Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.
Solar energy forecasting accuracy is essential for increasing the quantity of renewable energy that can be integrated into the existing electrical grid control systems. The availability of data at unprecedented levels of granularity allows for the development of data-driven algorithms to improve the estimation of solar energy generation and production. In this paper, we develop a prediction of solar potential across large photovoltaic panels from the roof tops using a machine learning method. The Restricted Boltzmann Machine (RBM) is the machine learning method used in the study to predict or forecast the solar potential in rooftops. The machine learning model is supplied with training dataset to get trained with the dataset for conversion into the model and then tested with the test dataset for validating the model. The results of simulation are conducted on R-package over various libraries to predict the rooftop solar potential. The results of simulation shows that the proposed method achieves higher rate of prediction accuracy than the other methods. The results of the simulation show that the proposed method achieves a higher rate of prediction accuracy of 99% than the other methods.
Corrugations are folds on a surface as found on wings of dragon fly insects. Although they fly at relatively lower altitudes its wings are adapted for better aerodynamic and aero-elastic characteristics. In the present work, three airfoil geometries were studied using the 2-D panel method to evaluate the aerodynamic performance for low Reynolds number. The experiments were conducted in wind tunnel for incompressible flow regime to demonstrate the coefficients of lift drag and glide ratio at two Reynolds numbers 1.9x104 and 1.5x105 and for angles of attack ranging between 00 and 160. The panel method results have been validated using the current and existing experiment data as well as with the computational work from cited literature. A good agreement between the experimental and the panel methods were found for low angles of attack. The results showed that till 80 angle of attack higher lift coefficient and lower drag coefficient are obtainable for corrugated airfoils as compared to NACA 0010. The validation of surface pressure coefficients for all three airfoils using the panel method at 40 angles of attack was done. The contours of the non-dimensional pressure and velocity are illustrated from -100 to 200 angles of attack. A good correlation between the experiment data and the computational methods revealed that the corrugated airfoils exhibit better aerodynamic performance than NACA 0010.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.