Wind power generation highly depends on the determination of wind power potential, which drives the design and feasibility of the wind energy production investment. This gives an important role to wind power estimation, which creates the need for an accurate wind data analysis and wind energy potential assessments for a given location. Such assessments require the implementation of an accurate and suitable wind distribution model. Therefore, in the quest for a well-fitted model, eight methods for estimating the Weibull parameters are investigated in this paper. The methods were then investigated by employing statistical tools, and their performances have been discussed in terms of various error indicators such as root mean squared error (RMSE), regression error (R2), chi-square (X2), and mean absolute error (MAE). Meteorological data for diverse terrain from 14 provinces with 30 sites scattered across Iran were employed to examine the performance of the investigated methods. The results demonstrated that the empirical method has superiority over the investigated technique in terms of errors.

Vortex shedding phenomenon behind bluff bodies and its destructive unsteady wake can be controlled by employing active and passive flow control methods. In this quest, researchers employed experimental fluid dynamics (EFD), computational fluid dynamics (CFD) and an analytical approach to investigate such phenomena to reach a desired outcome. This study reviews the available literature on the flow control of vortex shedding behind bluff bodies and its destructive wake through the modification of the geometry of the bluff body. Various modifications on the bluff body geometries namely perforated bluff bodies, permeable and porous mesh, corner modification and wavy cylinder have been reviewed. The effectiveness of these methods has been discussed in terms of drag variation, wake structure modifications and Strouhal number alteration.

El artículo aborda la inteligencia artificial y el aprendizaje automático, aplicándose dos algoritmos de aprendizaje automático de referencia: el árbol de decisión y la red neuronal de propagación hacia atrás implementada en el 30% del conjunto de datos Galaxy para observar y analizar el rendimiento de clasificación de técnicas de aprendizaje automático. Una muestra de fuentes de radio con una luminosidad de radio de 1.4 GHz, S1.4> 10mJy, que emplea una nueva técnica más eficiente que métodos anteriores, como un índice espectral pronunciado o un tamaño angular pequeño, utilizando datos del infrarrojo cercano para filtrar a baja, utilizan radiogalaxias con desplazamiento al rojo (z <2) al incluir solo fuentes con una identificación muy débil o sin detección en la banda K o a 3,6 μm.