Bulletin of the American Physical Society
77th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 24–26, 2024; Salt Lake City, Utah
Session T13: CFD: General III |
Hide Abstracts |
Chair: Siva Thangam, Stevens Institute of Technology Room: 155 C |
Monday, November 25, 2024 4:45PM - 4:58PM |
T13.00001: A viscous-layer compressibility correction for two-equation Reynolds-averaged Navier-Stokes models Xiaohan Hu, George P Huang, Paul Allen Durbin, Xiang Yang The baseline two-equation Reynolds-averaged Navier-Stokes (RANS) models include fluid density but lack calibration for compressible flows, making them inadequate for high Mach numbers. Various compressibility corrections have been proposed to integrate the van Driest transformation or the semi-local transformation, i.e., a given compressible law of the wall, into the RANS formulation. Prior work has focused mainly on the logarithmic layer, but upon evaluating these log-layer compressibility corrections, we find that they do not significantly improve skin friction estimates.To overcome this challenge, we develop viscous-layer compressibility corrections. We do that by altering the dissipation terms. These corrections conform the RANS model to the semi-local scaling, resulting in more accurate predictions of mean velocity and temperature in a posteriori tests. Although not the primary focus of this study, we also find that the baseline one-equation Spalart-Allmaras model, which was proposed before the concept of semi-local scaling, produces results consistent with the semi-local scaling in a posteriori tests without requiring any compressibility correction. |
Monday, November 25, 2024 4:58PM - 5:11PM |
T13.00002: Flow characterization past large Arrays of Oscillating 2D Airfoils Yicheng Chen, Leonardo Chamorro, Som Dutta Assessing the so-called two-dimensional turbulence is crucial for understanding large-scale atmospheric and oceanic motions and for modeling geostrophic turbulence. Here, we investigate the distinct flow patterns generated by arrays of oscillating two-dimensional airfoils arranged in staggered configurations, consisting of 5 to 39 oscillating structures within Reynolds numbers ranging from 200 to 2000. Simulations were conducted using a novel Nonconforming Schwarz-Spectral Element Method (Schwarz-SEM) with multiple overlapping meshes to address the challenges of conformal meshes in complex geometries. A custom automated mesh generation tool was developed to create the computational mesh. Analysis of turbulent kinetic energy, spectral features, and other high-order statistics provides insights into how the wake characteristics depend on the number and spatial arrangement of the vortex-generating airfoils. |
Monday, November 25, 2024 5:11PM - 5:24PM |
T13.00003: Revisiting the turbulent viscosity parameter Cμ in the k-ϵ model for wall-bounded flows Harshit Mishra, Karan Venayagamoorthy The k-∈ model is one of the most popular RANS models used by the industry. RANS models leverage the eddy viscosity model where the closure of Reynolds stress is obtained by expressing it as a product of turbulent viscosity and the mean shear. In the k-∈ model, a turbulent viscosity parameter Cμ is required in addition to the turbulent kinetic energy k and its dissipation rate ∈ to obtain turbulent viscosity. In the equilibrium region, where the production of turbulent kinetic energy is nearly equal to its dissipation rate, the value of is taken as 0.09, based on the stress-intensity ratio, obtained using experiments conducted in the late 1960s at very low Reynolds numbers. However, the latest DNS datasets of wall-bounded suggest that the accepted value of Cμ needs revision. In this work, a new value of Cμ is suggested. The new value shows better agreement with the existing DNS datasets and is recommended for modeling wall-bound flows using the k-∈ model. |
Monday, November 25, 2024 5:24PM - 5:37PM |
T13.00004: A simple three-component mixing problem for the evaluation of a new reaction rate model Brandon E Morgan, Kevin Ferguson A simple computational mixing problem is presented which can be utilized to assess the behavior of Reynolds-averaged reaction rate models in a problem with temporally varying mixedness. In this problem, three mixing components are homogeneously distributed but initially separated in a triply periodic domain. These components are initialized within a Taylor-Green-like velocity field, which creates a mixing history evolving from the so-called ``no-mix limit'' to a well-mixed state. Large-eddy simulation results from this problem in configurations involving both premixed and non-premixed reactants are then compared with zero-dimensional Reynolds-averaged Navier-Stokes results utilizing a new model for multicomponent reacting mixtures. The new model is shown to appropriately respect the no-mix limit and outperforms an earlier model [Morgan, Phys. Rev. E 105, 045104 (2022)], particularly at early times when components are near the no-mix limit. |
Monday, November 25, 2024 5:37PM - 5:50PM |
T13.00005: ABSTRACT WITHDRAWN
|
Monday, November 25, 2024 5:50PM - 6:03PM |
T13.00006: Controlling Rayleigh-B´enard Convection through Regression Neural Network Mohammad Ali Ali Boroumand, Gabriele Morra, Peter Mora Rayleigh-Bénard thermal turbulent convection depends primarily on two dimensionless numbers: the Rayleigh (Ra) number related to the vigor of convection, and the Prandtl (Pr) number, the ratio between viscous and thermal diffusivities. Other parameters are boundary and initial conditions [1]. Here we simulate the Rayleigh-Bénard convection by using the Lattice Boltzmann Method (LBM) [2] to create training and testing images for a Deep Neural Network (DNN) [3] using regression to simultaneously estimate Ra and Pr from a single snapshot of temperature and velocities of the convective field. Training and testing ranges for Pr and Ra are [1 − 128] and [105 − 109] respectively. We verify the ability of the network to simultaneously predict Ra and Pr both for values never seen by the network within the training range, as well as beyond the training range (Pr ∈ [0.35 − 362] and Ra ∈ [104 − 1010]). Results show that the distribution of the predicted values for Ra and Pr is centered around the correct value for Ra and Pr within the training range and that the estimation precision decays outside it. Ra predictions are more precise than Pr across the entire training range, while Pr predictions are systematically lower at high Pr, which can be explained based on the characteristics of the Thermal Navier-Stokes equations. Our analysis suggests a practical tool for monitoring and controlling industrial flow and for studying geophysical flows, including heat transport in the earth’s interiors, ocean, and atmosphere. |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2025 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700