Bulletin of the American Physical Society
75th Annual Meeting of the Division of Fluid Dynamics
Volume 67, Number 19
Sunday–Tuesday, November 20–22, 2022; Indiana Convention Center, Indianapolis, Indiana.
Session L14: Energy: Wind Power - Modeling, Entrainment & Loads |
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Chair: Nicholas Hamilton, National Renewable Energy Laboratory Room: 141 |
Monday, November 21, 2022 8:00AM - 8:13AM |
L14.00001: On Carbon Entrainment in the Wind Turbine Wake Clarice Nelson, Venkatesh Pulletikurthi, Luciano Castillo Carbon dioxide (CO2) emissions from dispersed sources, such as ground vehicles and airplanes, are difficult to eliminate and play a significant role in global warming. A potential solution, Direct Air Capture (DAC) of CO2, is currently uneconomical due to the low relative concentration of CO2 in the air. It has been shown in previous studies that turbulent energy fluxes play a major role in horizontal axis wind turbine's (HAWT) energy entrainment. We demonstrate that there are conditions where this mechanism may not only increase power generation, but also the local concentration of CO2 in the wake of the turbine. Mass transport equations are incorporated into NREL's SOWFA LES solver to simulate a series of 5MW HAWT under a neutral Atmospheric Boundary Layer with several CO2 profiles; including uniform, logarithmic, and empirical. The mass entrainment fluxes are calculated in the wake to identify what conditions result in increased CO2 concentration, and where - to be utilized to increase economic viability of DAC technologies. |
Monday, November 21, 2022 8:13AM - 8:26AM |
L14.00002: Experimental Study of CO2 Capture in a Model Wind Turbine Array Abigayle E Moser, Antonio Esquivel-Puentes, Zackary F Van Zante, Vito Francioso, Oluwatuyi N Johnson, Clarice Nelson, Shyuan Cheng, Leonardo Chamorro, Mirian Velay-Lizancos, Luciano Castillo Mitigating climate change requires a multi-faceted approach through actively removing carbon dioxide (CO2) from the atmosphere and reducing emissions. Direct air capture (DAC) of CO2 is a tool for reducing the impact of human-induced climate change; however, the relatively low concentration of CO2 capture potential in the lower atmosphere makes this technology inefficient. Various studies emphasize the role turbulent kinetic energy (TKE) fluxes play in energy entrainment in horizontal axis wind turbines (HAWTs). Here, we explore how turbulent kinetic energy fluxes allow CO2 capture within a model wind turbine array. Experiments were performed in a wind tunnel with several CO2 concentration profiles mimicking those in the field to examine the viability of enhanced carbon capture. Advanced flow diagnostics, including particle image velocimetry, infrared imaging, and CO2 concentration sensors, are used to quantify local CO2 increase concentration within the turbine array. |
Monday, November 21, 2022 8:26AM - 8:39AM |
L14.00003: Rotor Aerodynamics, Aeroelastics, and Wake (RAAW) Campaign Overview Nicholas Hamilton, Paula Doubrawa, Jonathan W Naughton, Christopher L Kelley The Rotor Aerodynamics Aeroelastics and Wakes (RAAW) project is Megawatt-scale wind energy field experiment and model validation campaign. A suite of remote sensing and in situ instrumention will simultaneously measure the turbine inflow, loads, performance, and wake. A variety of the national laboratories' wind turbine simulations tools and models will be validated with one of the most sophisticated datasets ever collected for a single wind turbine. This project seeks to produce a step change in simulation fidelity and in our understanding of large-rotor physics by applying new data assimilation techniques that enables time-resolved, high-fidelity model comparisons to the experimental data. The data collection campaign will last for an entire year to ensure a robust and diverse dataset for a range of naturally occurring wind conditions and weather events, as well as turbine stop and fault conditions. The complexity of the experiment and validation efforts are made possible by a collaboration of a large team at the national laboratories and a commercial wind turbine manufacturer. |
Monday, November 21, 2022 8:39AM - 8:52AM Author not Attending |
L14.00004: Evaluating the dynamics loads of 2 blades and 3 blades wind turbines on different hurricanes category and power generation. Oluwatuyi N Johnson, Venkatesh Pulletikurthi, Luciano Castillo, Diego A Siguenza, Helber A Esquivel-Puentes, Arquimedes Ruiz, Andres W Cubero-Cruz, Arindam Chowdhury, James Erwin, Johnny Estephan Wind turbine blade configuration and number play a significant role in the dynamic loading and power generation. With the growing demand for wind energy all over the world, it's important to analyze how wind turbines could withstand the hurricane prone areas specifically in the Caribbean and East coast of USA. Hurricanes can seriously damage wind farms both offshore and onshore. Thus, the aim of this research is to evaluate experimentally the dynamic loads and power output of 2 blades vs 3 blades for Horizontal axis wind turbines (HAWT). Experiments were performed at the Wall of Wind (WOW) facility at FIU which can generate 70 m/s wind speed (Category 5). Our focus is to quantify the risk of failures of wind turbines due to hurricanes-induced wind on the blades, tower and the rotor for both 2 blades and 3 blades wind turbines. We will also compare the loads and the power output of the wind turbines at normal wind speeds to the high-speed winds. |
Monday, November 21, 2022 8:52AM - 9:05AM |
L14.00005: An experimental study on the influence of gust and debris on the wake flow characteristics of wind turbines Babak Ranjbaran, Jesus O Rodriguez-Garcia, Bahadir Turkyilmaz, Armann Gylfason The operational efficiency of wind turbines is highly dependent on environmental conditions. Impacting particles such as hail, dust, rain, and other debris that may be suspended in the air, such as during a strong wind gust, can significantly impact the wind turbines and overall performance of wind farms over the short and long term. As a result, studying the characteristics of the flow during such intense events is essential, with the aim of developing mitigation strategies. In this experimental study, we attempt to simulate gust events that are sufficiently strong to suspend particles that subsequently impact small-scale wind turbines. A porous disk is placed in a water tank where rotating impellers generate the background turbulence field. A strong gust event is intermittently introduced by deformation of the flow field resulting from the motion of a rapidly moving plate. The fluid is seeded with inertial particles that settle on the bottom of the turbulent flow field but are resuspended due to the gust event. Particle Image Velocimetry and Lagrangian Particle Tracking are used to analyze the flow field in the near wake behind the porous disk. |
Monday, November 21, 2022 9:05AM - 9:18AM |
L14.00006: Effect of turbulence length scales on wind turbine loads Brooke J. Stanislawski, Ashesh Sharma, Regis Thedin, Ganesh Vijayakumar As wind turbines become larger, the temporal and spatial variations in the incoming flow field play a more important role in the structural loading of the turbine. Current design standards assume that using a single integral length scale to characterize the turbulent inflow will result in a robust design for all large turbines. However, turbines face different turbulence characteristics and atmospheric stabilities that are not reflected by a single length scale. Here, we quantify the impact of turbulent flow fields on turbine loads to identify which length scales are important. To generate turbulent flow fields of varying integral length scales and atmospheric stabilities, we use the Mann spectral tensor model and high-fidelity numerical simulations of the atmospheric boundary layer using AMR-Wind, a part of the ExaWind modeling and simulation framework. Preliminary results indicate that integral length scales have the potential to impact the standard deviation of turbine loads by a factor of up to 3.5. |
Monday, November 21, 2022 9:18AM - 9:31AM |
L14.00007: Testing and applying the filtered actuator line model for very coarse scales in LES of large wind farms Xiaowei Zhu, Dennice Gayme, Charles Meneveau In the actuator line model (ALM), the wind turbine blade forces are usually applied on the flow field using a Gaussian smearing function. It is known that an optimal kernel length of about 25% chord length is required for accurate ALM-based force and flow predictions. However, for Large Eddy Simulations (LES) of extended wind farms, coarse grid resolutions and much coarser filter kernel sizes are typically required. In this study, the filtered actuator line model proposed by Martinez-Tossas and Meneveau (J. Fluid Mech. 2019, vol 863, pp 269-292) is tested and implemented at very large filtering scales, as large as 25% of rotor radius. We evaluate the accuracy of the ALM implementation by performing simulations for both an idealized blade (constant chord wing) and a wind turbine blade (NREL5MW) in uniform inflow, confirming that filter-scale independent predictions are obtained. We then compare the results with predictions from blade-element momentum (BEM) calculations and prior simulations with the optimal filtering size. We push the simulation grid to a resolution at which ALM-based LES of very large wind farms becomes feasible. |
Monday, November 21, 2022 9:31AM - 9:44AM |
L14.00008: Optimization performance analysis and validation of the FLOW Estimation and Rose Superposition (FLOWERS) model Michael LoCascio, Luis A Martinez-Tossas, Christopher J Bay, Garrett Barter, Catherine Gorle A common objective of a wind plant layout optimization study is to maximize total annual energy production (AEP). AEP is typically calculated as a numerical integral of a wind farm's power production across discrete wind speed-direction bins, each of which requires a separate simulation. The FLOW Estimation and Rose Superposition (FLOWERS) model estimates the annually-averaged wake velocity flow field by taking an analytical integral of a wake deficit model across every wind direction. This new approach is well-suited to the layout optimization problem, as a more efficient method to calculate AEP can yield substantial savings in computational time over potentially thousands of model evaluations. We explore further proof-of-concept of the FLOWERS model in this work. First, we conduct a comprehensive comparison of optimization performance and cost between FLOWERS and the conventional layout optimization framework. Second, we validate the FLOWERS estimates of average wake velocity compared with large eddy simulation predictions. |
Monday, November 21, 2022 9:44AM - 9:57AM |
L14.00009: A unified hierarchical wind farm modeling system Yongjie Lu, Bin Ma, Ravon Venters, Oumaima Lamaakel, Marina Astitha, Georgios Matheou Offshore wind is an abundant energy resource with significant environmental and economic benefits, but as a natural resource, it is variable. A modeling system is developed to improve the design and operation of wind farms by enabling realistic modeling of the atmospheric boundary layer at the wind-farm scale. A hierarchy of modeling methods is used to model the energy flow through the atmosphere, beginning with weather prediction of the entire atmosphere. The output of the global model is then used as input to the Weather Research and Forecasting (WRF) Model, a regional weather model, that captures wind patterns at smaller scales of about 250 meters. The output of the regional model is passed on in turn to a high-resolution large-eddy simulation (LES) model that includes the interaction of the time-depended three-dimensional wind field with the wind farm. To generate a realistic turbulent inflow condition to the wind farm, two concurrently running LES simulations are performed. The auxiliary LES is forced with the WRF model output. A vertical plane from the auxiliary LES provides the inflow fields for the main LES, which includes the wind farm. The present work extends large-eddy simulations of entire wind farms to realistic atmospheric conditions enabling accurate wind farm design, operations, and energy forecasting. |
Monday, November 21, 2022 9:57AM - 10:10AM |
L14.00010: Stochastic dynamical modeling of wake turbulence in wind farms Armin Zare, Aditya H Bhatt, Federico Bernardoni, Mireille Rodrigues, Stefano Leonardi Low-fidelity analytical models of turbine wakes have traditionally been used for wind farm planning, performance evaluation, and demonstrating the utility of advanced control algorithms in increasing the annual energy production. In practice, however, it remains challenging to correctly estimate the flow and achieve significant performance gains using controllers that are based on such models. This is due to the over-simplified static nature of wake predictions from models that are agnostic to the complex aerodynamic interactions among turbines. To improve the predictive capability of low-fidelity models while remaining amenable to control design, we offer a stochastic dynamical modeling framework for capturing the effect of atmospheric turbulence on the thrust force and power generation as determined by the actuator disk concept. We use stochastically forced linearized NS equations to model a turbulent velocity field that complements the analytically computed static wake velocity. This enables us to achieve consistency with the predictions of higher-fidelity models in capturing power and thrust force measurements. The model is also capable of predicting the turbulence intensities both in the presence and absence of yaw misalignment. The power-spectral densities of our stochastic models are identified via convex optimization to ensure consistency with partially available velocity statistics or power and thrust force measurements. Our results provide insight into the significance of sparse field measurements in recovering the statistical signature of the flow using stochastic linear models. |
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