Bulletin of the American Physical Society
71st Annual Meeting of the APS Division of Fluid Dynamics
Volume 63, Number 13
Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
Session F39: Turbulence: General I 
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Chair: Gregory Bewley, Cornell University Room: Georgia World Congress Center Ballroom 3/4 
Monday, November 19, 2018 8:00AM  8:13AM 
F39.00001: Evidence of physical turbulence cascade mechanism via reconnection cascade scenario Fazle Hussain, Jie Yao Direct numerical simulation of viscous antiparallel vortex tube reconnection is performed up to vortex Reynolds number Re(=Γ/ν) = 40 000. To suppress the previously observed symmetry breaking through KelvinHelmholtz instability triggered by numerical roundoff error, and also to explore reconnection phenomenon further, we impose the symmetric boundary condition. We report here, for the first time, the evidence of vortex reconnection cascade scenario first proposed by Melander and Hussain (1988, CTR Report), where the remaining threads continue to undergo successive reconnections. The secondary reconnections at Re=9000 lead to the creation of various smallscale coherent structures, including vortex rings and packets of hairpinlike vortices. As Re increases, higher (e.g. third) generation of reconnection is also observed, and the energy is more rapidly transferred to finer scales  forming a turbulent cloud consisting of tangle of slender vortices. In addition, we observe a 5/3 kinetic energy spectrum for large wavenumbers associated with the reconnected bridges and remaining threads. These observations clearly demonstrate and confirm our longstanding claim that vortex reconnection is an important physical mechanism of turbulent energy cascade, particularity at high Re’s. 
Monday, November 19, 2018 8:13AM  8:26AM 
F39.00002: Analysis of Lyapunov vectors of turbulent flows Malik Hassanaly, Venkatramanan Raman In the perspective of predicting the formation of instabilities in turbulent flows, it is meaningful to characterize the formation and the propagation of disturbances. To this end, the Lyapunov theory provides a useful description of a turbulent flow field. On one hand, the computation of the Lyapunov spectrum provides the rate of growth of disturbances in a flow field and thereby quantifies the dimensionality of the strange attractor of the system. This is given by the Lyapunov exponents (LE) that have been the focus of numerous recent studies. On the other hand, the Lyapunov vectors (LV) can also be computed, but have been more overlooked since interpreting it is less obvious. In this work, the LV of several turbulent flows (1D Kuramoto Sivashinsky equation, homogeneous isotropic turbulence and turbulent channel flows) are computed, and their spatial structure is analyzed. It is shown that the LV exhibit nontrivial features for the vector associated with the null LE. In particular the concept of turbulent Lyapunov crisis is introduced and related to different turbulent forcing types. 
Monday, November 19, 2018 8:26AM  8:39AM 
F39.00003: A Spectral View on highR_{λ }turbulence Christian Küchler, Eberhard Bodenschatz, Gregory P Bewley The shape of the turbulent energy spectrum at high Taylorscale Reynolds numbers (R_{λ}) has been investigated in numerical, laboratory, and field experiments. It is important for both the fundamental understanding of turbulence and for tests of LES and its numerous applications. The Variable Density Turbulence Tunnel (see Bodenschatz et al. (2014)) exploits the capabilities of a unique active grid to produce turbulent flows up to R_{λ} ~6000. We use NSTAPs provided by Princeton University (e.g. Kunkel et al. (2006), Bailey et. al. (2010), Fan et al. (2015)) to acquire twopoint statistics of high resolution. By combining the flexibility of the active grid and different densities we will present a relative energy spectrum almost free of probe biases. This purely experimental approach is accompanied by numerical simulations to quantify and eliminate such biases resulting in absolute energy spectra. We find that two features of the energy spectrum show variations with R_{λ}. We can quantify for the first time (to the best of our knowledge) in a classical wind tunnel experiment how the bottleneck effect gets weaker with increasing R_{λ}. We further identify a R_{λ}dependence of the intermittency corrections to K41predictions of the spectral slope. 
Monday, November 19, 2018 8:39AM  8:52AM 
F39.00004: A Rigorous Entropy Law for the Turbulent Cascade Andre Fuchs, Nico Reinke, Daniel Nickelsen, Matthias Waechter, Joachim Peinke One important question of turbulence theory is to get a profound understanding of turbulence as a cascade process, that can be understood as the evolution of turbulent structures on different spatial or temporal scales. There have been many works to achieve a better understanding but rigorous results, like the KáarmánHowarth equations or Kolmogorov's 4/5th law, are still rare. Although these laws are derived from the Navier Stokes equation, the experimental verification is not of high precision.

Monday, November 19, 2018 8:52AM  9:05AM 
F39.00005: Taming turbulence via spectral and real space nudging Patricio Clark Di Leoni, Andrea Mazzino, Luca Biferale The technique of spectral nudging is commonly used to incorporate empirical data into a simulation in order to control its evolution and reproduce a given dynamical benchmark. We show how to do this in fully developed three dimensional turbulence. We give physical arguments for the choice of optimal parameters, based on the Reynolds number and on the amount of control provided by the nudging. We show how the algorithm can be used to extract unknown information from the data. Furthermore, we extend the technique to real space nudging, where the penalty is now performed in volumes in configuration space. 
Monday, November 19, 2018 9:05AM  9:18AM 
F39.00006: Deep learning of turbulent velocity signals Alessandro Corbetta, Roberto Benzi, Vlado Menkovski, Federico Toschi We investigate the capability of a stateoftheart deep neural model at learning features of turbulent velocity signals. Deep neural network (DNN) models are at the center of the present machine learning revolution. The set of complex tasks in which they over perform human capabilities and best algorithmic solutions grows at an impressive rate and includes, but it is not limited to, image, video and language analysis, automated control, and even life science modeling. Besides, deep learning is receiving increasing attention in connection to a vast set of problems in physics where quantitatively accurate outcomes are expected. 
Monday, November 19, 2018 9:18AM  9:31AM 
F39.00007: Computer Aided Image Segmentation and Classification of the Reynolds Stress Anisotropy Tensor Naseem Ali, Nicholas Hamilton, Marc Calaf, Rau'l Bayoa'n Cal A combinatorial technique merging image segmentation via Kmeans clustering and colormap of the barycentric triangle to investigate the Reynolds stress anisotropy tensor is posed. The clustering aids in extracting the identical features from the spatial distribution of the anisotropy colormap images by minimizing the sum of squared error between cluster center and all data points, simultaneously minimizing the squared error over all clusters. Three data sets are used to investigate the applicability of the clustering technique including a convergingdiverging channel flow, a supersonic jet flow, and the flow in a large wind farm array under three different thermal stratification cases (unstable, neutral and stable). The clustering technique improves pattern visualization and allows identifying different complex region of the turbulent flow. Helping to better understand the internal structure of the turbulent flow for different cases. The clustering images of the anisotropy colormap allow extracting the characteristic turbulence states in large three dimensional domains with clarity, revealing the natural grouping of the anisotropy stress tensor and illustrating the form and behavior of the turbulence in a particular region. 
Monday, November 19, 2018 9:31AM  9:44AM 
F39.00008: Filtering techniques for statistically under sampled turbulent data sets Austin Davis, John James Charonko, Katherine P Prestridge Extracting averaged values from low sample size turbulent data sets can be challenging due to the natural variation in the variables caused by the turbulent flows. Filtering techniques (Leonard 1974, Germano 1992) can be applied to these data sets to better match the statistics found from the larger sample size data. The experimental data from the turbulent mixing tunnel (TMT) (Charonko, Prestridge 2017) provides an excellent data set for testing these techniques as the sample size is large, the samples are high resolution, and variable density effects are tested. Containing 10,000 samples per location, a subset of the TMT data is chosen to represent a low sample size data set. Filtering techniques are then applied to the sample set and the statistics are compared to that of the full data set. These techniques have application to validation of simulations in which the domain has a very large number of spatial grid points, limiting the amount of time resolved data. 
Monday, November 19, 2018 9:44AM  9:57AM 
F39.00009: A NonLocal Characterization of Coherent Regions in Turbulence: Disentangling Turbulence Eddies Luis Manuel Portela, Siddhartha Mukherjee, Merlijn Mascini The description of coherent motion in turbulent flows and the identification of eddies have been a topic of long standing debate. There exist numerous criteria based upon the velocity gradient, which can have their benefits, depending on the application, but suffer from a common shortcoming: they are all pointcriteria, hence cannot account for the nonlocal structure of a coherent region, which we intuitively call an 'eddy'. We present a twostep method to overcome this. First, we introduce an instantaneous measure for spatial coherence, based upon a generalization of the twopoint correlation: at each point in the velocity field, a correlationvector is determined, by calculating a correlationlength along three orthogonal directions, using an invariant of the correlationtensor. The isomagnitude contours of this correlationvector field represent a kernel of longer range coherent motions. Next, using BiotSavart's law, the velocity field associated with isolated kernels can be reconstructed, which helps to isolate coherent motion in space, as well as scale. This procedure helps to visualize the dynamics of the eddies, possibly also giving a view into the ubiquitous eddybreakup mechanism characteristic of the turbulence cascade, as summed up in Richardson's famous verse. 
Monday, November 19, 2018 9:57AM  10:10AM 
F39.00010: Energy Flow of Homogeneous Isotropic Turbulence at Subscale Eddies Mohamad Ibrahim Cheikh, James Chen Understanding the flow of energy at the smallest eddy scale can provide insights into energy cascade. Much work has been done on investigating the energy flow at the level of the smallest eddies using different NavierStokes (NS) based techniques. However, resorting to NS based methods to understand the impact of the smallest eddies on the energy flow is tedious due to the strong coupling between the translational and rotational motion of the eddies. Therefore, a higher order morphing continuum theory (MCT) is employed. MCT treats the continuum not as a set of volumeless points as NS, but as a set of smallscale structures that possess their own rotational motion and inertia. The current study derives the MCT conservation laws under a translational symmetry. The resulting conservation laws reveal the existence of new routes for energy transfer among translation, rotation and internal energy. It broadens the view on the energy cascade phenomena in homogeneous isotropic turbulence as an example. The energy analysis shows that the energy flux (positive or negative) is highly dependent on the rotation of the smallscale structure as well as their translational motion. 
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