Bulletin of the American Physical Society
72nd Annual Meeting of the APS Division of Fluid Dynamics
Volume 64, Number 13
Saturday–Tuesday, November 23–26, 2019; Seattle, Washington
Session S02: Bubbles: Cavitation I |
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Chair: Tim Colonius, California Institute of Technology Room: 2B |
Tuesday, November 26, 2019 10:31AM - 10:44AM |
S02.00001: Vortex pair interaction and cavitation inception Aditya Madabhushi, Krishnan Mahesh Cavitation inception during vortex pair interaction is a common phenomenon observed in turbulent flows. Here, DNS is used to study the interaction between a counter rotating unequal strength vortex pair at Re = 200000. The entire evolution, beginning with the Crow instability during initial stages to non-linear dynamics at the later stages leading to inception, is studied. Velocity gradient invariants are used to characterize the topology and core dynamics. The factors that could potentially lead to inception in either of the vortices are discussed. A fully compressible Euler-Lagrangian model is being developed that would accurately predict the dynamics of the sub-grid bubbles during inception. The model will be discussed. [Preview Abstract] |
Tuesday, November 26, 2019 10:44AM - 10:57AM |
S02.00002: Investigation of cavitating flows using LES Filipe Brandao, Krishnan Mahesh LES and the homogeneous mixture approach are used to investigate cavitating flow over two geometries: a circular cylinder and a backstep. For the cylinder, different cavitation numbers are considered with different amounts of vapor and non-condensable gas freestream volume fraction. It is observed that the location of boundary-layer separation is largely affected by levels of void fraction in the freestream. Therefore, the upstream movement of the separation point as the cavitation number is reduced, as predicted in Arakeri (J. Fluid. Mech., (1975) 68: 779--799), can be obtained. A dynamic mode decomposition (DMD) algorithm developed in Anantharamu and Mahesh (J. Comput. Phys., (2019) 380: 355--377) is employed to study wake characteristics. DMD reveals that cavitation has a large effect on the primary K\'arm\'an vortex street transition. Differences in vortex stretching, dilatation and baroclinic torque between the cyclic ($\sigma = 1.0$) and transitional ($\sigma = 0.7$) regime are also studied. LES of the backstep shows good agreement for mean flow and turbulence intensities with experiments. Cavitation inception in the shear layer downstream of the step is studied and will be discussed. [Preview Abstract] |
Tuesday, November 26, 2019 10:57AM - 11:10AM |
S02.00003: A volume-of-fluid (VOF) methodology for the prediction of cavitation phenomena Ali Fakhreddine, Karim Alame, Krishnan Mahesh A sharp interface approach for modeling cavitation phenomena in incompressible viscous flows is presented. We utilize the incompressible Navier-Stokes equations with a modified Poisson equation. The modification to the Poisson equation accounts for phase change taking place at the interface between the vapor-liquid phases. We adopt a one-fluid formulation for the vapor-liquid two-phase flow and the interface is tracked using a volume-of-fluid (VOF) methodology. The model is validated with canonical test cases. The interaction of bubbles with a boundary layer is discussed. [Preview Abstract] |
Tuesday, November 26, 2019 11:10AM - 11:23AM |
S02.00004: Numerical simulations of a cavitating bubble with phase transition near an object Mauro Rodriguez, Tim Colonius Understanding the bubble dynamics in and near soft/hard matter is important for biomedical applications, particularly in the context of cavitation-induced damage. Applications include therapeutic, focused ultrasound tools to treat pathogenic tissues (e.g., soft cancerous tissues, hard kidney stones). During the therapy, a cloud of bubbles fractionates the surrounding tissue with the violent oscillations and collapse of water vapor/gas(es) bubbles. While not well understood, during bubble’s lifecycle (oscillations of growth and collapse) the mass transfer rates across the bubble wall interface plays a significant role on the bubble dynamics, and therefore, treatment efficacy. To investigate this phenomenon, an in-house, shock- and interface-capturing method is used to solve a 3D, 6-equation multiphase numerical model in a Eulerian framework that accounts for thermal and mass transfer across material interfaces. A canonical problem involving the growth and collapse of a bubble with gas and vapor in a liquid is considered. Comparisons between 1D Keller-Miksis-type and 3D spherical bubble simulations will be used to verify the approach. Cases involving a bubble with either gas and/or vapor collapsing near an object will also be presented. [Preview Abstract] |
Tuesday, November 26, 2019 11:23AM - 11:36AM |
S02.00005: Numerical investigations of cavitation-induced tissue damage Lauren Mancia, Mauro Rodriguez, Jonathan sukovich, Selda Buyukozturk, Christian Franck, Zhen Xu, Eric Johnsen Cavitation is known to damage tissue in blast injuries and ultrasound procedures. The mechanisms for cavitation-induced damage to soft matter are still poorly understood, and a basic modeling framework is needed to guide future experiments and to efficiently model bubble clouds. This presentation introduces a bubble dynamics model that has been validated using single--bubble radius vs. time data obtained from ultrasound and laser experiments. The model is then used to infer experimentally uncertain quantities such as nucleus size and thermodynamic conditions at the onset of bubble growth. Finally, potential cavitation damage mechanisms are introduced and used to propose a strain-based cavitation damage metric. [Preview Abstract] |
Tuesday, November 26, 2019 11:36AM - 11:49AM |
S02.00006: Computational modeling of pseudo-cavitation phenomenon in diesel injector nozzles Rohit Mishra, Dorrin Jarrahbashi Cavitation in fuel injectors occurs in the nozzle region where local pressure drops below the fuel saturation pressure. The bubbles formed from cavitation collapse downstream of the flow and potentially damage the nozzle wall; however, they promote turbulence by reducing the effective diameter of the nozzle and contributing to vortex formation. The discrepancies observed in the size of the cavitation zone between the experiment and current model predictions are attributed to pseudo-cavitation, which is, the formation of bubbles of non-condensable gases such as oxygen and nitrogen dissolved in the fuel. A new model has been developed that accounts for the formation of nitrogen bubbles separating from the fluid stream due to the changes in solubility driven by the pressure drop. The volume fraction in the new model has two source terms that account for the main cavitation and pseudo-cavitation. The gas phase is assumed to be a homogenous mixture of nitrogen and vapor. The proposed model improves the prediction of size and location of the bubbles formed in the domain and captures the effects of vortex formation. [Preview Abstract] |
Tuesday, November 26, 2019 11:49AM - 12:02PM |
S02.00007: Neural-network-augmented Gaussian moment method for the statistics of cavitating bubble populations Spencer Bryngelson, Alexis Charalampopoulos, Themistoklis Sapsis, Tim Colonius Phase-averaged bubbly flow models are forced by moments of the bubble population dynamics. Computation of such moments requires a representation of the bubble population statistics. This has traditionally been accomplished via a classes method that evolves bins of discrete bubble sizes and computes the required moments via quadrature. We instead propose a method based upon explicit evolution of low-order moments of the bubble population and Gaussian closures. This circumvents the additional expense associated with the evolution of classes in lieu of a numerical evaluation of the associated closure integrals, which is particularly advantageous when the bubble population distributions are broad. This approach is exact for linear bubble dynamics, though has larger errors for bubble populations undergoing increasingly strong nonlinear dynamics. This problem is associated with the generation of higher-order moments, which we treat via recurrent neural networks. They are trained with Monte Carlo surrogate-truth data and augment our evolution of the low-order moments and evaluation of higher-order moments. The neural networks markedly improve model predictions, even for out-of-sample testing data. [Preview Abstract] |
Tuesday, November 26, 2019 12:02PM - 12:15PM |
S02.00008: Cavitation Collapse Near Slot Geometries Elijah Andrews, Ivo Peters Vapor bubbles in water collapse towards a nearby solid boundary producing a jet that can clean, or damage, the boundary. It is useful to understand how different boundary geometries will affect the direction in which the jet is produced. The majority of research so far has focused on simple flat boundaries or limited cases with analytic solutions such as axisymmetric boundaries. We numerically and experimentally investigate how a slot in a flat boundary affects the jet direction of a single bubble. We use a boundary element model to predict how the jet direction depends on key parameters and show that the results collapse to a single curve when the parameters are normalized appropriately. We then experimentally verify the predicted dependencies using laser induced cavitation and compare the experimental results to the predicted dependencies. This research provides useful insights into how jet direction is affected by slot geometries and demonstrates a method that can be used to investigate other complex geometries. [Preview Abstract] |
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