Flow visualization using momentum and energy transport tubes and applications to turbulent flow in wind farms

As a generalization of the mass-flux based classical stream-tube, the concept of momentum and energy transport tubes is discussed as a flow visualization tool. These transport tubes have the property, respectively, that no fluxes of momentum or energy exist over their respective tube mantles. As an example application using data from large-eddy simulation, such tubes are visualized for the mean-flow structure of turbulent flow in large wind farms, in fully developed wind-turbine-array boundary layers. The three-dimensional organization of energy transport tubes changes considerably when turbine spacings are varied, enabling the visualization of the path taken by the kinetic energy flux that is ultimately available at any given turbine within the array.Comment: Accepted for publication in Journal of Fluid Mechanic

On the decay of dispersive motions in the outer region of rough-wall boundary layers

In rough-wall boundary layers, wall-parallel non-homogeneous mean-flow solutions exist that lead to so-called dispersive velocity components and dispersive stresses. They play a significant role in the mean-flow momentum balance near the wall, but typically disappear in the outer layer. A theoretical framework is presented to study the decay of dispersive motions in the outer layer. To this end, the problem is formulated in Fourier space, and a set of governing ordinary differential equations per mode in wavenumber space is derived by linearizing the Reynolds-averaged Navier-Stokes equations around a constant background velocity. With further simplifications, analytically tractable solutions are found consisting of linear combinations of $\exp(-kz)$ and $\exp(-Kz)$, with $z$ the wall distance, $k$ the magnitude of the horizontal wavevector $\mathbf{k}$, and where $K(\mathbf{k},\textit{Re})$ is a function of $\mathbf{k}$ and the Reynolds number $\textit{Re}$. Moreover, for $k\rightarrow \infty$ or $k_1\rightarrow 0$, $K\rightarrow k$ is found, in which case solutions consist of a linear combination of $\exp(-kz)$ and $z\exp(-kz)$, and are Reynolds number independent. These analytical relations are verified in the limit of $k_1=0$ using the rough boundary layer experiments by Vanderwel and Ganapathisubramani (J. Fluid Mech. 774, R2, 2015) and are in good agreement for $\ell_k/\delta \leq 0.5$, with $\delta$ the boundary-layer thickness and $\ell_k = 2\pi/k$

A new wake-merging method for wind-farm power prediction in presence of heterogeneous background velocity fields

Many wind farms are placed near coastal regions or in proximity of orographic obstacles. The meso-scale gradients that develop in these zones make wind farms operating in velocity fields that are rarely uniform. However, all existing wake-merging methods in engineering wind-farm wake models assume a homogeneous background velocity field in and around the farm, relying on a single wind-speed value usually measured several hundreds of meters upstream of the first row of turbines. In this study, we derive a new momentum-conserving wake-merging method capable of superimposing the waked flow on a heterogeneous background velocity field. We couple the proposed wake-merging method with four different wake models, i.e. the Gaussian, super-Gaussian, double-Gaussian and Ishihara model, and we test its performance against LES data, dual-Doppler radar measurements and SCADA data from the Horns Rev, London Array, and Westermost Rough farm. Next to this, as an additional point of reference, the standard Jensen model with quadratic superposition is also included. Results show that the new method performs similarly to linear superposition of velocity deficits in homogeneous conditions but it shows better performance when a spatially varying background velocity is used. The most accurate estimates are obtained when the wake-merging method is coupled with the double-Gaussian and Gaussian single-wake model. The Ishihara model also shows good agreements with observations. In contrast to this, the Jensen and super-Gaussian wake model underestimate the farm power output for all wind speeds, wind directions and wind farms considered in our analysis

Periodic Shadowing Sensitivity Analysis of Chaotic Systems

The sensitivity of long-time averages of a hyperbolic chaotic system to parameter perturbations can be determined using the shadowing direction, the uniformly-bounded-in-time solution of the sensitivity equations. Although its existence is formally guaranteed for certain systems, methods to determine it are hardly available. One practical approach is the Least-Squares Shadowing (LSS) algorithm (Q Wang, SIAM J Numer Anal 52, 156, 2014), whereby the shadowing direction is approximated by the solution of the sensitivity equations with the least square average norm. Here, we present an alternative, potentially simpler shadowing-based algorithm, termed periodic shadowing. The key idea is to obtain a bounded solution of the sensitivity equations by complementing it with periodic boundary conditions in time. We show that this is not only justifiable when the reference trajectory is itself periodic, but also possible and effective for chaotic trajectories. Our error analysis shows that periodic shadowing has the same convergence rates as LSS when the time span $T$ is increased: the sensitivity error first decays as $1/T$ and then, asymptotically as $1/\sqrt{T}$. We demonstrate the approach on the Lorenz equations, and also show that, as $T$ tends to infinity, periodic shadowing sensitivities converge to the same value obtained from long unstable periodic orbits (D Lasagna, SIAM J Appl Dyn Syst 17, 1, 2018) for which there is no shadowing error. Finally, finite-difference approximations of the sensitivity are also examined, and we show that subtle non-hyperbolicity features of the Lorenz system introduce a small, yet systematic, bias