Bulletin of the American Physical Society
77th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 24–26, 2024; Salt Lake City, Utah
Session X37: Wind Energy |
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Chair: Amrit Shankar Verma, University of Maine Room: 355 C |
Tuesday, November 26, 2024 8:00AM - 8:13AM |
X37.00001: Application of a moving surface drag model to multiscale ocean surfaces Manuel Ayala, Raúl Bayoán B Cal, Dennice F Gayme, Charles Meneveau Accurately representing wind-wave interactions in large eddy simulations (LES) of the marine atmospheric boundary layer (MABL) is essential for improving climate models and optimizing offshore operations. The recently proposed MOving Surface Drag (MOSD) model enables cost-effective wall-modeled LES to capture phase information and momentum transfer from waves to turbulent airflow. This model enhances the surface gradient-based wall model by computing pressure drag from ideal potential flow interacting with the moving surface, approximated as piece-wise ramp flow, while unresolved waves are modeled using the standard equilibrium wall model. This study introduces extensions that include local unsteady effects due to time-dependent wave slopes and demonstrates the MOSD model's robustness and accuracy in predicting momentum transfer under a broad range of wind-wave conditions. Using LES with the MOSD model we simulate neutral atmospheric flow over various ocean wavefields generated based on a JONSWAP spectrum. The results are shown to closely match first-order turbulent statistics from more intensive simulations and the contributions of unsteady effects are quantified. The results show that using LES with the MOSD wall-model provides a cost-effective means to introduce realistic ocean wavefield effects onto MABL simulations and thus constitutes a practical method for incorporating wave effects in wind energy studies. |
Tuesday, November 26, 2024 8:13AM - 8:26AM |
X37.00002: Tower Top Motion of a Bottom-Fixed Offshore Wind Turbine in the Hammerhead Configuration: Root Causes and System Identification Tests Saravanan Bhaskaran, Max Kruse, Andrew Goupee, Amrit Verma Offshore wind turbine (OWT) installation is a challenging task and there are significant gaps in understanding their unique dynamics. The partially installed state of the OWT where the monopile, tower and the nacelle have been installed is called the hammerhead configuration. In this configuration, the nacelle exhibits complex tower top motions with rapidly changing elliptical orbits, which poses a major challenge to the safe deployment of OWTs. The aim of this study is to investigate the root causes behind the complex orbital motion of OWTs during installation. A numerical model of the hammerhead configuration of an OWT has been established. Various scenarios, including different sea states, wave headings, and alterations to the hammerhead configuration (i.e., varying eccentric distances and varying masses) were tested to evaluate the effect of each parameter individually. Additionally, a Froude-scaled experimental model of the OWT system was designed and constructed. System identification tests were carried out on the model to verify the natural frequencies and mode shapes of the as-built system. A novel methodology for designing a fully flexible scaled model of an OWT in hammerhead configurations apt for wave tank testing is presented. |
Tuesday, November 26, 2024 8:26AM - 8:39AM |
X37.00003: Vertical Entrainment of Mean Kinetic Energy in Offshore Wind Farms Zein Ahmad Sadek, Ondrej Fercak, Manuel Ayala, Dennice F Gayme, Charles Meneveau, Raúl Bayoán B Cal Fixed bottom and floating wind farms are two prevailing design methodologies for offshore wind, depending on the depth of the ocean floor. Fixed bottom farms are coupled to wave-induced wake modulation while floating wind farms include rotor misalignment due the dynamic response to turbulent aerodynamic and hydrodynamic forcing. The present work performs scaled wind tunnel experiments exploring the effects of different waves on various performance metrics for both fixed-bottom and floating wind farms. Experiments are conducted in the Portland State University wind and wave tunnel with a four-by-three wind farm spaced at 5D with 15 cm diameter turbines. Two wave steepness are used at four wavelengths, which are set as multiples of the farm spacing to elucidate the presence of harmonics. Coupled motion-capture and power measurements are made of the floating wind farm. Stereoscopic particle image velocimetry is used to capture multiple 2D-3C, streamwise-vertical planes. Time series of power-motion and row-to-row motion correlations are presented. Vertical momentum entrainment is calculated and compared across various wave cases, isolating the effects of waves on wake recovery. Wavelength is shown to cause phase-misalignment with turbine dynamics, which affects, and under certain conditions can enhance, wake recovery. This work provides deeper insight towards offshore farm optimization for both existing fixed-bottom turbines and future floating farms. |
Tuesday, November 26, 2024 8:39AM - 8:52AM |
X37.00004: Adaptive Importance Sampling for Enhancing Offshore Wind Turbine Reliability Yihan Liu, Michael Chertkov In this paper, we continue the reliability study initiated in [1] on the extreme values of key mechanical characteristics -- pitch, surge, and heave -- of a floating offshore wind turbine (FOWT). Utilizing a comprehensive list of wind and wave patterns that cause anomalously large deviations in FOWT characteristics, originally revealed via brute-force Markov Chain Monte Carlo (MCMC) simulations, we have developed an efficient Adaptive Importance Sampling (AIS) MCMC. This new approach enables us to bootstrap and uncover the tails of the probability distributions associated with even higher and potentially more damaging values of pitch, surge, and heave which are not accessible through standard MCMC. Enhanced modeling of fluctuations in the large-scale wind component has allowed us to identify and examine both previously known and new rare but dangerous regimes. Notably, using AIS-MCMC, we pinpoint and analyze a surge anomaly driven by rare coherent wind patterns with relatively low mean values and wave interactions that interfere with wind turbine control. |
Tuesday, November 26, 2024 8:52AM - 9:05AM |
X37.00005: Wind power forecast for the South Fork offshore wind farm Yongjie Lu, Tasnim Zaman, Marina Astitha, Georgios Matheou The South Fork wind farm, located 35 miles east of Montauk Point, NY, is the first commercial-scale offshore wind farm in the U.S. Offshore wind turbines operate in variable weather conditions, making it difficult to predict the energy output of the wind farm. This causes power dispatch challenges and leads to significant energy loss. To better characterize the meteorological conditions at the wind-farm scale, we develop a hierarchy of modeling methods to model the atmospheric boundary layer. Starting from the weather prediction of the entire atmosphere and the Weather Research and Forecasting Model (WRF), we obtain the average meteorological conditions at the wind farm. Then these results are used as an input for two concurrent large-eddy simulations (LES) to generate the time-depended three-dimensional wind field. The LES model is validated with data from experiments and simulations in literature. We apply our methods on the configurations of the South Fork wind farm, an offshore wind farm consisting of 12 turbines, and study the flow fields and power output of the wind farm under different meteorological conditions. Our work enables wind farm simulations with realistic atmospheric conditions and can be useful for wind farm operations and energy forecasting. |
Tuesday, November 26, 2024 9:05AM - 9:18AM |
X37.00006: Influence of Large-Scale Climate on Offshore Wind-Farm Scale Environment: A Deep Learning Approach Balu Nadiga, Anton Myshak, Raghavendra Krishnamurthy Characterization of the spatiotemporal variability of the local wind |
Tuesday, November 26, 2024 9:18AM - 9:31AM |
X37.00007: Intermittency of Aggregated Wind Power Productions Samy Lakhal, Jim Sardonia, Mahesh M Bandi Wind turbines convert the kinetic energy of wind into electric power. As a consequence, the produced power displays the same statistics as atmospheric turbulence. In particular, power fluctuations exhibit Kolmogorov scaling, multifractal statistics, and transition from antipersistent to persistent scaling when averaged over several farms. |
Tuesday, November 26, 2024 9:31AM - 9:44AM |
X37.00008: ABSTRACT WITHDRAWN
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Tuesday, November 26, 2024 9:44AM - 9:57AM |
X37.00009: Predicting Wind Power of an onshore wind farm in Complex Terrain with Large-Eddy Simulations (LES) Christian Jane Ippel, Rafael Palacios, Sylvain Laizet Onshore wind farms are significantly influenced by terrain topography, especially in hilly regions where flow can accelerate due to favorable pressure gradients, direction changes, high shear, and turbulence from flow separation. Accurately predicting the unsteady flow dynamics of atmosphere-to-wake and wake-to-wake interactions is crucial for determining turbine power output in these areas. Lower-fidelity models often miss these complex interactions, leading to inaccurate power predictions. In contrast, Large-Eddy Simulations (LES) are essential for capturing non-linear effects and providing the necessary fidelity for detailed flow analysis. Using the open-source framework Xcompact3d, we perform high-order LES of the atmospheric boundary layer (ABL) with an immersed boundary method and a wall model to replicate terrain features, along with actuator discs to parameterize the wind turbines. This simulation environment has been validated on a steep hill using previously published experimental and numerical data. In this talk, we present the application of our methodology to a real complex terrain for forecasting power production and validating results with SCADA data. The inlet ABL is reproduced in a precursor simulation by replicating the profile from a mast on the farm. The average power and flow statistics for the predominant wind direction are assessed along with a 30-degree window average. |
Tuesday, November 26, 2024 9:57AM - 10:10AM |
X37.00010: Wake-added turbulence characteristics and modeling in the stratified atmospheric boundary layer Kerry S Klemmer, Michael F Howland To achieve decarbonization targets, wind turbines are growing in hub height, rotor diameter, and are being deployed in new locations with diverse atmospheric conditions not previously seen, such as offshore. Physics-based analytical wake models commonly used for the design and control of wind farms simplify atmospheric boundary layer (ABL) and wake physics to achieve computational efficiency. This is accomplished primarily through a simplified model form that neglects certain flow processes, such as stratification, and through the parameterization of ABL and wake turbulence through a wake spreading rate. In this study, we analyze wind turbine wakes over a range of atmospheric stabilities and ambient turbulence intensities using large eddy simulation (LES). To parse the turbulence in the wake from the turbulent, incident ABL flow, we decompose the flow into the base ABL flow and the deficit flow produced by the turbine. The subsequent deficit budget analysis allows for isolation of wake-added quantities, such as wake-added turbulence kinetic energy, which we then utilize in Reynolds-Averaged Navier Stokes (RANS) based eddy viscosity models to predict mean wake momentum. With this dataset, we analyze the primary forcing and transport mechanisms that influence wake-added turbulence in stratified ABL flow. We also evaluate the assumptions present in engineering models for wake-added turbulence through turbulence budget analysis. |
Tuesday, November 26, 2024 10:10AM - 10:23AM |
X37.00011: Analysis of flow curvature and unsteady effects on the aerodynamic coefficients of a one-bladed vertical-axis turbine Philippe Rochefort, Louis Précourt, Gregoire Winckelmans, Guy Dumas To obtain realistic predictions of vertical-axis turbines (VAT), simplified models must use correct aerodynamic coefficients based on the local flow conditions. As the blades of a VAT operate in flows with curved streamlines, and in an unsteady manner, predicting realistic coefficients based on the local angle of attack becomes challenging. Indeed, the flow curvature and its unsteadiness both alter the aerodynamic coefficients of the blade. In this work, we first present a methodology to properly measure the aerodynamic coefficients (lift, drag and moment) of a NACA 0015 airfoil in steady curved flow at Rec = 6 x 106 using CFD with a "keyhole mesh domain", with varying the airfoil’s angle of attack, arm radius to airfoil's chord ratio (R/c), as well as the position of the airfoil's connection point to the arm. In particular, it is seen that the pressure drag is much affected by the flow curvature which we show to be related to a Coriolis effect. Furthermore, when performing CFD with added upstream cross-flow, it is also seen that the effect of flow unsteadiness is significant, producing a hysteresis curve of the aerodynamic coefficients over the rotation cycle. It is also shown that a simple quasi-steady correction model can predict the hysteresis effect fairly well. The findings of this investigation will help develop improved models for actuator line methods (ALM). |
Tuesday, November 26, 2024 10:23AM - 10:36AM |
X37.00012: A Novel Velocity Spectrum Model Incorporating Long-Range Dependence and Fractal Features Shyuan Cheng, Yaswanth S Jetti, Vincent S Neary, Martin Starzewski, Leonardo Chamorro Quantifying key processes in turbulent flows across different scales is essential in many engineering and scientific fields, particularly for analyzing unsteady aerodynamics and hydrodynamics. Traditional velocity spectra models often fall short in representing critical energy-containing regions over a broad range of scales. We present a novel spectral model that accurately characterizes velocity spectra across various scales to overcome these limitations. This new model is defined by a five-parameter covariance function, where each parameter has a |
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