mekele was 0.0011$/kWh using VESTAS V110-2.0, while the highest average cost was 7.3148$/kWh using POLARIS P15-50 at Shire. Furthermore, it can be suggested that Atsbi, Chercher, Mekele and Senkata were most profitable for electrical and mechanical applications than hydropower cost in the country.

This paper investigates the effects of different preparation methods (dry tamping, moist tamping, dry pluviation and wet pluviation) on inherent fabric anisotropy of reconstituted sand samples in the laboratory. An image analysis approach was used to quantify the inherent fabric anisotropy in terms of the vector magnitude based on a second-order fabric tensor. In addition, the effects of reconstituted soil sample preparation methods on the void ratio and density index were investigated. This study shows that the specimens prepared by pluviation methods possess more pronounced inherent fabric anisotropy than those by tamping methods. Among all the methods considered in this investigation, the dry pluviation method provided the highest degree of inherent fabric anisotropy. It is shown that the mass-flow and drop height does not effectively influence the packing density. Also, it has been found that no significant effect on void ratio is observed while using different tamping methods, which may be attributed to the fact that the sand used in this study is less well graded. In addition, it is found that the increase in the height of tamper leads to an increase in packing density or a decrease in relative density, with an approximately linear relationship.