Steven Tran

Large Eddy Simulation based on the Residual-based Variational Multiscale Method and Lagrangian Dynamic Smagorinsky Model

In this work, we focus on large eddy simulation (LES) using the finite element method. The use of finite elements is beneficial due to their flexibility for complex problems (e.g., complex geometry, high-order discretizations, adaptivity, parallelization). In order to achieve this, we combine aspects of the residual-based variational multiscale (RBVMS) approach, which provides the basis for stabilized finite element methods, and the dynamic Smagorinsky eddy-viscosity model. For the Smagorinsky model, we employ two dynamic procedures: one that employs spatial averaging over homogeneous directions (which is applicable to turbulent flows with statistical homogeneity) and the other is based on Lagrangian, or fluid pathline, averaging (which is applicable to inhomogeneous turbulent flows). In this study, we consider three models. One model is solely based on the RBVMS approach. The other two models combine the cross-stress terms due to the RBVMS model with the dynamic Smagorinsky eddy-viscosity model for the Reynolds stress terms. In the two combined models, we employ a different type of averaging in each model. We present results using these models on two cases: turbulent channel flow (Reτ = 590) and flow over a cylinder (ReD = 3, 900). For the turbulent channel flow, all models provide similar predictions while for the flow over a cylinder, the combined model provides a better prediction.

Finite element-based large eddy simulation using a combination of the variational multiscale method and the dynamic Smagorinsky model

ABSTRACT A finite element-based large eddy simulation (LES) is proposed using a combination of the residual-based variational multiscale (RBVMS) approach and the dynamic Smagorinsky eddy-viscosity model. In this combined model, the cross-stress terms are modelled using the RBVMS approach while the eddy-viscosity model is used to represent the Reynolds stresses. The eddy-viscosity is computed dynamically in a local fashion for which a localized version of the variational Germano identity is developed. To improve the robustness of the local dynamic procedure, two types of averaging schemes are considered. The first type employs spatial averaging over homogeneous direction(s) which is only applicable to turbulent flows with statistical homogeneity in at least one direction. The second type is based on Lagrangian averaging over fluid pathtubes, which is applicable to inhomogeneous turbulent flows. The predictions from the combined model are compared to the direct numerical simulation or experimental data and also to the predictions from the RBVMS model. This is done for two cases: turbulent flow in a channel (Re τ = 590) and flow over a cylinder (Re D = 3, 900). For the turbulent channel flow, predictions are similar between the RBVMS model and the combined model. For flow over a cylinder, the combined model provides better predictions, specifically for fluctuations in the streamwise velocity and lift.

Synthetic Jet based Active Flow Control of Dynamic Stall Phenomenon on Wind Turbines Under Yaw Misalignment

One of the largest contributors to the structural failure of wind turbines is the unsteady aerodynamic loading experienced by the blades. This can arise due to yaw misalignment, wind shear, gusting or a combination of these conditions. Under these conditions, cyclic blade loading occurs and dynamic stall phenomenon is possible which in-turn results in hysteresis and causes vibrations in turbine components. Therefore, it is important to mitigate, or even fully suppress, dynamic stall. In this paper we use numerical simulations to study synthetic-jet based active flow control to mitigate dynamic stall. The goal is to achieve fast-time response control with actuators that require low energy input and are physically compact. We focus on the NREL Phase VI turbine with the S809 airfoil shape. The baseline configuration (without synthetic jets) is modeled at below rated (7 m/s), rated (10 m/s), and above rated (15 m/s) wind speeds and at a yaw angle of 30◦. It is found that the unsteady loading due to yaw misalignment can cause power fluctuations of up to 9kW or 135% for each blade during one blade revolution. Next we study active flow control on a pitching S809 airfoil with a synthetic-jet actuator, where two wind conditions are considered that correspond to the yaw angle of 30◦for two wind speeds of 10 and 15 m/s at blade span of 60% and 80%, respectively. The jet is placed at 5% chord location (i.e., x/c = 0.05) and is activated at a non-dimensional frequency of 5. Synthetic-jet based control is shown to significantly reduce the flow separation near the leading edge and thus, reduce the hysteresis by up to 73% at the rated wind speed.

Enabling HPC simulation workflows for complex industrial flow problems

The use of simulation based engineering taking advantage of massively parallel computing methods by industry is limited due to the costs associated with developing and using high performance computing software and systems. To address industries ability to effectively include large-scale parallel simulations in daily production use, two key areas need to be addressed. The first is access to large-scale parallel computing systems that are cost effective to use. The second is support for complete simulation workflow execution on these systems by industrial users. This paper presents an approach, and set of associated software components, that can support industrial users on large-scale parallel computing systems available at various national laboratories, universities, or on clouds.

Large Eddy Simulation of Surging Airfoils with Moderate to Large Streamwise Oscillations

A large eddy simulation (LES) based numerical investigation is carried out for flow over two surging airfoils with moderate to large streamwise oscillations. In each case, the airfoil is subjected to a sinusoidal surging motion with streamwise oscillation at a fixed angle of attack. The amplitude of the sinusoidal oscillation is varied within a moderate range as well as in the high range. The amplitude of oscillation is characterized by the advance ratio, which is defined as the ratio of the maximum relative velocity in excess to the mean relative velocity (or mean free-stream velocity) to the mean relative velocity. The relative velocity is defined between the airfoil and ambient fluid. For the moderate range, NACA 0018 airfoil at a mean Reynolds number of 300,000 and 4◦ angle of attack is considered. Two advance ratios of 0.34 and 0.51 are considered to match with the experiments of Strangfeld et al. For the high range, NACA 0012 airfoil at a mean Reynolds number of 40,000 and 6◦ angle of attack is considered at three advance ratios of 0.8, 1.0 and 1.2 to match with the experiments of Granlund et al. Note that the highest advance ratio case involves the reversed flow condition, where in a part of the surging cycle the relative flow becomes negative or is from the geometric trailing end of the airfoil to the leading end. Lift force is compared between the experiments and simulations. Overall a good agreement is obtained for the lift force in all cases. Additionally, for all cases flowfields from simulations are examined at different phases of the surge cycle. For the moderate advance ratio cases, a similar flow pattern is observed between the two advance ratios and no distinct vortex is shed from the airfoil. On the other hand, in all three high advance ratio cases a distinct vortex is shed near the (geometric) leading edge on the suction or upper side. This prominent leading-edge vortex is shed as the minimum velocity is reached in the surging cycle and advects downstream (in the horizontal direction) by roughly the mean free-stream velocity. The relative position of the shed vortex (with respect to the airfoil) varies significantly between the three high advance ratio cases; it crosses the leading edge before sweeping over the airfoil in the case with the highest advance ratio of 1.2 (i.e., in the case with the reverse flow regime).