Bulletin of the American Physical Society
66th Annual Meeting of the APS Division of Fluid Dynamics
Volume 58, Number 18
Sunday–Tuesday, November 24–26, 2013; Pittsburgh, Pennsylvania
Session R8: General Fluid Dynamics IV: Theory II |
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Chair: John Dannenhoffer, Syracuse University Room: 330 |
Tuesday, November 26, 2013 1:05PM - 1:18PM |
R8.00001: Data Fusion for Fluid Dynamic Data Christopher Ruscher, John Dannenhoffer, Mark Glauser In recent years, fluid dynamic measurement tools and computational fluid dynamics (CFD) have greatly improved, leading to vast amounts of data being collected. Extracting the useful information from large data sets can be a challenging task when investigating data from a single source. However, most experiments use data from multiple sources such as particle image velocimetry (PIV), pressure sensors, acoustic measurements, and CFD to name a few. Knowing the strengths and weaknesses of each measurement technique, one can fuse the data together to improve the understanding of the problem being studied. Using data fusion, sensor fusion, and data integration techniques in combination with fluid dynamic analysis tools, one can extract information from large multi-source fluid dynamic data sets, which is not obtainable from any single data source alone. [Preview Abstract] |
Tuesday, November 26, 2013 1:18PM - 1:31PM |
R8.00002: Dynamics and Control of the 2-d Navier-Stokes Equations Nejib Smaoui, Mohamed Zribi The control problem of the dynamics of the two-dimensional (2-d) Navier-Stokes (N-S) equations with spatially periodic and temporally steady forcing is studied. First, we devise a dynamical system of several nonlinear differential equations by a truncation of the 2-d N-S equations. Then, we study the dynamics of the obtained Galerkin system by analyzing the system's attractors for different values of the Reynolds number, $R_e$. By applying the symmetry of the equation on one of the system's attractors, a symmetric limit trajectory that is part of the dynamics is obtained. Next, a control strategy to drive the dynamics from one attractor to another attractor for a given $R_e$ is designed. Finally, numerical simulations are undertaken to validate the theoretical developments. [Preview Abstract] |
Tuesday, November 26, 2013 1:31PM - 1:44PM |
R8.00003: K-Means Clustering for Data Visualization and Flow Interpretation: Inclined Jet in Crossflow Example Julia Ling, Julien Bodart, Filippo Coletti, John Eaton The k-means clustering algorithm is a versatile data processing technique that has not yet been extensively applied to fluids data sets. The clustering algorithm can operate in high dimensional spaces to extract structure from large data sets. In the context of fluid dynamics, k-means clustering can be used to cluster the output of experimental or computational results based on mean velocity gradients or other single-point statistics. This technique has been applied to three dimensional mean velocity fields for an inclined jet in crossflow that were acquired using MRI-based 3D velocimetry, a Reynolds Averaged Navier Stokes (RANS) simulation, and a Large Eddy Simulation (LES). In each case, the clusters were based on the mean velocity gradient tensor. The optimal number of clusters was determined using an external validation technique in which a linear regression was performed within each LES cluster to predict the Reynolds stresses based on the mean velocity gradient. These linear regressions were subsequently evaluated on a validation subset of the LES data, and it was shown that eight clusters gave the lowest validation error. These eight clusters were used to explore the differences in flow structure between experiment, LES, and RANS and to determine which characteristics were associated with higher error in the RANS simulation. It was shown that the RANS Reynolds stresses were least accurate in regions of high strain or high streamwise vorticity. [Preview Abstract] |
Tuesday, November 26, 2013 1:44PM - 1:57PM |
R8.00004: Covariant Formulation of Fluid Dynamics and Estakhr's Material Geodesic Equation, far down the Rabbit hole Ahmad Reza Estakhr ``When i meet God, I am going to ask him two questions, why relativity and why turbulence. A. Einstein'' You probably will not need to ask these questions of God, I've already answered both of them. $U^{\mu}=\gamma (c,u(\vec {r}, t))$ denotes four-velocity field. $J^{\mu}=\rho U^{\mu}$ denotes four-current mass density. Estakhr's Material-Geodesic equation is developed analogy of Navier Stokes equation and Einstein Geodesic equation. $\frac{DJ^{\mu}}{D\tau}=\frac{dJ^{\mu}}{D\tau}+\Gamma^{\mu}_{\alpha\beta}J^{\alpha}U^{\beta}=J_{\nu}\Omega^{\mu\nu}+\partial_{\nu}T^{\mu\nu}+\Gamma^{\mu}_{\alpha\beta}J^{\alpha}U^{\beta}$ Covariant formulation of fluid dynamics, describe the motion of fluid substances. The local existence and uniqueness theorem for geodesics states that geodesics on a smooth manifold with an affine connection exist, and are unique. EMG equation is also applicable in different branches of physics, it all depend on what you mean by 4-current density, if you mean 4-current electron number density then it is plasma physics, if you mean 4-current electron charge density then it is $\frac{DJ^{\mu}}{D\tau}=J_{\nu}F^{\mu\nu}+\partial_{\nu}T^{\mu\nu}+\Gamma^{\mu}_{\alpha\beta}J^{\alpha}U^{\beta}$ electromagnetism. [Preview Abstract] |
Tuesday, November 26, 2013 1:57PM - 2:10PM |
R8.00005: Spreading and atomization dynamics of ultrasonically excited droplets Ranganathan Kumar, Deepu P, Saptarshi Basu The dynamics of a sessile droplet under the combined influence of standing pressure wave and a constant substrate acceleration is investigated experimentally. The asymmetric acoustic force field results in radial spreading of the droplet. The spreading rate varies inversely with viscosity which is explained using an analytical model. In low viscosity droplets, towards the end of droplet spreading capillary waves grow to form ligaments of varying length and time scales, ultimately leading to droplet disintegration. Proper Orthogonal Decomposition of high speed images from the droplet spreading phase predicts the likelihood of atomization. The different regimes in the life of surface ligaments are identified. Viscous dissipation plays a crucial role in determining the initial ligament momentum and thus the frequency of ligament breakup. However in the current experimental conditions the growth of a typical ligament is governed by inertial and capillary forces and the influence of viscosity in the ligament growth phase is rather negligible. By including the effect of acoustic pressure, a characteristic timescale is deduced which collapses the ligament growth profiles for different fluids on a straight line. [Preview Abstract] |
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