Fast Numerical simulations of 2D turbulence using a dynamic model for Subgrid Motions

We present numerical simulation of 2D turbulent flow using a new model for the subgrid scales which are computed using a dynamic equation linking the subgrid scales with the resolved velocity. This equation is not postulated, but derived from the constitutive equations under the assumption that the non-linear interactions of subgrid scales between themselves are equivalent to a turbulent viscosity.The performances of our model are compared with Direct Numerical Simulations of decaying and forced turbulence. For a same resolution, numerical simulations using our model allow for a significant reduction of the computational time (of the order of 100 in the case we consider), and allow the achievement of significantly larger Reynolds number than the direct method.Comment: 35 pages, 9 figure

The streamwise turbulence intensity in the intermediate layer of turbulent pipe flow

The spectral model of Perry et al. (J. Fluid Mech., vol. 165, 1986, pp. 163–199) predicts that the integral length scale varies very slowly with distance to the wall in the intermediate layer. The only way for the integral length scale’s variation to be more realistic while keeping with the Townsend–Perry attached eddy spectrum is to add a new wavenumber range to the model at wavenumbers smaller than that spectrum. This necessary addition can also account for the high-Reynolds-number outer peak of the turbulent kinetic energy in the intermediate layer. An analytic expression is obtained for this outer peak in agreement with extremely high-Reynolds-number data by Hultmark et al. (Phys. Rev. Lett., vol. 108, 2012, 094501; J. Fluid Mech., vol. 728, 2013, pp. 376–395). Townsend’s (The Structure of Turbulent Shear Flows, 1976, Cambridge University Press) production–dissipation balance and the finding of Dallas et al. (Phys. Rev. E, vol. 80, 2009, 046306) that, in the intermediate layer, the eddy turnover time scales with skin friction velocity and distance to the wall implies that the logarithmic derivative of the mean flow has an outer peak at the same location as the turbulent kinetic energy. This is seen in the data of Hultmark et al. (Phys. Rev. Lett., vol. 108, 2012, 094501; J. Fluid Mech., vol. 728, 2013, pp. 376–395). The same approach also predicts that the logarithmic derivative of the mean flow has a logarithmic decay at distances to the wall larger than the position of the outer peak. This qualitative prediction is also supported by the aforementioned data

Direct Numerical Simulation of a separated channel flow with a smooth profile

A direct numerical simulation (DNS) of a channel flow with one curved surface was performed at moderate Reynolds number (Re_tau = 395 at the inlet). The adverse pressure gradient was obtained by a wall curvature through a mathematical mapping from physical coordinates to Cartesian ones. The code, using spectral spanwise and normal discretization, combines the advantage of a good accuracy with a fast integration procedure compared to standard numerical procedures for complex geometries. The turbulent flow slightly separates on the profile at the lower curved wall and is at the onset of separation at the opposite flat wall. The thin separation bubble is characterized with a reversal flow fraction. Intense vortices are generated near the separation line on the lower wall but also at the upper wall. Turbulent normal stresses and kinetic energy budget are investigated along the channel.Comment: 23 pages, submitted to Journal of Turbulenc

A model for rapid stochastic distortions of small-scale turbulence

We present a model describing the evolution of the small-scale Navier–Stokes turbulence due to its stochastic distortion by much larger turbulent scales. This study is motivated by numerical findings (Laval et al. Phys. Fluids vol. 13, 2001, p. 1995) that such interactions of separated scales play an important role in turbulence intermittency. We introduce a description of turbulence in terms of the moments of $k$-space quantities using a method previously developed for the kinematic dynamo problem (Nazarenko et al. Phys. Rev. E vol. 68, 2003, 0266311). Working with the $k$-space moments allows us to introduce new useful measures of intermittency such as the mean polarization and the spectral flatness. Our study of the small-scale two-dimensional turbulence shows that the Fourier moments take their Gaussian values in the energy cascade range whereas the enstrophy cascade is intermittent. In three dimensions, we show that the statistics of turbulence wavepackets deviates from Gaussianity toward dominance of the plane polarizations. Such turbulence is formed by ellipsoids in the $k$-space centred at its origin and having one large, one neutral and one small axis with the velocity field pointing parallel to the smallest axis

Information extraction of cybersecurity concepts: An LSTM approach

Extracting cybersecurity entities and the relationships between them from online textual resources such as articles, bulletins, and blogs and converting these resources into more structured and formal representations has important applications in cybersecurity research and is valuable for professional practitioners. Previous works to accomplish this task were mainly based on utilizing feature-based models. Feature-based models are time-consuming and need labor-intensive feature engineering to describe the properties of entities, domain knowledge, entity context, and linguistic characteristics. Therefore, to alleviate the need for feature engineering, we propose the usage of neural network models, specifically the long short-term memory (LSTM) models to accomplish the tasks of Named Entity Recognition (NER) and Relation Extraction (RE).We evaluated the proposed models on two tasks. The first task is performing NER and evaluating the results against the state-of-the-art Conditional Random Fields (CRFs) method. The second task is performing RE using three LSTM models and comparing their results to assess which model is more suitable for the domain of cybersecurity. The proposed models achieved competitive performance with less feature-engineering work. We demonstrate that exploiting neural network models in cybersecurity text mining is effective and practical. - 2019 by the authors.This publication was made possible by the support of Qatar University and DISP laboratory (Lumi?re University Lyon 2, France).Scopu

A comprehensive approach to analyze discrepancies between land surface models and in-situ measurements: a case study over the US and Illinois with SECHIBA forced by NLDAS

The purpose of this study is to test the ability of the Land Surface Model SECHIBA to simulate water budget and particularly soil moisture at two different scales: regional and local. The model is forced by NLDAS data set at 1/8th degree resolution over the 1997–1999 period. SECHIBA gives satisfying results in terms of evapotranspiration and runoff over the US compared with four other land surface models, all forced by NLDAS data set for a common time period. The simulated soil moisture is compared to in-situ data from the Global Soil Moisture Database across Illinois by computing a soil wetness index. A comprehensive approach is performed to test the ability of SECHIBA to simulate soil moisture with a gradual change of the vegetation parameters closely related to the experimental conditions. With default values of vegetation parameters, the model overestimates soil moisture, particularly during summer. Sensitivity tests of the model to the change of vegetation parameters show that the roots extraction parameter has the largest impact on soil moisture, other parameters such as LAI, height or soil resistance having a minor impact. Moreover, a new evapotranspiration computation including bare soil evaporation under vegetation has been introduced into the model. The results point out an improvement of the soil moisture simulation when this effect is taken into account. Finally, soil moisture sensitivity to precipitation variation is addressed and it is shown that soil moisture observations can be rather different, depending on the method of measuring field capacity. When the observed field capacity is deducted from the observed volumetric water profiles, simulated soil wetness index is closer to the observations