1,721,131 research outputs found
Spinodal decomposition in the inverse cascade of two-dimensional, binary-fluid turbulence
No full textWe study spinodal decomposition in the inverse-cascade regime of two dimensional turbulence in symmetric, binary fluid mixtures. We show that turbulence leads to break up of domains whose size, in the inverse cascade regime, is proportional to the Hinze scale. Even more strikingly, we show that the inverse cascade of energy is blocked by the formation of domains
Particles and Fields in Superfluids: Insights from the Two-dimensional Gross-Pitaevskii Equation
No full textWe study the dynamics of active particles in two-dimensional superfluids at temperature , for a variety of initial configurations, by carrying out extensive direct-numerical-simulations of the two-dimensional, Galerkin-truncated Gross-Pitaevskii equation. Our study elucidates the interplay of particles and fields, in both simple and turbulent flows. We show that particle collisions can be inelastic, if the repulsive interactions between particles is weak, and elastic otherwise. We show that assemblies of many particles and vortices yield turbulent spatiotemporal evolutions
Multifractal Droplet Dynamics in Two-Dimensional, binary-fluid turbulence
No full textWe present the most extensive direct numerical simulations, attempted so far, of statistically steady, homogeneous, isotropic turbulence in two-dimensional, binary-fluid mixtures with air-drag-induced friction. We model this mixture by using the Cahn-Hilliard-Navier-Stokes equations and choose parameters, e.g., the surface tension, such that we have a droplet of the minority phase moving inside a turbulent background of the majority phase. Our study reveals that a single droplet, whose mean radius lies in the inertial range of scales, (a) enhances the the forward-cascade part of the energy spectrum of two-dimensional turbulence and (b) stretches the tails of the PDF of the Okubo-Weiss parameter . We show that the dynamics of the droplet is affected significantly by the turbulence in the fluid. In particular, the PDFs of the components of the acceleration shows wide, non-Guassian tails. We characterize the time dependence of the deformation of the droplet and show that it exhibits multifractality
Real-space Manifestations of Bottlenecks in Turbulence Spectra
No full textAn energy-spectrum bottleneck, a bump in the turbulence spectrum between the inertial and dissipation ranges, is shown to occur in the non-turbulent, one-dimensional, hyperviscous Burgers equation and found to be the Fourier-space signature of oscillations in the real-space velocity, which are explained by boundary-layer-expansion techniques. Pseudospectral simulations are used to show that such oscillations occur in velocity correlation functions in one- and three-dimensional hyperviscous hydrodynamical equations that display genuine turbulence
Universal Statistical Properties of Inertial-particle Trajectories in Three-dimensional, Homogeneous, Isotropic, Fluid Turbulence
No full textWe obtain new universal statistical properties of heavy-particle trajectories in three-dimensional, statistically steady, homogeneous, and isotropic turbulent flows by direct numerical simulations. We show that the probability distribution functions (PDFs) P(Φ), of the angle Φ between the Eulerian velocity u and the particle velocity v, at a point and time, scales as P(Φ) ∼Φ−, with a new universal exponent ≃ 4
Mutual-Friction Coefficients in Two-Dimensional Superfluids: From the Gross-Pitaevskii equation to the Hall-Vinen-Bekharevich-Khalatnikov Two-fluid Model
No full textWe start from the two-dimensional Gross-Pitaevskii equation (GPE) and develop algorithms for the ab-initio determination of the temperature (T) dependence of the mutual-friction coefficients, α and α, and the normal-fluid density Pn, which appear as parameters in the Hall-Vinen-Bekharevich-Khalatnikov (HVBK) two-fluid model for a superfluid. In the second part of our study, we elucidate the statistical properties of two-dimensional, homogeneous, isotropic superfluid turbulence in the simplified HVBK model, with values for the mutual-friction coefficients that are comparable to those we obtain from the first part of our study
Multivariate Wind Turbine Power Curve Model Based on Data Clustering and Polynomial LASSO Regression
No full textWind turbine performance monitoring is a complex task because of the non-stationary operation conditions and because the power has a multivariate dependence on the ambient conditions and working parameters. This motivates the research about the use of SCADA data for constructing reliable models applicable in wind turbine performance monitoring. The present work is devoted to multivariate wind turbine power curves, which can be conceived of as multiple input, single output models. The output is the power of the target wind turbine, and the input variables are the wind speed and additional covariates, which in this work are the blade pitch and rotor speed. The objective of this study is to contribute to the formulation of multivariate wind turbine power curve models, which conjugate precision and simplicity and are therefore appropriate for industrial applications. The non-linearity of the relation between the input variables and the output was taken into account through the simplification of a polynomial LASSO regression: the advantages of this are that the input variables selection is performed automatically. The k-means algorithm was employed for automatic multi-dimensional data clustering, and a separate sub-model was formulated for each cluster, whose total number was selected by analyzing the silhouette score. The proposed method was tested on the SCADA data of an industrial Vestas V52 wind turbine. It resulted that the most appropriate number of clusters was three, which fairly resembles the main features of the wind turbine control. As expected, the importance of the different input variables varied with the cluster. The achieved model validation error metrics are the following: the mean absolute percentage error was in the order of 7.2%, and the average difference of mean percentage errors on random subsets of the target data set was of the order of 0.001%. This indicates that the proposed model, despite its simplicity, can be reliably employed for wind turbine power monitoring and for evaluating accumulated performance changes due to aging and/or optimization
Enhancing wind power forecasting and ramp detection using long short-term memory networks and the swinging door algorithm
No full textWind turbine performance decline with age
Full text linkWind turbines, as any technical system, are expected to have an efficiency that declines in time [...
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