1,720,976 research outputs found
Data-Driven Reduced Order Model for Prediction of Wind Turbine Wakes
No full textIn this paper a new paradigm for prediction of wind turbine wakes is proposed, which is based on a reduced order model (ROM) embedded in a Kalman filter. The ROM is evaluated by means of dynamic mode decomposition performed on high fidelity LES numerical simulations of wind turbines operating under different operational regimes. The ROM enables to capture the main physical processes underpinning the downstream evolution and dynamics of wind turbine wakes. The ROM is then embedded within a Kalman filter in order to produce a time-marching algorithm for prediction of wind turbine wake flows. This data-driven algorithm enables data assimilation of new measurements simultaneously to the wake prediction, which leads to an improved accuracy and a dynamic update of the ROM in presence of emerging coherent wake dynamics observed from new available data. Thanks to its low computational cost, this numerical tool is particularly suitable for real-time applications, control and optimization of large wind farms
Effect of the turbine scale on yaw control
No full textFull text access from Treasures at UT Dallas is restricted to current UTD affiliates.Yaw misalignment between the incoming wind and the rotor of a turbine causes a lateral displacement of the wake. This effect can be exploited to avoid or mitigate wake interactions in wind farms, so that power losses are minimized. We performed large-eddy simulations to evaluate yaw control for a three-turbine wind farm. We used two different turbine models to assess how the size of the turbine rotor affects the farm efficiency and the effectiveness of the control strategy. A utility-scale wind turbine with rotor diameter of 126 m is compared with a scaled research wind turbine with rotor diameter of 27 m. In both cases, a model-free algorithm is used to determine the turbine yaw set point, which maximizes total power production. The algorithm is the nested extremum-seeking control (NESC), which allows for the coordinated optimization of the wind turbine operating points. The results achieved with NESC are validated by computing a static performance map for different yaw angles. NESC converges to optimal operating conditions, which are in good agreement with the static map benchmark. Numerical results show that a larger rotor diameter induces larger wake deflection, thus achieving higher power improvements. From the analysis of the turbine structural loads, an increase in damage equivalent load is observed for both the yawed turbine and the waked one. Present results suggest that there is a cost-effective trade-off between performance and loads for large turbines. © 2018 John Wiley & Sons, Ltd.This work was supported by NSF Award IIP 1362033 (I/UCRCWindSTAR) and NSF PIRE grant No. 1243482 (the WINDINSPIRE project).Erik Jonsson School of Engineering and Computer Scienc
Evaluation of log‐of‐power extremum seeking control for wind turbines using large eddy simulations
No full textFull text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided Link to Article).The extremum seeking control (ESC) algorithm has been proposed to determine operating parameters that maximize power production below rated wind speeds (region II). This is usually done by measuring the turbine's power signal to determine optimal values for parameters of the control law or actuator settings. This paper shows that the standard ESC with power feedback is quite sensitive to variations in mean wind speed, with long convergence time at low wind speeds and aggressive transient response, possibly unstable, at high wind speeds. The paper also evaluates the performance, as measured by the dynamic and steady state response, of the ESC with feedback of the logarithm of the power signal (LP-ESC). Large eddy simulations (LES) demonstrate that the LP-ESC, calibrated at a given wind speed, exhibits consistent robust performance at all wind speeds in a typical region II. The LP-ESC is able to achieve the optimal set-point within a prescribed settling time, despite variations in the mean wind speed, turbulence, and shear. The LES have been conducted using realistic wind input profiles with shear and turbulence. The ESC and LP-ESC are implemented in the LES without assuming the availability of analytical gradients. ©2019 John Wiley & Sons, Ltd.NSF award number 1243482.Erik Jonsson School of Engineering and Computer Scienc
Constrained state estimation and control
No full textThis dissertation addresses two important problems in control theory: state estimation with constraints and model predictive control. The focus of the dissertation is on two common issues found in practical applications: modeling inaccuracies and implementation cost. The first part considers a state estimation problem for a discrete-time linear system driven by a Gaussian random process. The covariance of this random process and the covariance of the system initial condition are uncertain. In estimation, constraints can be used to represent information about the system, and therefore, have the potential of increasing the estimation accuracy. Unfortunately, the use of constraints usually leads to an increase of the online computation requirements. The approach proposed in this dissertation allows the incorporation of probability constraints in the estimator design. The resulting estimator offers improved accuracy, compared to existing estimators, with no increase of the online computation requirements. The second part considers a model predictive control (MPC) problem where the model of the system is only partially known. A method is presented to increase the robustness of this control architecture from the choice of the uncertain model parameters. The method consists of penalizing the energy of the state trajectory sensitivity, with respect to these parameters, in the MPC cost function. The resulting controller has the potential of increasing the robustness of the conventional MPC architecture. This gain in robustness is achieved with no increase of the online computation requirements as this controller retains the on-line computational simplicity of the conventional MPC problem. An optimization-based method is proposed to design this controller and two benchmark problems are used to illustrate its potential
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Data-Driven RANS for Simulations of Large Wind Farms
No full textIn the wind energy industry there is a growing need for real-time predictions of wind turbine wake flows in order to optimize power plant control and inhibit detrimental wake interactions. To this aim, a data-driven RANS approach is proposed in order to achieve very low computational costs and adequate accuracy through the data assimilation procedure. The RANS simulations are implemented with a classical Boussinesq hypothesis and a mixing length turbulence closure model, which is calibrated through the available data. High-fidelity LES simulations of a utility-scale wind turbine operating with different tip speed ratios are used as database. It is shown that the mixing length model for the RANS simulations can be calibrated accurately through the Reynolds stress of the axial and radial velocity components, and the gradient of the axial velocity in the radial direction. It is found that the mixing length is roughly invariant in the very near wake, then it increases linearly with the downstream distance in the diffusive region. The variation rate of the mixing length in the downstream direction is proposed as a criterion to detect the transition between near wake and transition region of a wind turbine wake. Finally, RANS simulations were performed with the calibrated mixing length model, and a good agreement with the LES simulations is observed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Quadratic stabilization of uncertain systems: Reduced gain controllers, order reduction, and quadratic controllability
No full textIn order to control an aerospace or mechanical system in the real world, a mathematical model is created to capture the system\u27s salient features. Inevitably, this abstraction of the real system contains uncertain elements. This thesis deals with robust control problems for uncertain systems, in which the uncertainties are modelled deterministically rather than stochastically. Using a fixed quadratic form Lyapunov function (this approach is known as quadratic stabilization), we investigate the following issues pertaining to robust stabilization of uncertain systems: (i) Reduced gain controller synthesis for uncertain systems in which the uncertainties are characterized by certain structural conditions and are bounded by some known bounding parameters. (ii) System order reduction in robust stabilization problems. (iii) Controllability concepts for uncertain systems. Some physical examples are used to demonstrate the applicability of the results
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