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• 1. [Article] Blade Number Effects in a Scaled Down Wind Farm

  Two 3 × 4 scaled down wind farms were analyzed to understand differences in wind turbine boundary layers when turbines operating at identical power coefficients have two or three blades. Mean streamwise ...

  Citation × Citation Citation Abstract Copy Title: Blade Number Effects in a Scaled Down Wind Farm Author: Newman, A. Jensen, Cal, Raúl Bayoán, Castillo, Luciano Year: 2015 Two 3 × 4 scaled down wind farms were analyzed to understand differences in wind turbine boundary layers when turbines operating at identical power coefficients have two or three blades. Mean streamwise velocities in two bladed turbine near wakes ranged between 10 and 100% larger than those in the three bladed case with large differences just behind the nacelle. In the rotor swept region of far wakes, mean velocity differences between the two arrays were about 10% (max) and became smaller with increasing streamwise direction. Contrary to these findings, regions above and below rotors become less similar deep in the array. Incoming flow to downstream turbines was shown to have greater Reynolds streamwise normal stress for three bladed rotors. Percentage differences ranged between about 30% for the second turbine down to 10% for the fourth turbine. Additionally, there is qualitative evidence that suggests incoming streamwise Reynolds normal stress becomes similar between the two types of turbines, indicating that asymptotically two and three bladed turbines could have similar fatigue loading properties. These results show that use of two bladed turbines would have the most impact when used in a wind farm's first two rows. Title: Blade Number Effects in a Scaled Down Wind Farm Author: Newman, A. Jensen, Cal, Raúl Bayoán, Castillo, Luciano Year: 2015 Two 3 × 4 scaled down wind farms were analyzed to understand differences in wind turbine boundary layers when turbines operating at identical power coefficients have two or three blades. Mean streamwise velocities in two bladed turbine near wakes ranged between 10 and 100% larger than those in the three bladed case with large differences just behind the nacelle. In the rotor swept region of far wakes, mean velocity differences between the two arrays were about 10% (max) and became smaller with increasing streamwise direction. Contrary to these findings, regions above and below rotors become less similar deep in the array. Incoming flow to downstream turbines was shown to have greater Reynolds streamwise normal stress for three bladed rotors. Percentage differences ranged between about 30% for the second turbine down to 10% for the fourth turbine. Additionally, there is qualitative evidence that suggests incoming streamwise Reynolds normal stress becomes similar between the two types of turbines, indicating that asymptotically two and three bladed turbines could have similar fatigue loading properties. These results show that use of two bladed turbines would have the most impact when used in a wind farm's first two rows. Close

• 2. [Article] Wake Character in the Wind Turbine Array: (Dis-)Organization, Spatial and Dynamic Evolution and Low-dimensional Modeling

  To maximize the effectiveness of the rapidly increasing capacity of installed wind energy resources, new models must be developed that are capable of more nuanced control of each wind turbine so that each ...

  Citation × Citation Citation Abstract Copy Title: Wake Character in the Wind Turbine Array: (Dis-)Organization, Spatial and Dynamic Evolution and Low-dimensional Modeling Author: Hamilton, Nicholas Michael Year: 2016 To maximize the effectiveness of the rapidly increasing capacity of installed wind energy resources, new models must be developed that are capable of more nuanced control of each wind turbine so that each device is more responsive to inflow events. Models used to plan wind turbine arrays and control behavior of devices within the farm currently make questionable estimates of the incoming atmospheric flow and update turbine configurations infrequently. As a result, wind turbines often operate at diminished capacities, especially in arrays where wind turbine wakes interact and inflow conditions are far from ideal. New turbine control and wake prediction models must be developed to tune individual devices and make accurate power predictions. To that end, wind tunnel experiments are conducted detailing the turbulent flow in the wake of a wind turbine in a model-scale array. The proper orthogonal decomposition (POD) is applied to characterize the spatial evolution of structures in the wake. Mode bases from distinct downstream locations are reconciled through a secondary decomposition, called double proper orthogonal decomposition (DPOD), indicating that modes of common rank in the wake share an ordered set of sub-modal projections whose organization delineates underlying wake structures and spatial evolution. The doubly truncated basis of sub-modal structures represents a reduction to 0.015% of the total degrees of freedom of the wind turbine wake. Low-order representations of the Reynolds stress tensor are made using only the most dominant DPOD modes, corrected to account for energy excluded from the truncated basis with a tensor of constant coefficients defined to rescale the low-order representation of the stresses to match the original statistics. Data from the wind turbine wake are contrasted against simulation data from a fully-developed channel flow, illuminating a range of anisotropic states of turbulence. Complexity of flow descriptions resulting from truncated POD bases is suppressed in severe basis truncations, exaggerating anisotropy of the modeled flow and, in extreme cases, can lead to the loss of three dimensionality. Constant corrections to the low-order descriptions of the Reynolds stress tensor reduce the root-mean-square error between low-order descriptions of the flow and the full statistics as much as 40% and, in some cases, reintroduce three-dimensionality to severe truncations of POD bases. Low-dimensional models are constructed by coupling the evolution of the dynamic mode coefficients through their respective time derivatives and successfully account for non-linear mode interaction. Deviation between time derivatives of mode coefficients and their least-squares fit is amplified in numerical integration of the system, leading to unstable long-time solutions. Periodic recalibration of the dynamical system is undertaken by limiting the integration time and using a virtual sensor upstream of the wind turbine actuator disk in to read the effective inflow velocity. A series of open-loop transfer functions are designed to inform the low-order dynamical system of the flow incident to the wind turbine rotor. Validation data shows that the model tuned to the inflow reproduces dynamic mode coefficients with little to no error given a sufficiently small interval between instances of recalibration. The reduced-order model makes accurate predictions of the wake when informed of turbulent inflow events. The modeling scheme represents a viable path for continuous time feedback and control that may be used to selectively tune a wind turbine in the effort to maximize power output of large wind farms. Title: Wake Character in the Wind Turbine Array: (Dis-)Organization, Spatial and Dynamic Evolution and Low-dimensional Modeling Author: Hamilton, Nicholas Michael Year: 2016 To maximize the effectiveness of the rapidly increasing capacity of installed wind energy resources, new models must be developed that are capable of more nuanced control of each wind turbine so that each device is more responsive to inflow events. Models used to plan wind turbine arrays and control behavior of devices within the farm currently make questionable estimates of the incoming atmospheric flow and update turbine configurations infrequently. As a result, wind turbines often operate at diminished capacities, especially in arrays where wind turbine wakes interact and inflow conditions are far from ideal. New turbine control and wake prediction models must be developed to tune individual devices and make accurate power predictions. To that end, wind tunnel experiments are conducted detailing the turbulent flow in the wake of a wind turbine in a model-scale array. The proper orthogonal decomposition (POD) is applied to characterize the spatial evolution of structures in the wake. Mode bases from distinct downstream locations are reconciled through a secondary decomposition, called double proper orthogonal decomposition (DPOD), indicating that modes of common rank in the wake share an ordered set of sub-modal projections whose organization delineates underlying wake structures and spatial evolution. The doubly truncated basis of sub-modal structures represents a reduction to 0.015% of the total degrees of freedom of the wind turbine wake. Low-order representations of the Reynolds stress tensor are made using only the most dominant DPOD modes, corrected to account for energy excluded from the truncated basis with a tensor of constant coefficients defined to rescale the low-order representation of the stresses to match the original statistics. Data from the wind turbine wake are contrasted against simulation data from a fully-developed channel flow, illuminating a range of anisotropic states of turbulence. Complexity of flow descriptions resulting from truncated POD bases is suppressed in severe basis truncations, exaggerating anisotropy of the modeled flow and, in extreme cases, can lead to the loss of three dimensionality. Constant corrections to the low-order descriptions of the Reynolds stress tensor reduce the root-mean-square error between low-order descriptions of the flow and the full statistics as much as 40% and, in some cases, reintroduce three-dimensionality to severe truncations of POD bases. Low-dimensional models are constructed by coupling the evolution of the dynamic mode coefficients through their respective time derivatives and successfully account for non-linear mode interaction. Deviation between time derivatives of mode coefficients and their least-squares fit is amplified in numerical integration of the system, leading to unstable long-time solutions. Periodic recalibration of the dynamical system is undertaken by limiting the integration time and using a virtual sensor upstream of the wind turbine actuator disk in to read the effective inflow velocity. A series of open-loop transfer functions are designed to inform the low-order dynamical system of the flow incident to the wind turbine rotor. Validation data shows that the model tuned to the inflow reproduces dynamic mode coefficients with little to no error given a sufficiently small interval between instances of recalibration. The reduced-order model makes accurate predictions of the wake when informed of turbulent inflow events. The modeling scheme represents a viable path for continuous time feedback and control that may be used to selectively tune a wind turbine in the effort to maximize power output of large wind farms. Close

• 3. [Article] Conditional Averaging and Classification in the Near Wake of a Wind Turbine Array Boundary Layer

  Flow perturbation induced by interaction with a turbine rotor produces considerable turbulence which can alter the productivity of subsequent units within a wind farm. Previous methods have characterized ...

  Citation × Citation Citation Abstract Copy Title: Conditional Averaging and Classification in the Near Wake of a Wind Turbine Array Boundary Layer Author: Smith, Sarah E. Year: 2017 Flow perturbation induced by interaction with a turbine rotor produces considerable turbulence which can alter the productivity of subsequent units within a wind farm. Previous methods have characterized near wake vorticity of a single turbine as well as recovery distance for various turbine array configurations. This study aims to build from previous methods of analysis from the perspective of the rotor well within a turbine array and develop a model to examine points of significant imposition in relation to rotational effects. Hot wire anemometry was employed downstream of a turbine located in the middle of the third row in a 3x3 array with data points selected in a plane parallel to the turbine rotor. Data was collected in two separate periods capturing simultaneous streamwise and wall normal velocity components and simultaneous streamwise and transverse velocity components. Contour plots of the data were constructed and analyzed in relation to rotor position. The proposed modeling method was successful in that Reynolds stresses induced by the rotor in the near wake region aligned with rotor position were visible and well-defined in expected locations. Furthermore, examination of stresses in terms of streamwise and transverse velocity components clearly depicted rotational effects induced by the rotor. Title: Conditional Averaging and Classification in the Near Wake of a Wind Turbine Array Boundary Layer Author: Smith, Sarah E. Year: 2017 Flow perturbation induced by interaction with a turbine rotor produces considerable turbulence which can alter the productivity of subsequent units within a wind farm. Previous methods have characterized near wake vorticity of a single turbine as well as recovery distance for various turbine array configurations. This study aims to build from previous methods of analysis from the perspective of the rotor well within a turbine array and develop a model to examine points of significant imposition in relation to rotational effects. Hot wire anemometry was employed downstream of a turbine located in the middle of the third row in a 3x3 array with data points selected in a plane parallel to the turbine rotor. Data was collected in two separate periods capturing simultaneous streamwise and wall normal velocity components and simultaneous streamwise and transverse velocity components. Contour plots of the data were constructed and analyzed in relation to rotor position. The proposed modeling method was successful in that Reynolds stresses induced by the rotor in the near wake region aligned with rotor position were visible and well-defined in expected locations. Furthermore, examination of stresses in terms of streamwise and transverse velocity components clearly depicted rotational effects induced by the rotor. Close