In this study, we address the question of how kinetic energy is entrained into large wind turbine arrays and, in particular, how large-scale flow structures contribute to such entrainment. Previous research has shown this entrainment to be an important limiting factor in the performance of very large arrays where the flow becomes fully developed and there is a balance between the forcing of the atmospheric boundary layer and the resistance of the wind turbines. Given the high Reynolds numbers and domain sizes on the order of kilometers, we rely on wall-modeled large eddy simulation (LES) to simulate turbulent flow within the wind farm. Three-dimensional proper orthogonal decomposition (POD) analysis is then used to identify the most energetic flow structures present in the LES data. We quantify the contribution of each POD mode to the kinetic energy entrainment and its dependence on the layout of the wind turbine array. The primary large-scale structures are found to be streamwise, counter-rotating vortices located above the height of the wind turbines. While the flow is periodic, the geometry is not invariant to all horizontal translations due to the presence of the wind turbines and thus POD modes need not be Fourier modes. Differences of the obtained modes with Fourier modes are documented. Some of the modes are responsible for a large fraction of the kinetic energy flux to the wind turbine region. Surprisingly, more flow structures (POD modes) are needed to capture at least 40% of the turbulent kinetic energy, for which the POD analysis is optimal, than are needed to capture at least 40% of the kinetic energy flux to the turbines. For comparison, we consider the cases of aligned and staggered wind turbine arrays in a neutral atmospheric boundary layer as well as a reference case without wind turbines. While the general characteristics of the flow structures are robust, the net kinetic energy entrainment to the turbines depends on the presence and relative arrangement of the wind turbines in the domain.

In this study, horizontally periodic large eddy simulations (LES) are utilized to study turbulent atmospheric boundary-layer flow over wind turbines in the far-downstream portion of a large wind farm where the wakes have merged and the flow is fully developed. In an attempt to increase power generation by enhancing the mean kinetic energy (MKE) entrainment to the wind turbines, hypothetical synthetic forcing is applied to the flow at the turbine rotor locations. The synthetic forcing is not meant to represent any existing devices or control schemes, but rather acts as a proof of concept to inform future designs. The turbines are modeled using traditional actuator disks, and the unconventional synthetic forcing is applied in the vertical direction with the magnitude and direction dependent on the instantaneous velocity fluctuation at the rotor disk; in one set of LES meant to enhance the vertical entrainment of MKE, a downward force is prescribed in conjunction with a positive axial velocity fluctuation, whereas a negative axial velocity fluctuation results in an upward force. The magnitude of the forcing is proportional to the instantaneous thrust force with prefactors ranging from 0.1 to 1. The synthetic vertical forcing is found to have a significant effect on the power generated by the wind farm. Consistent with previous findings, the MKE Energies 2015, 8 371 flux to the level of the turbines is found to vary along with the total power produced by the wind turbine array. The reverse strategy of downward forcing of slow axial velocity flow is found to have almost no effect on the power output or entrainment. Several of the scenarios tested, e.g., where the vertical force is of similar magnitude to the horizontal thrust, would be very difficult to implement in practice, but the simulations serve the purpose of identifying trends and bounds on possible power increases from flow modifications through action at the turbine rotor.

Wind resource assessments are used to estimate a wind farm's power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshore farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non-uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. We show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice. KEYWORDSonshore wind farms, statistical modeling, turbine wakes, wind power assessment INTRODUCTIONWind energy is a fast-growing segment of the energy sector worldwide. As wind starts to play a larger role in our electricity systems, there is increasing interest in using it efficiently. Wind power is, for the most part, not a dispatchable form of energy; forecasts are not perfect, and an individual wind farm cannot be set to a desired production level unless the wind conditions are sufficient for that level of power. Managing a farm's variability and intermittency is still a big challenge, but recent research shows promising results in optimizing farm production. 1-4 Knowledge of the amount of power that a wind farm will produce in a given hour, day, or year will improve the way that the farm is operated and financed. Perfect knowledge of future wind power production is impossible, but recent research has made great strides in estimating these quantities. Power estimation and prediction is important for multiple applications within the timeline of a wind energy project. In the initial planning stages of a farm, one must develop accurate estimates of what the wind conditions will be once the farm is built. This typically involves the installation of meteorological towers (or met towers) for data collection. This information is used to determine the turbine layout and alignment within the farm that will best capture the available energy. Initial estimates of long-term wind conditions are used to assess the financial viability of a wind farm. Thus, the decision of farm design and resource assessment is critical, as the return on investment depends on the ability of the wind farm to generate enough en...

This work provides a detailed description of the setup and execution of an experiment employing Magnetic Resonance Thermometry (MRT) techniques for measuring the three-dimensional temperature field of a fully turbulent jet mixing with a cross flow. The proposed methodology has the flexibility of applying different thermal boundary conditions — adiabatic and conductive — by varying the materials used in the test section as well as varying the temperatures of the mixing flows. The experiment described in this paper employs a standard magnetic resonance imaging system comparable to those used in medical radiology departments worldwide. A series of MR scans with both isothermal and thermal mixing conditions were conducted and results are presented with sub-millimeter resolution across the measured 3D domain of interest within one degree Celsius. The methodology presented here holds unique advantages over conventional techniques because measurements can be acquired without introducing flow disturbances and in regions without any optical access. When coupled with other established MR-based measurement techniques, MRT provides large, robust data sets that can be used for validation, design, and insight into system thermal performance for complex, turbulent flows. The materials and components employed in this work cost approximately $13,900, and the experimental setup and data collection required approximately 48 hours.

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