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 P13: Convection and Buoyancy-driven Flows: Simulation |
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Chair: Roberto Verzicco, Twente Room: 304 |
Monday, November 25, 2019 5:16PM - 5:29PM |
P13.00001: Application of new unstructured geometry capability within FDS to simulation of outdoor smoke transport and deposition Marcos Vanella, Oriol Rios, Glenn Forney, Emanuele Gissi, Jason Floyd, Saverio La Mendola, Randall McDermott Over the years, the Fire Dynamics Simulator (FDS) has become one of the industry preferred tools for simulation of fire scenarios in design of indoor fire protection systems, forensic studies and wildland fires, among others. The flow solver of FDS evolves the Low Mach approximation equations for thermally driven buoyant flows by means of Large Eddy Simulation (LES), and standard discretization on block structured meshes. Recently, we have been developing the capability of FDS to simulate such flows over complex unstructured geometries via a cut-cell scheme and immersed boundary method. In this talk we will focus on the numerical scheme, and ongoing work on simulation of an outdoor smoke dispersion problem considering agglomeration and deposition in the kilometer scale CERN Meyrin campus. Details of the software implementation, simulation setup, and species transport and deposition results for a specific contaminant release scenario will be discussed. [Preview Abstract] |
Monday, November 25, 2019 5:29PM - 5:42PM |
P13.00002: Effects of Equations of State Selection in Numerical Simulations of Supercritical Carbon Dioxide Elizabeth Rasmussen, Michael Martin, Shashank Yellapantula, John Kramlich Supercritical carbon dioxide (sCO$_{\mathrm{2}})$ is employed in a growing range of applications including novel material synthesis and advanced energy systems. However, a lack of understanding of how the complex behavior of sCO$_{\mathrm{2\thinspace }}$near the critical point 304.25 K and 7.39 MPa affects the flow field, accompanied by numeric challenges of simulating under these conditions, limits the use of simulation as a predictive tool in these systems. Initial simulations using the high-fidelity Span-Wagner equation of state at a pressure of 8 MPa, fluid and wall temperatures ranging from 305 K to 390 K, and Reynolds numbers ranging from 0.1 to 35, show complex changes in flow and heat transfer. When simulations are repeated using the ideal gas law, Soave-Redlich-Kwong, and Peng-Robinson equations of state to the system, many of these effects are not fully captured. We compare the drastically different flow characteristics between non-ideal and ideal models as well as present on the computational cost of the varying degrees of accuracy. [Preview Abstract] |
Monday, November 25, 2019 5:42PM - 5:55PM |
P13.00003: Boussinesq approximation in rapidly rotating flows Jagmohan Singh, H. M. Blackburn, J. M. Lopez, A. J. Smits Rotating thermal convection (RTC) comprises of a thermal buoyant plume surrounded by a swirling flow. RTC is fundamental to many geophysical and engineering flows including tornados, firewhirls, dust devils and gas turbine combustors. Over the decades, RTC has been studied experimentally and numerically and it is shown that the axial buoyancy due to gravity in the presence of swirling creates a large updraft and aids to the formation of these columnar flows. In numerical simulations, the gravitational buoyancy is commonly modelled via the Boussinesq approximation which ignores the density variations in the momentum equation except in the terms multiplied by the gravity. However, a similar approach leads to additional buoyancy terms due to centrifugal forces, Coriolis forces and the inertia. The buoyancy due to centrifugal forces has recently started gaining attention but the other buoyancy terms are still overlooked and ignored. In this study, we systematically investigate the effect of different buoyancy terms in the Navier--Stokes equations via direct numerical simulations for flow inside the rotating container with an axial temperature gradient. Our results demonstrate that the buoyancy due to Coriolis forces and the inertia can change the flow behaviour significantly. [Preview Abstract] |
Monday, November 25, 2019 5:55PM - 6:08PM |
P13.00004: Boundary treatment for the compressible natural convection with discrete-unified gas-kinetic scheme Xin Wen, Lian-Ping Wang, Zhaoli Guo The buoyancy-driven natural convection in an enclosure has been studied by researchers for several decades, it plays an important role in both flow physics study and practical applications. When the temperature difference is large, the flow is beyond the Boussinesq limit and governed by full compressible Navier-Stokes equations. An aspect that has not been well studied in the kinetic method is the boundary treatment for compressible thermal flow when the temperature field and velocity field are strongly coupled. How to implement the no-slip condition, the isothermal wall temperature and the adiabatic boundary condition can be a challenge for the kinetic method. In this talk, we propose a systematic approach of deriving the ‘bounce-back’ boundary treatment for the straight boundary using the Chapman-Enskog expansion. The ‘bounce-back’ expression allows the boundary nodes to satisfy the compressible Navier-Stokes equations. For a curved boundary, we implement the immersed boundary method into the discrete-unified gas-kinetic scheme (DUGKS). By design, the IB forces only contribute the leading order of momentum and energy equation, the no-slip condition and isothermal wall can then be implemented for the curved boundary. [Preview Abstract] |
Monday, November 25, 2019 6:08PM - 6:21PM |
P13.00005: AFiD-MF: an efficient solver for three-dimensional multiphase flows Haoran Liu, Qi Wang, Kai Leong Chong, Roberto Verzicco, Detlef Lohse We propose an extension of our code AFiD (www.afid.eu) to simulate efficiently three-dimensional multiphase flows. In this approach, we implement the phase field model into AFiD in order to retain the massive solver for the incompressible Navier-Stokes equations. The performance of AFiD has been confirmed in many previous studies. To simulate the dynamics of multiphase flows, we rely on the phase field model to capture the fluid-fluid interface and deal with large density/viscosity ratios of the phases. The coupling of the phase field model with the AFiD solver is obtained by the volume fraction distribution of each phase and the surface tension force on the fluid-fluid interface. Our new approach, AFiD-MF, is validated by comparisons with data in the literature and verified through several numerical experiments, such as an oscillating droplet, the deformation of a drop in shear flow, the breakup of a rising bubble and the Rayleigh-Bénard flow with two immiscible phases. [Preview Abstract] |
Monday, November 25, 2019 6:21PM - 6:34PM |
P13.00006: Variational multiscale large eddy simulation with diffusive flux reconstruction for Rayleigh-B\'{e}nard convection David Sondak, John Shadid, Tom Smith, Sidafa Conde, Roger Pawlowski Large eddy simulation (LES) models for Rayleigh-B\'{e}nard convection are developed using the variational multiscale formulation (VMS). In the VMS formulation, a sum decomposition is used to split the fields into resolved and unresolved components. The unresolved components are modeled to be proportional to the residual of the governing equations. In this way, if the numerical solution exactly captures all scales of motion, the residual will be zero and the unresolved portion of the field vanishes. The result is a consistent numerical method and a dynamic LES model. When discretizing the resolved scales with linear finite elements, the diffusive terms in the residuals are zero and the true residual is not satisfied. In Rayleigh-B\'{e}nard convection, this unbalanced residual may negatively impact results due to the importance of boundary layer effects. In the current work, the diffusive terms are reconstructed and included in the stabilized residual. This reconstruction is shown to provide better numerical convergence to the correct solution. Moreover, coarse near-wall resolution can be partially offset by correctly reconstructing the residual. All of these effects have a bearing on the scaling of the heat transport with Rayleigh number. [Preview Abstract] |
Monday, November 25, 2019 6:34PM - 6:47PM |
P13.00007: A cross Criticality in the convection of Yield Stress fluids Francesca Pelusi, Mauro Sbragaglia, Andrea Scagliarini, Massimo Bernaschi We study numerically the Rayleigh-B ́enard instability of a two-dimensional multi-component system confined between two horizontal walls heated from below and cooled from above. We first carefully validate the numerical model in the “mixed” regime, by studying the transition from conduction to convection in a homogeneous Newtonian system. As a further upgrade of complexity, the system is prepared in the “de-mixed” regime, with many liquid droplets closely packed together and separated by thin interfaces. In such conditions, the system is a yield stress fluid, i.e. it exhibits reference stress (the “yield stress”) below which it reacts to external perturbations as a solid, and above which it flows with non-Newtonian rheology. The transition to convection is characterized as a function of the packing and the intensity of the initial perturbation. When the system exhibits convection, a crucial additional feature with respect to the Newtonian homogeneous system is the presence of plasticity at the droplets scale, which is expected to alter the heat transfer from the hot to cold walls. [Preview Abstract] |
Monday, November 25, 2019 6:47PM - 7:00PM |
P13.00008: Using machine learning to predict 1D steady-state temperature profiles from compressible mantle convection simulations Siddhant Agarwal, Nicola Tosi, Doris Breuer, Pan Kessel, Gregoire Montavon Thermal evolution simulations of planetary mantles in 2D and 3D are computationally intensive. A low-fidelity alternative is to use scaling laws based on boundary-layer theory to express Nusselt Number (Nu) as a function of Rayleigh Number (Ra). Such a Ra-Nu relation can be used to run `0D' parametrized evolution models by solving a simple energy balance equation. Yet scaling relations are available only for simple flows that cannot capture the full complexity of mantle dynamics. We propose leveraging Machine Learning to find a higher-dimensional mapping from five different parameters to the entire 1D temperature profile. The parameters are Ra, internal heating Ra, dissipation number and the maximum viscosity contrast between top and bottom due to temperature and pressure. We train a Neural Network (NN) to take these inputs and predict the resulting steady-state temperature profile. The training data comes from a subset of 20,000 compressible simulations on a 2D cylindrical grid. This results in predictions with an average error of 1.6{\%} on the test set. The NN can potentially be used to build a 1D evolution model by stacking several steady-state temperature profiles together, with each prediction serving as an input at the next time-step. [Preview Abstract] |
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