Rotea, Mario A.

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Mario Rotea is the head of the Department of Materials Engineering and holds the Eric Johnson Chair. His research interests include control systems; condition monitoring systems; energy conversion, storage, and conservation; mechanical systems; and aerospace systems.


Recent Submissions

Now showing 1 - 3 of 3
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    Evaluation of Log-Of-Power Extremum Seeking Control for Wind Turbines Using Large Eddy Simulations
    (John Wiley & Sons Ltd) Ciri, Umberto; Leonardi, Stefano; Rotea, Mario A.; 0000-0002-9809-7191 (Leonardi, S); 0000-0002-4239-0591 (Rotea, MA); Ciri, Umberto; Leonardi, Stefano; Rotea, Mario A.
    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.
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    Data-Driven RANS for Simulations of Large Wind Farms
    (Institute of Physics Publishing) Iungo, Giacomo V.; Viola, F.; Ciri, Umberto; Rotea, Mario A.; Leonardi, Stefano; 0000-0002-0990-8133 (Iungo, GV); 0000-0002-9809-7191 (Leonardi, S); Sorensen J.N.; Ivanell S.; Barney A.; Iungo, Giacomo V.; Ciri, Umberto; Rotea, Mario A.; Leonardi, Stefano
    In 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.
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    Data-Driven Reduced Order Model for Prediction of Wind Turbine Wakes
    (Institute of Physics Publishing) Iungo, Giacomo V.; Santoni-Ortiz, Christian; Abkar, M.; Porté-Agel, F.; Rotea, Mario A.; Leonardi, Stefano; Iungo, Giacomo V.; Santoni-Ortiz, Christian.; Rotea, Mario A.; Leonardi, Stefano
    In 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.

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