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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Fast yaw optimization for wind plant wake steering using Boolean yaw angles

Abstract. In wind plants, turbines can be yawed into the wind to steer their wakes away from downstream turbines and achieve an overall increase in plant power. Mathematical optimization is typically used to determine the best yaw angles at which to operate the turbines in a plant. In this paper, we present a new heuristic to rapidly determine the yaw angles in a wind plant. In this method, we define the turbine yaw angles as Boolean – either yawed at a predefined angle or nonyawed – as opposed to the typical methods of defining yaw angles as continuous or with fine discretizations. We then optimize which turbines should be yawed with an algorithm that sweeps through the turbines from the most upstream to the most downstream. We demonstrate that our new Boolean optimization method can find turbine yaw angles that perform well compared to a traditionally used gradient-based optimizer for which the yaw angles are defined as continuous. There is less than 0.6 % difference in the optimized power between the two optimization methods for randomly placed turbine layouts and less than a 0.6 % difference in the optimal annual energy production between the two optimization methods for a real wind farm. Additionally, we show that our new method is much more computationally efficient than the traditional method. For plants with nonzero optimal yaw angles, our new method is generally able to solve for the turbine yaw angles 50–150 times faster, and in some extreme cases up to 500 times faster, than the traditional method.

17 WIND ENERGY↗
DOE OSTI2020

A Distributed Reinforcement Learning Yaw Control Approach for Wind Farm Energy Capture Maximization: Preprint

In this paper, we present a reinforcement-learning based distributed approach to wind farm energy capture maximization using yaw control, also known as wake steering. In order to maximize the power output of a wind farm, it is often necessary for individual turbines to decrease their own power output through yaw misalignment so as to deflect their wakes away from downstream turbines. Although using model-based methods to achieve yaw misalignment is one option, a model-free method might be better suited to incorporate factors that are difficult to model or changing conditions. We propose an algorithm that adapts concepts of temporal difference reinforcement learning distributed to a multi-agent environment that allows individual turbines to act so as to optimize overall wind farm output and react to unforeseen disturbances.

controls↗
NASA NTRS1982

The effect of delta 3 on a yawing HAWT blade and on yaw dynamics

A single degree of freedom aeroelastic computer model, WMSTAB3, was employed to perform a parametric analysis of HAWT blade behavior during yaw maneuvers. Over 1,000 different combinations of delta sub 3 and normal frequency were analyzed. The effect of delta sub 3 and flapping stiffness on flapping frequency, phase, and magnitude are discussed. The moments transmitted to the fixed system during yaw maneuvers were calculated and reduced to time constants of response to step changes in wind direction. The significance of the time constants for the configurations considered relative to yaw response rate and lag angle is discussed, along with their possible significance for large HAWT.

Perkins, F. W.↗
DOE OSTI2020

Highlighting the Impact of Yaw Control by Parsing Atmospheric Conditions Based on Total Variation: Preprint

Identification of atmospheric conditions within a multivariate atmospheric dataset is a necessary step in the validation of wind plant control strategies. Most often, operating conditions are characterized in terms of aggregated observations and assume that the atmosphere is 'quasi-steady'. Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of wind plant operating conditions. Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation sets the stage for more advanced model validation and the development of real-time control and operation strategies. Controlling observational data for statistical stationarity highlights significant enhancements to the power production of waked turbines under wake steering wind plant control. Considering the combined energy ratio of the turbine to which yaw offsets are prescribed as well as the waked turbine show only moderate improvements when filtering for total validation. The same quality control that highlights wake steering also serves to underpin the off-nominal operation of the controlled turbine.

17 WIND ENERGY↗
DOE OSTI2023

Aerodynamic characterization of two tandem wind turbines under yaw misalignment control using actuator line model

Yaw control has proven to be promising in alleviating the wake effects that plague the efficiency of wind farms. In this work, the actuator line modeling (ALM) method is adopted to simulate the flows over two tandem turbines distanced by 3–7 rotor diameters, with the yaw angle of the upstream rotor varying from γ 1 = 0° to 50°. The aim is to provide a comprehensive aerodynamic characterization of this simple wind farm under yaw misalignment control. With increasing yaw angle, the power generated by the downstream rotor increases, compensating the power loss in the upstream rotor, and resulting in significantly higher total power of the two turbines than that without yaw control. The maximum power output is achieved as the upstream wake of the yawed rotor is redirected away from the downstream rotor plane. Behind the downstream rotor, the secondary steering phenomenon is observed, where the wake is also redirected from the centerline. The use of the actuator line model also reveal unsteady aerodynamic characteristics that cannot be captured by lower-fidelity models. For the upstream rotor, the yaw misalignment results in time-varying change in the local angle of attack on the blade, giving rise to unsteady loading. The downstream rotor is partially submerged in the deflected wake incurred by the yawed upstream rotor. As the blade revolves into and out of the wake deficit, the blade experiences cyclic loading, leading to even stronger fluctuations in the aerodynamic loads than the upstream rotor. These analysis provides a comprehensive understanding of the yaw control effects on the two tandem rotors from the perspectives of aerodynamic performance, wake profiles, and unsteady characteristics. In conclusion, the insights gained from the present study can aid the design of collective yaw control strategies of wind farms, and lay the foundation for assessing the fatigue damage associated with yaw misalignment.

17 WIND ENERGY↗
DOE OSTI2025

Load assessment of a wind farm considering negative and positive yaw misalignment for wake steering

Wake steering strategies are employed to increase the overall power production of wind farms by deflecting wakes of upstream turbines away from downstream ones. The gain in net power comes at the expense of increased fatigue loads experienced by downstream turbines. In this work we investigate performance and fatigue loading characteristics of a small farm consisting of five aligned International Energy Agency Wind Technology Collaboration Programme 15 MW wind turbines. A parametric study is performed where, for every wind direction from −20 to 20°, the yaw misalignment angle varies from −25 to 25°. This setup allows us to investigate asymmetries and identify optimal conditions for a given wind direction. In general, we find that positive yaw configurations are preferred and that yaw configurations that result in attractive power differences when compared to a baseline no-yaw scenario (25 %) come with significant increase in fatigue loading (we use the standard deviation and damage-equivalent load (DEL) of the blade-root, low-speed shaft, and tower-base moments as proxies for fatigue load). We find that for any given positive wind inflow angle, yaw angles between −2.5 and 15° yield power differences of 10 %–20 % over a no-yaw baseline, and positive yaw is preferred because of lower fatigue loading. For any given negative wind inflow angles, positive yaw also results in lower magnitudes of standard deviation and DEL for the channels investigated. A small power loss of up to 2 % is observed for some positive yaw angles under negative wind directions (as compared to symmetric negative yaw and positive wind cases), but improvements in terms of loads exceed 25 % and may be enough to justify a positive yaw configuration under negative winds as well. We show that such behavior can be explained by partial waking and the direction of the rotation of the rotor.

17 WIND ENERGY↗
NASA NTRS1993

Effects of internal yaw-vectoring devices on the static performance of a pitch-vectoring nonaxisymmetric convergent-divergent nozzle

An investigation was conducted in the static test facility of the Langley 16-Foot Transonic Tunnel to evaluate the internal performance of a nonaxisymmetric convergent divergent nozzle designed to have simultaneous pitch and yaw thrust vectoring capability. This concept utilized divergent flap deflection for thrust vectoring in the pitch plane and flow-turning deflectors installed within the divergent flaps for yaw thrust vectoring. Modifications consisting of reducing the sidewall length and deflecting the sidewall outboard were investigated as means to increase yaw-vectoring performance. This investigation studied the effects of multiaxis (pitch and yaw) thrust vectoring on nozzle internal performance characteristics. All tests were conducted with no external flow, and nozzle pressure ratio was varied from 2.0 to approximately 13.0. The results indicate that this nozzle concept can successfully generate multiaxis thrust vectoring. Deflection of the divergent flaps produced resultant pitch vector angles that, although dependent on nozzle pressure ratio, were nearly equal to the geometric pitch vector angle. Losses in resultant thrust due to pitch vectoring were small or negligible. The yaw deflectors produced resultant yaw vector angles up to 21 degrees that were controllable by varying yaw deflector rotation. However, yaw deflector rotation resulted in significant losses in thrust ratios and, in some cases, nozzle discharge coefficient. Either of the sidewall modifications generally reduced these losses and increased maximum resultant yaw vector angle. During multiaxis (simultaneous pitch and yaw) thrust vectoring, little or no cross coupling between the thrust vectoring processes was observed.

Asbury, Scott C.↗
NASA NTRS1980

The effect of yaw on horizontal axis wind turbine loading and performance

The Mod-0 100 kW experimental wind turbine was tested to determine the effects of yaw on rotor power, blade loads and teeter response. The wind turbine was operated for extended periods at yaw angles up to 49 deg to define average or mean response to yaw. It was determined that the effect of yaw on rotor power can be approximated by the cube of the velocity normal to the rotor disc as long as the yaw angle is less than 30 deg. Blade bending loads were relatively unaffected by yaw, but teeter angle increased with wind speed as the magnitude of the yaw angle exceeded 30 deg indicating a potential for teeter stop impacts at large yaw angles. No other adverse effects due to yaw were noted during the tests.

Glasgow, J. C.↗
NASA NTRS2003

Cross-axis adaptation of torsional components in the yaw-axis vestibulo-ocular reflex

The three pairs of semicircular canals within the labyrinth are not perfectly aligned with the pulling directions of the six extraocular muscles. Therefore, for a given head movement, the vestibulo-ocular reflex (VOR) depends upon central neural mechanisms that couple the canals to the muscles with the appropriate functional gains in order to generate a response that rotates the eye the correct amount and around the correct axis. A consequence of these neural connections is a cross-axis adaptive capability, which can be stimulated experimentally when head rotation is around one axis and visual motion about another. From this visual-vestibular conflict the brain infers that the slow-phase eye movement is rotating around the wrong axis. We explored the capability of human cross-axis adaptation, using a short-term training paradigm, to determine if torsional eye movements could be elicited by yaw (horizontal) head rotation (where torsion is normally inappropriate). We applied yaw sinusoidal head rotation (+/-10 degrees, 0.33 Hz) and measured eye movement responses in the dark, and before and after adaptation. The adaptation paradigm lasted 45-60 min, and consisted of the identical head motion, coupled with a moving visual scene that required one of several types of eye movements: (1) torsion alone (-Roll); (2) horizontal/torsional, head right/CW torsion (Yaw-Roll); (3) horizontal/torsional, head right/CCW torsion (Yaw+Roll); (4) horizontal, vertical, torsional combined (Yaw+Pitch-Roll); and (5) horizontal and vertical together (Yaw+Pitch). The largest and most significant changes in torsional amplitude occurred in the Yaw-Roll and Yaw+Roll conditions. We conclude that short-term, cross-axis adaptation of torsion is possible but constrained by the complexity of the adaptation task: smaller torsional components are produced if more than one cross-coupling component is required. In contrast, vertical cross-axis components can be easily trained to occur with yaw head movements.

Non-NASA Center↗
NASA NTRS1937

Air Propellers in Yaw

Report presents the results of tests conducted at Stanford University of a 3-foot model propeller at four pitch settings and at 0 degree, 10 degrees, 20 degrees, and 30 degrees yaw. In addition to the usual propeller coefficients, cross-wind and vertical forces and yawing, pitching, and rolling moments were determined about axes having their origin at the intersection of the blade axis and the axis of rotation. The tests showed that the maximum efficiency was reduced only slightly for angles of yaw up to 10 degrees but that at 30 degrees yaw the loss in efficiency was about 10 percent. In all cases the cross-wind force was found to be greater than the cross-wind component of the axial thrust. With a yawed propeller an appreciable thrust was found for v/nd for zero thrust at zero yaw. Yawing a propeller was found to induce a pitching moment that increased in magnitude with yaw.

Lesley, E P↗
NASA NTRS1959

Low-Speed Yawed-Rolling Characteristics of a Pair of 56-Inch-Diameter, 32-Ply-Rating, Type 7 Aircraft Tires

The low-speed (up to 4 miles per hour) yawed-rolling characteristics of two 56 x 16 32-ply-rating, type 7 aircraft tires under straight-yawed rolling were determined over a range of inflation pressures and yaw angles for a vertical load approximately equal to 75 percent of the rated vertical load. The quantities measured or determined included cornering force, drag force self-alining torque, pneumatic caster vertical tire deflection, yaw angle, and relaxation length. During straight-yawed rolling the normal force generally increased with increasing yaw angle within the test range. The self-alining torque increased to a maximum value and then decreased with increasing angle of yaw. The pneumatic caster tended to decrease with increasing yaw angle.

Thompson, Wilbur E.↗
DOE OSTI2023

Yaw-Augmented Control for Wind Farm Power Tracking: Preprint

This paper presents an inner-outer control loop structure which uses wake steering (yaw control) to augment pitch control for wind farms to track a power reference signal. The outer-loop yaw controller employs a recently proposed dynamic yaw model with a time-varying graph structure that accounts for dynamic changes in the farm wake interactions due to the yaw action of upstream turbines. The wake interactions within the model include the physics of the streamwise and lateral wake evolution, which collectively determine its impact on downstream turbines. The inner-loop employs a compensation scheme to account for the slow timescale effects of the yaw control actions within the faster timescale pitch control. The controller is applied to track two power trajectories (typical of secondary frequency regulation signals) using a large eddy simulation wind farm plant. The results demonstrate that the additional control authority from yaw provides some added benefit in reducing the required turbine derates needed for wind farms to track transient power increases in the proposed setting. However, the benefit decreases and pitch control alone is sufficient when the turbines are derated beyond a certain level. These findings suggest that augmenting pitch control with yaw may provide financial incentives in terms of allowing wind farms to maximize power supply to the bulk power market while still providing regulation services. Further work is needed analyze the costs versus benefits of the additional control complexity versus bandwidth in augmenting pitch control with wake steering offers in these applications.

active power control↗