Bergami, Leonardo

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Downloaded from orbit.dtu.dk on: Dec 20, 2017Adaptive Trailing Edge Flaps for Load ReductionBergami, LeonardoPublication date:2011Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Bergami, L. (2011). Adaptive Trailing Edge Flaps for Load Reduction. Paper presented at 7th PhD Seminar onWind Energy in Europe, Delft, Netherlands.Adaptive Trailing Edge Flaps for Active Load ReductionLeonardo BergamiWind Energy Division, Ris DTU - National Laboratory for Sustainable Energy,VEA-118, Frederiksborgvej 399, 4000 Roskilde, DenmarkE-mail: leob@risoe.dtu.dkKeywords: smart rotors, ATEF, active load control1 INTRODUCTIONThe size of modern utility-scaled horizontal-axis wind tur-bines shows a continuously increasing trend. As the rotorsize increases, the loads affecting the wind turbine scale up,not only as an effect of the increased mass of the structure,but also as a consequence of larger variations in the flowfield spanned by the wind turbine blades; such variationsoriginate, for instance, from atmospheric turbulence, windshear, tower shadow, and wakes from other turbines.Future generations of wind turbines would benefit from, andmaybe necessitate, a control system able to alleviate theloads on the structure, for instance, by, for instance, activelycontrolling the aerodynamic forces along the blades so tocompensate for variations in the flow field.The concept is known as smart rotor: the active control sys-tem collects information on the current wind turbine statethrough different sensors, the informations are then pro-cessed by a control algorithm, and sent to actuators mod-ifying the aerodynamic forces along the blade.Several institutions in Europe and US are investigating smartrotor potentialities [1, 2], and different control strategies andactuator devices have been proposed. Some solutions pointat using already existing actuators, as the pitch system, andintegrate it with sensors and control algorithms so to con-trol each blade pitch individually [3]. Other studies proposeto use actuators that modify the aerodynamic forces locallyalong the blade span, as, for instance, micro-tabs[4], rotatingflaps, or adaptive trailing edge flaps [5, 6].The current work focuses on an adaptive trailing edge flap(ATEF) active control system, where the flap actuator de-flects the aft part of the airfoil section by applying a smoothand continuous deformation shape to the airfoil camber line.Compared to a classic rotating flap, the continuous deforma-tion shape results in better lift over drag performances, andlower noise emissions.The paper briefly presents the aerodynamic model for lift,drag and moment on an airfoil undergoing arbitrary motionand flap deflection. In the following section, indications onthe design process and requirements to the active flap con-trol system are retrieved from a preliminary load analysis onthe NREL 5-MW wind turbine in its baseline configuration[7]. Future work will then aim at designing an active flapcontrol solution for the studied wind turbine, and assess thebenefits of the active control system on the overall loadingconditions.2 MODEL AND METHODThe presence of active flap devices poses new challenges inthe modeling of the aerodynamic forces that generates alongthe wind turbine blades. CFD tools like Reynolds AveragedNavier Stokes (RANS) solvers provide accurate solutions,but the computation requirements are still too demandingfor the tool to be efficiently integrated in an aeroelastic sim-ulation tool.Most simulation tools used to compute the aeroelastic re-sponse of a wind turbine in the time domain are based on im-plementations of the Blade Element Method (BEM) [8]. Un-der the BEM assumption of independent annular elementson the rotor plane, the rotor aerodynamic is determined fromthe lift and drag forces along the blades; the aerodynamicforces are then computed independently for each 2D airfoilsection in which the blade has been discretized into.This work presents an aerodynamic model to computelift, drag, and moment coefficients for an airfoil sectionequipped with an Adaptive Trailing Edge Flap. The modelreproduces both steady and dynamic characteristics of theforces on the airfoil undergoing arbitrary (within the limitsof plane wake assumption) motion and flap deflection. Thedynamic effects reproduced by the model can be split intothree categories.Added mass effects, or non-circulatory contributions, de-scribe the forces that arise simply as a reaction of the fluidaccelerated by the airfoil (or the flap) motion. The term hasno memory effects, and only depends on the instantaneousmotion of the airfoil or flap.Effects from wake dynamics, or potential flow effects, de-scribe the memory effects of the vorticity shed into the wake,following a change of the airfoil aerodynamic loading, as,for instance, due to a variation in angle of attack or flap de-flection.Dynamic stall effects represent the dynamics of the forceson an airfoil undergoing flow separation (stall).2.1 Potential flow partIn this part of the model, the aerodynamic forces are com-puted assuming the airfoil always operates in fully attachedflow conditions; viscous effects on the aerodynamic forcesare thus neglected (potential flow assumption).The deficiency, or time lag, on the lift force, caused by thevorticity shed into the wake, is modeled through an equiva-lent effective downwash speed, as in the model from Hansenet al. [9].Gaunaa [10] shows that, under the assumptions of potentialflow and thin-airfoil theory, the effective downwash speedfor an airfoil undergoing arbitrary motion and camber linedeformation can be evaluated as a superposition of indicialstep responses. The indicial response function is expressedin an exponential form, similar to Jones’s function for theflat plate response, thus allowing for an efficient time stepintegration algorithm.The effective downwash speed is then split into an equiva-lent effective angle of attack αeff and flap deflection βeff con-tributions; the effective angle of attack and flap deflectionare used in the look-up of the input steady data, returningthus aerodynamic forces that account for the wake memoryeffects.2.2 Dynamic stall partThe flow separation part of the model follows the Beddoes-Leishmann dynamic stall formulation given in Hansen et al.[9]. The circulatory lift is expressed as a weighted sum of afully attached and a fully separated contribution:CCirc.DynL =CattL fdyn +CfsL (1− f dyn). (1)The dynamics of the flow separation along the airfoil are de-scribed through the separation function f dyn, which assignsthe weight between the fully attached and the fully separatedcomponent. The value of the function depends on the poten-tial flow lift, and on a steady separation function f st .The steady values of the separation function f st , fully at-tached CattL , and fully separated CfsL lift coefficients are re-trieved from the steady input data CstL . All the quantities arenow function of both angle of attack and flap deflection, andtheir computation is not as straightforward as in the rigidairfoil case. The computations are performed in an external‘preprocessing unit’, which allows the user to check, and,eventually, correct the steady data that will be used as inputin the aerodynamic model.In the preprocessing unit, the steady state input lift CstL issplit into a fully attached and a fully separated contribution,assuming that the fully attached part has a linear dependencyon both angle of attack α and flap deflection β :ClinL =∂CL∂α(αst −α0)+ ∂CL∂β βst . (2)The steady separation function is then computed from theexpression for the flat-plate lift in Kirchoff flow, as:f (α,β )st =(2√CstLClinL−1)2. (3)The separation function should be real and bounded 0 ≤f st ≤ 1, thus singularities arise whenever CstL /ClinL < 0 orCstL /ClinL > 1. The singularities are avoided by simply round-ing the function to the closest boundary value; further ‘man-ual adjustments’ will need to be applied by the user to avoiddiscontinuities in the lift coefficient and separation functioninput data.Once the separation function f st is determined, three flowregions are identified.Fully attached region, where the separation function is f st =1. Here, CattL is taken equal to CstL , rather than ClinL , so tomaintain the steady lift coefficient equal to the input one:f st = 1 →{CattL =CstLC f sL =CstL /2; (4)Fully separated region, where the separation function isf st = 0:f st = 0 →{C f sL =CstLCattL =ClinL; (5)Transition region:0 < f st < 1 →{CattL =ClinLC f sL =CstL −ClinL f st1− f st. (6)The model has been validated by comparison with RANSsolutions for an airfoil undergoing harmonic pitching mo-tion and flap deflection, and it has been implemented in themulti-body aeroelastic simulation tool HAWC2 [11].2.3 MethodThe aeroelastic simulation tool HAWC2 is used to evalu-ate the aeroelastic response of the NREL 5-MW baselinewind turbine [7] in the time domain. A wide set of simula-tion conditions is outlined following the design load cases(DLC) prescribed by the IEC standard [12]; simulations in-clude both stochastic, and deterministic wind fields inputs,normal operation and extreme events. The resulting simu-lated loads are summarized in terms of ultimate loads, andequivalent fatigue loads.The equivalent fatigue loads are computed from the simu-lated time series by applying a Rain Flow Counting algo-rithm, and Palmgren-Miner rule for linear fatigue damage.The equivalent fatigue loads account for the amount of timethe wind turbine is expected to spend in each simulated con-dition. A relative fatigue contribution k is evaluated as theratio between the fatigue damage accumulated in a specificoperation condition, and the total life-time fatigue damage.3 PRELIMINARY LOAD ANALYSISA preliminary analysis is carried out on the NREL 5-MWbaseline wind turbine. The analysis of the wind turbine inits baseline configuration allows to identify the DLC thatare critical in terms of fatigue or ultimate loads. Active loadalleviation in these specific cases would directly benefit thewhole wind turbine structure. The critical cases provide thusa convenient set of simulation conditions for designing, test-ing, and assessing the performances of the active load con-trol system.Fatigue loads have been monitored at the blade root, in flap-wise (Mx), edgewise (My), and torsion (Mz), at the towerbase flange in fore-aft (MFA) and side-to-side (MSS) direc-tions, and for the main shaft torsion (MDT ).Relative contributions to the total life-time fatigue damageare grouped according to the operation condition that hasgenerated the fatigue damage, figure 1. Operations in nor-mal power production (DLC 1.1) are responsible for most ofthe fatigue loads on the structure, mainly because of the timethe turbine is expected to spend in these conditions. There-fore, an active load alleviation system operating during nor-mal power production can yield to a sensible reduction ofthe overall fatigue damage.Figure 1: Fatigue damage relative contributions grouped by op-eration condition.The same load-measuring locations are used to monitor ul-timate loads; the maximum loads reported in each opera-tion condition are plotted in figure 2. The highest simulatedloads occur for operations under an extreme coherent gust(DLC 2.3), extreme turbulence (DLC 1.3), or extreme co-herent gust with direction change (DLC 1.4). These casesshould be thus considered during the design process of theactive load control system, since a reduction of these maxi-mum loads might yield a beneficial reduction of the overallstructural requirements.To obtain an overview on which frequency range the activecontrol should focus on, an attempt is made to determinewhich frequencies returns the highest fatigue load contribu-tions. A frequency spectrum is retrieved from the simulatedloads, and Benasciutti and Tovo’s [13] method is applied toFigure 2: Ultimate load analysis. Maximum loads reported ineach simulation condition.obtain an equivalent fatigue damage rate from the load spec-trum. The relative contribution from each frequency rangeis determined by setting to zero its spectral energy contentand comparing the resulting fatigue damage rate with theoriginal-full spectrum fatigue damage.The result is presented as a ‘fatigue-spectrogram’ plot, fig-ure 3, where the relative contributions to fatigue damage de-pend on both frequency and wind speed; red colors indicatehigher fatigue contributions. The relative contributions fromdifferent wind speeds account for the time each wind speedbin is expected to occur.For the blade flapwise root moment, figure 3, the largest fa-tigue contributions originate at high wind speeds, and fromfrequencies close to 1P (and its harmonics). Furthermore,both for blade and tower loads (not shown here), frequenciesabove 5 Hz yield only a modest contribution to the overallfatigue damage.To conclude, the preliminary load analysis allows to iden-tify a set of load cases which are critical in terms of eitherfatigue or ultimate loads. This reduced set of cases providesa convenient test field for the future design of the active con-trol system, as load alleviation in these design cases wouldyield a direct benefit on the overall loading conditions.Fatigue analysis in the frequency domain have highlightedthat the highest contributions to the fatigue damage origi-nates from loads with frequencies below 5 Hz, giving thusFigure 3: Fatigue analysis in the frequency domain. Blade flap-wise root moment, relative contribution to the totalfatigue damage from different frequencies and windspeeds.a clear indication on the frequency range the active controlsystem should focus on.4 FUTURE WORK: ACTIVE LOAD CONTROLAn active load control system will be designed for the NREL5-MW reference wind turbine, using Adaptive Trailing EdgeFlaps actuators, and structural loads sensors. First, theplacement of the ATEF sections along the blade span needsto be optimized, accounting for factors as the flap effect onthe measured loads, time delay, and structural constraints.Once the flap positions have been fixed, the ATEF controlalgorithm is designed; the requirements outlined in the pre-vious analysis, as well as the critical set of load cases willhelp in the design process. The control algorithm will mostlikely be a Model Predictive Control, and system identifi-cation techniques will be investigated in order to obtain thelinear model required by the MPC controller.The flap actuators and the active load control algorithm willbe included in the aero-servo-elastic model of the NREL 5-MW wind turbine, and simulation will be performed withthe HAWC2 code. As in the preliminary analysis, simula-tion conditions will be based on the design load cases pre-scribed in the IEC standard [12], and the loads will be evalu-ated in terms of equivalent fatigue loads and ultimate loads.A comparison of the fatigue and ultimate loads resultingfrom simulations with and without active flap control will di-rectly measure the impact of the ATEF system on the overallturbine loading conditions; as well as that, the comparisonwill bring new elements to be considered in the (ineluctablyiterative...) design process. Finally, the comparison betweenflap and baseline configurations will allow to quantify thepotential of the ATEF system for active load alleviation onMW-sized wind turbines.REFERENCES[1] T. K. Barlas and G. van Kuik. State of the art andprospectives of smart rotor control for wind turbines. Jour-nal of Physics: Conference Series, 75(1):012080 (20 pp.),2007. Journal Article.[2] Matthew A. Lackner and Gijs van Kuik. A compar-ison of smart rotor control approaches using trailing edgeflaps and individual pitch control. Wind Energy, 13(2-3):117–134, March 2010.[3] T. J. Larsen, H. A. Madsen, and K. Thomsen. Activeload reduction using individual pitch, based on local bladeflow measurements. Wind Energy, 8(1):67–80, 2005. Jour-nal Article.[4] Scott J. Johnson, Jonathon P. Baker, C. P. van Dam,and Dale Berg. An overview of active load control tech-niques for wind turbines with an emphasis on microtabs.Wind Energy, 13(2-3):239–253, March 2010.[5] Peter Bjoern Andersen, Lars Henriksen, Mac Gaunaa,Christian Bak, and Thomas Buhl. Deformable trailing edgeflaps for modern megawatt wind turbine controllers usingstrain gauge sensors. Wind Energy, 13(2-3):193–206, March2010.[6] A. W Hulskamp, J. W van Wingerden, T. Barlas,H. Champliaud, G. A. M van Kuik, H. E. N Bersee, andM. Verhaegen. Design of a scaled wind turbine with a smartrotor for dynamic load control experiments. Wind Energy,14(3):339–354, April 2011.[7] J. Jonkman, S. Butterfield, W. Musial, and G. Scott.Definition of a 5-MW reference wind turbine for offshoresystem development. Technical Report NREL/TP-500-38060, National Renewable Energy Laboratory (NREL),February 2009.[8] Martin Hansen. Aerodynamics of wind turbines : ro-tors, loads and structure. James & James, London, 2000.[9] Morten Hartvig Hansen, Mac Gaunaa, and Helge Aa-gaard Madsen. A Beddoes-Leishman type dynamic stallmodel in state-space and indicial formulations. TechnicalReport R-1354(EN), Risoe National Laboratory, Roskilde(DK), 2004.[10] M. Gaunaa. Unsteady two-dimensional potential-flowmodel for thin variable geometry airfoils. Wind Energy,13(2-3):167–192, 2010.[11] Torben Juul Larsen. How 2 HAWC2 the user’s man-ual. Technical Report R-1597(EN), Risoe National Labora-tory. Technical University of Denmark, 2009.[12] International Electrotechnical Commission. IEC61400-1: Wind turbines part 1: Design requirements. Tech-nical report, International Electrotechnical Commission,2005.[13] D. Benasciutti and R. Tovo. Comparison of spec-tral methods for fatigue analysis of broad-band gaussianrandom processes. Probabilistic Engineering Mechanics,21(4):287–299, October 2006.

Adaptive Trailing Edge Flaps for Load Reduction

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Adaptive Trailing Edge Flaps for Load Reduction

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