When a wind turbine works in yaw, the wake intensity and the power production of the turbine become slightly smaller and a defl ection of the wake is induced. Therefore, a good understanding of this effect would allow an active control of the yaw angle of upstream turbines to steer the wake away from downstream machines, reducing its effect on them. In wind farms where interaction between turbines is signifi cant, it is of interest to maximize the power output from the wind farm as a whole and to reduce fatigue loads on downstream turbines due to the increase of turbulence intensity in wakes.A large eddy simulation model with particular wind boundary conditions has been used recently to simulate and characterize the turbulence generated by the presence of a wind turbine and its evolution downstream the machine. The simplifi ed turbine is placed within an environment in which relevant fl ow properties like wind speed profi le, turbulence intensity and the anisotropy of turbulence are found to be similar to the ones of the neutral atmosphere. In this work, the model is used to characterize the wake defl ection for a range of yaw angles and thrust coeffi cients of the turbine. The results are compared with experimental data obtained by other authors with a particle image velocimetry technique from wind tunnel experiments. Also, a comparison with simple analytical correlations is carried out.

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This work is mainly dedicated to the study of the characteristics of spectral coherence of turbulence fluctuations in wind turbine wakes. A computational fluid dynamics (CFD) code has been implemented using a large-eddy simulation (LES) approach, which is thought to be conceptually more suitable for studying the turbulence evolution in a wind turbine wake. Comparisons with experimental data from the Nørrekaer Enge II Windfarm, in Denmark, and with an analytical model proposed by Panofsky and Dutton have been performed, and the results are found to be in reasonable agreement with both.

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In this article, a time-of-flight detection technique in the frequency domain is described for an ultrasonic Local Positioning System (LPS) based on encoded beacons. Beacon transmissions have been synchronized and become simultaneous by means of the DS-CDMA (Direct-Sequence Code Division Multiple Access) technique. Every beacon has been associated to a 255-bit Kasami code. The detection of signal arrival instant at the receiver, from which the distance to each beacon can be obtained, is based on the application of the Generalized Cross-Correlation (GCC), by using the cross-spectral density between the received signal and the sequence to be detected. Prior filtering to enhance the frequency components around the carrier frequency (40 kHz) has improved estimations when obtaining the correlation function maximum, which implies an improvement in distance measurement precision. Positioning has been achieved by using hyperbolic trilateration, based on the Time Differences of Arrival (TDOA) between a reference beacon and the others.

This paper studies the problem of determining the position of beacon nodes in Local Positioning Systems, for which there is no inter-node distance measurements available. Also, neither the mobile node nor any of the stationary nodes have positioning or odometry information. The common solution is implemented using a mobile node capable of measuring its distance to the stationary beacon nodes within a sensing radius. Many authors had implemented heuristic methods based on optimization algorithms to solve the problem, however such techniques can fail if the range measurements doesn't provide enough information to obtain a unique solution. Equally, the actual methods require a good initial estimation of the node positions in order to find the correct solution. In this paper we use rigidity theory to determine the necessary conditions in which such problem is solvable for Local Positioning Systems. We also a present a new method to calculate the inter-beacon distances based in the linearization of the trilateration equations. This method doesn't require any initial estimation of the nodes position. The simulation results show a good estimation of the beacon nodes position without using any optimization algorithm.