Turbulence-resolving simulations of wind turbine wakes

Turbulence-resolving simulations of wind turbine wakes are presented using a high--order flow solver combined with both a standard and a novel dynamic implicit spectral vanishing viscosity (iSVV and dynamic iSVV) model to account for subgrid-scale (SGS) stresses. The numerical solutions are compared against wind tunnel measurements, which include mean velocity and turbulent intensity profiles, as well as integral rotor quantities such as power and thrust coefficients. For the standard (also termed static) case the magnitude of the spectral vanishing viscosity is selected via a heuristic analysis of the wake statistics, while in the case of the dynamic model the magnitude is adjusted both in space and time at each time step. The study focuses on examining the ability of the two approaches, standard (static) and dynamic, to accurately capture the wake features, both qualitatively and quantitatively. The results suggest that the static method can become over-dissipative when the magnitude of the spectral viscosity is increased, while the dynamic approach which adjusts the magnitude of dissipation locally is shown to be more appropriate for a non-homogeneous flow such that of a wind turbine wake

Numerical investigation of plasma-controlled turbulent jets for mixing enhancement

Plasma-controlled turbulent jets are investigated by means of Implicit Large–Eddy Simulations at a Reynolds number equal to 460,000 (based on the diameter of the jet and the centreline velocity at the nozzle exit). Eight Dielectric Barrier Discharge (DBD) plasma actuators located just before the nozzle exit are used as an active control device with the aim to enhance the mixing of the jet. Four control configurations are presented in this numerical study as well as a reference case with no control and a tripping case where a random forcing is used to destabilize the nozzle boundary layer. Visualisations of the different cases and time-averaged statistics for the different controlled cases are showing strong modifications of the vortex structures downstream of the nozzle exit, with a substantial reduction of the potential core, an increase of the jet radial expansion and an improvement of the mixing properties of the flow

FR3D: Three-dimensional Flow Reconstruction and Force Estimation for Unsteady Flows Around Extruded Bluff Bodies via Conformal Mapping Aided Convolutional Autoencoders

In many practical fluid dynamics experiments, measuring variables such as velocity and pressure is possible only at a limited number of sensor locations, \textcolor{black}{for a few two-dimensional planes, or for a small 3D domain in the flow}. However, knowledge of the full fields is necessary to understand the dynamics of many flows. Deep learning reconstruction of full flow fields from sparse measurements has recently garnered significant research interest, as a way of overcoming this limitation. This task is referred to as the flow reconstruction (FR) task. In the present study, we propose a convolutional autoencoder based neural network model, dubbed FR3D, which enables FR to be carried out for three-dimensional flows around extruded 3D objects with different cross-sections. An innovative mapping approach, whereby multiple fluid domains are mapped to an annulus, enables FR3D to generalize its performance to objects not encountered during training. We conclusively demonstrate this generalization capability using a dataset composed of 80 training and 20 testing geometries, all randomly generated. We show that the FR3D model reconstructs pressure and velocity components with a few percentage points of error. Additionally, using these predictions, we accurately estimate the Q-criterion fields as well lift and drag forces on the geometries.Comment: 29 pages, 10 figures. Accepted at International Journal of Heat and Fluid Flo