Bulletin of the American Physical Society
76th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 19–21, 2023; Washington, DC
Session R36: Multiphase Flows: Computational Methods IV 
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Chair: Ali Mani, Stanford University Room: 202B 
Monday, November 20, 2023 1:50PM  2:03PM 
R36.00001: Time Accurate Solutions of the FokkerPlanck Equation in Simple Shear Dogukan T Karahan, Devesh Ranjan, Cyrus K Aidun Existing efforts in simple shear flow have deemed solving the FokkerPlanck (FP) equation for fiber orientation kinetics using the centered differencing scheme (CDS) with an explicit time integration scheme (ETS) unstable, expensive, and inaccurate at high Peclet numbers (Pe). This work shows that conservative solutions may be obtained for the FP equation using this combination of schemes at a low computational cost. The FP equation is solved on unstructured cubedsphere grids using the finitevolume method. The ETS is a twostage secondorder RungeKutta scheme. A modified Jeffery equation (Ferec et al. Rheol. Acta (2014) 53:445–456) is employed to solve for the time evolution of the orientation vector. The approach accounts for variable shape factor and rotational diffusion coefficient. Because an ETS is employed, the coupling of the fiber orientation probability density function with the shape factor and rotational diffusion coefficient does not require linearization. The solver is rigorously tested to show that the CDS does not require stabilization in the Pe interval considered. Solutions are obtained for nondilute and semi concentrated suspensions up to Pe=100000. We will demonstrate that the solver aids the development of constitutive relations and closure models. 
Monday, November 20, 2023 2:03PM  2:16PM 
R36.00002: Effect of Feedback Force on SelfInduced Perturbation Flow at Finite Volume Fraction Jungyun Kim, Kai Liu, S. Balachandar In EulerLagrange simulation in an unbounded domain, selfinduced perturbation flow of an isolated particle is due to the application of a feedback force at the particle's location smoothed using a Gaussian function. This study aims to examine the impact of finite volume fraction and the feedback force on the selfinduced perturbation velocity at the particle's location. Several physical parameters influence the selfinduced perturbation velocity, including the Reynolds number based on the Gaussian width (Re_{σ}), the magnitude of feedback force components in the x and y directions (F_{x} and F_{y}) and averaged volume fraction (Φ_{0}). By investigating various combinations of these parameters, we explore the effect of increasing particle volume fraction on the selfinduced correction, as well as the effect of an angled feedback force. 
Monday, November 20, 2023 2:16PM  2:29PM 
R36.00003: Inferring multiscale bubble growth dynamics by deep neural operator learning Zhen Li, Chensen Lin, Martin R Maxey, George E Karniadakis Simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge in complex fluids. In this talk, we will present a composite deep neural network (a branch and a trunk network) for regressing nonlinear operators to predict multiscale bubble growth dynamics. We consider tiny bubbles of initial size from 100 nm to 10 μm modelled by the Rayleigh–Plesset equation in the deterministic continuum regime above 1 μm and the manybody dissipative particle dynamics method for bubbles below 1 μm in the stochastic microscale regime. We simulate the multirate bubble growth dynamics caused by randomly timevarying liquid pressures drawn from Gaussian random fields, and collect simulation data of bubble growth for both deterministic continuum regime and stochastic microscale regime. Subsequently, we train the composite deep neural network based on mixed data to learn the governing operator of multiscale bubble growth dynamics. Results show that the trained neural operator can capture correct physics and make accurate predictions of bubble growth onthefly (within a fraction of a second) across four orders of magnitude difference in spatial scales and two orders of magnitude in temporal scales. We will demonstrate that the deep neural operator framework is general for learning nonlinear operators for diverse physical problems, including learning transient mechanical response of composites subject to a dynamic loading. 
Monday, November 20, 2023 2:29PM  2:42PM 
R36.00004: Subgrid Scale Modeling of the Scalar Transport Equation in Multiphase Flows Jacob Maarek, Stephane Popinet, Stephane Zaleski In multiphase systems with heat or mass transfer at large Peclet numbers, thin boundary layers form at the interface. These are potentially orders of magnitude smaller than the smallest hydrodynamic length scales, making a true DNS of the system infeasible. We present a subgridscale (SGS) model for the scalar transport equation which corrects transport for advective and diffusive fluxes near the interface from a DNS for the flow. This allows us to predict heat and mass transfer correctly even if the boundary layer is fully contained in a single cell layer around the interface. We demonstrate how a shallow neural network can be used to approximate a boundary layer profile, allowing the user to quickly develop a model for the boundary layer and integrate it into the overall framework. The modeling framework is integrated into an existing opensource multiphase flow solver with adaptive mesh resolution which computes the velocity field used in the scalar transport equation. We apply our SGS framework to a variety of problems including bubble dissolution, nucleate boiling, and liquidliquid transfer in a gasblown ladle. The method allows us to recover known scaling regimes with very coarse grids compared to what would be required with traditional methods. 
Monday, November 20, 2023 2:42PM  2:55PM 
R36.00005: A statistical framework using LES to assess the effect of internal heating and natural convection on airborne transmission. Rupal Patel, Kalivelampatti Arumugam Krishnaprasad, Jorge Salinas, Nadim Zgheib, S Balachandar In confined spaces with standard ventilation, the natural convection resulting from heat sources, such as a human adult, a heated wall, or electronic equipment, may have a substantial impact on the airflow. The present work builds on a recently proposed statistical framework using highfidelity largeeddy simulations and particle overloading. More specifically, we investigate the effect of heating on the mixing of contaminant concentration. 
Monday, November 20, 2023 2:55PM  3:08PM 
R36.00006: Abstract Withdrawn

Monday, November 20, 2023 3:08PM  3:21PM 
R36.00007: Artificial Neural Network Aided VaporLiquid Equilibrium Model for MultiComponent HighPressure Transcritical Flows with Phase Change Suo Yang, Navneeth Srinivasan, Hongyuan Zhang With ever increasing demand for high performance combustors, increasing the chamber pressure is one often sought after option. This leads to the working conditions to overlap with the transcritical/ supercritical regime of the reactants. The CFD modeling of transcritical flows with phase change is very challenging. Such modeling can be achieved by using the firstprincipled vaporliquid equilibrium (VLE) theory coupled with a realfluid equation of state (EOS). However, two major problems exist with VLE calculations  robustness and speed. In order to tackle the second problem, insitu adaptive tabulation (ISAT) has been used recently to provide significant computational speedups, but does not guarantee robustness. This work attempts to tackle both the issues by introducing a plugandplay Artificial Neural Network (ANN) aided VLE model to couple with CFD. Training is performed on Python and inference speeds are optimized using Open Neural Network Exchange (ONNX). The model is validated against the results generated by both direct VLE calculation as well as ISAT for a highpressure shockdroplet interaction case. The extendability of the model is also shown by lowpressure shockdroplet interaction cases, validated by experimental results. Finally, the computational speeds and parallel scaling achieved by the method are presented. 
Monday, November 20, 2023 3:21PM  3:34PM 
R36.00008: A Numerical Study of Coefficientfree Kinetic Evaporation Modeling in Liquid Hydrogen Ayaaz Yasin, Kishan S Bellur Numerical modeling of liquidvapor phase change has long presented a challenge due to the use of tuning coefficients. The commonly used HertzKnudsenSchrage equation requires accommodation coefficients as necessary inputs but reported values span three orders of magnitude even for common fluids such as water. Data for cryogenic fluids are severely limited. Computational modeling of evaporation in liquid Hydrogen is critical to the development of longterm cryostorage technologies for deepspace applications. Due to lack of data, the accommodation coefficient is commonly reduced to a nonphysical tuning parameter to achieve numerical stability. To alleviate this, we use a Transition State Theory based analytical description of the accommodation coefficients to develop a new coefficientfree computational approach to model evaporation. This approach is used inside a CFD setup in Ansys Fluent to model quasisteadystate evaporation, where a sharp interface is assumed. Mesh cells adjacent to the sharp interface are identified as the active region where UserDefined Functions are used to apply phase changerelated mass, heat, and momentum sources. The source terms are computed using local thermophysical quantities and applied along the entire interface, resulting in nonuniform evaporation. The model is validated by comparison to evaporation rates from recently published cryoneutron experiment datasets. The importance of drift velocity in the evaporation model is also studied. 
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