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

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

Analysis of Models Under Location Uncertainty within the Framework of Coarse Large Eddy Simulation (cLES)

International audienceLarge Eddy Simulations (LES) have become common place in the current research scenario with increasing computational resources. However, constraints still limit the application of LES in a variety of scenarios: high Reynolds (Re) number flows, complex geometry flows, or flows involving complicated wall boundary layers. While the last scenario is limited due to physical aspects of the model, the first two can be rectified by reducing the computational cost of performing LES. An immediate foreseeable solution is to reduce the number of computational points in the simulation. However, this leads to a stark decrease in accuracy for an LES model. Complex methodologies have been developed to negate this decrease such as hybrid RANS-LES models: using RANS model (respec. LES model) for coarse grid (respec. fine grid) regions. In this study, the focus is on physical behaviour characterisation of novel models under location uncertainty [1] in a coarse mesh construct. Analysis and comparisons with the performances of classic LES models, decreasing in accuracy with increasingly coarse meshes, are conducted. The models under location uncertainty originate from the stochastic mass and momentum conservation equations which are derived using stochastic calculus. Similar to a filtered NS equation for LES, the stochastic version contains a sub-grid scale dissipation terms – this term is fully specified; there is thus no need to rely on the additional Boussinesq viscosity assumption. It also contains a sub-grid scale velocity bias term acting on the advection component-this term is related to a phenomenon termed 'turbophoresis' in literature and is usually not taken into account in classical sub-grid modelling. Both terms are characterised by the small-scale velocity auto-correlation (a = σσ T) which requires modelling. While a Smagorinsky-like model under location uncertainty (StSm) can be envisaged (through a local isotropy assumption), the performance of these models excels when a local variance based a (StSp – spatial variance; StTe – temporal variance) is realised. The performance of these models is compared with the classic (Smag) and dynamic Smagorinsky (DSmag) models, and the Wall-Adaptive Local Eddy viscosity (WALE) model. Two well-studied flows, namely wake flow around cylinder at Re = 3900, and channel flow at Re t = 395, are used to analyse the performance of the models under a coarse resolution with reference statistics from [2] for wake flow (see fig 1.) and [3] for channel flow (see fig 2.). The statistical correlations are shown to be better even at low resolutions for the models under location uncertainty while the classical LES models are either inaccurate or numerically unstable. A flow with well-resolved vortices is observed with the models under location uncertainty and they also capture the important turbulent characteristics of a given flow better than the classical models