PIV Experimental Comparison of Vertical Axis Wind Turbine Wake with Theoretical Models

Vertical Axis Wind Turbine (VAWT) operation is characterized by complex and unsteady three-dimensional fluid dynamics, which presents considerable challenges. One of the crucial points to understand is the complex interaction between rotor, inflow, and wake systems. If we can demonstrate its effectiveness in the complex flow/inflow conditions, will be of great importance. To investigate the wake/wind aerodynamics validate state-of-the-art VAWT wake models, a high ¬fidelity experimental measurement in the domain of VAWT wakes is needed. Computational fluid dynamics (CFD) models for complex wind interactions are far from being feasible. These models are highly time-devouring and computationally expensive, and their cost prohibits the simulation of complex flow configurations. This is usually overcome through the implementation of simple and computationally inexpensive analytical wake models, where the flow conditions are solved through simple analytic expressions and only over specific points of interest. The work aims to realize and validate a simple and efficient analytical wake model (Abkar, 2019 [2]), (Bastankhah & Porté¬Agel, 2014 [7]) for prediction of the wind velocity profile downwind of a VAWT turbine: The best by comparing the modeling results with a set of particle image velocimetry (PIV) measurements of the wakes of an in-house designed VAWT are used as a high-fidelity reference. The present work evaluates and quantifies the influence of the wake deflection produced by the pitch angles of the blades on the scaled VAWT turbine. It reproduces the main phenomena involved in the flow pattern and identifies the general structure of the resulting wake that occurs: under the influence of pitching the blades on the upstream turbine. The configuration consists of a VAWT aligned in the direction of the incoming flow: With a class of cases for different configurations of the turbine are studied for example ¬ deflection of wake through zero pitch, positive 10 degrees pitch, Negative 10 degrees pitch angles (Mendoza et al, 2019 [32]). The available power distribution over other hypothetical downwind turbines due to the influence of wake deflection by the upstream turbine is studied and reported. These interactions are measured with the help of large ¬scale PIV experiments in OJF, TU Delft. Also, an efficacious force balance system for the VAWT rotor is studied, designed, and fabricated (in-house) to calculate the accurate lift and drag forces on the VAWT structure. The stereo particle image velocimetry results are compared with two theoretical works Jensen wake model, [21] and Gaussian-based wake model Abkar [2] and CFD simulations by Huang et al [18]. The study shows a wake shift towards the windward side (cross-flow width), Negative¬ Y direction. For all the pitch cases, maximum for the positive pitch angle and least for a negative pitch angle. The wake shift in the negative ¬Y direction can be caused by the blade rotation, with the clockwise rotation of the VAWT rotor, and the rear blade turning into the incoming flow of wind. The theoretical models especially the Gaussian-based wake model represent the wake deficit in close agreement to the experimental results but fails to account for wake shift and change in wake structure. The CFD simulations reproduce the 3D wake structures consistent with the experiment results but show an early recovery in the wake. The wake recovery with downwind available power is found higher with a positive pitch angle and least with the negative pitch. A study of in-plane velocity vectors indicates crosswind force introducing crosswind momentum to the flow and as a result would give rise to the CVPs, also according to a study by Rolin F, Porte-Agel [38]. The theoretical models do not account for any of these structural changes in wake and the deflections in them. Furthermore, the suitability of these experimental references are used to verify the CFD simulations and analytical models’ range of the fidelityElectrical Engineering | Sustainable Energy Technolog