Bulletin of the American Physical Society
74th Annual Meeting of the APS Division of Fluid Dynamics
Volume 66, Number 17
Sunday–Tuesday, November 21–23, 2021; Phoenix Convention Center, Phoenix, Arizona
Session Q24: Computational Fluid Dynamics: General II 
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Chair: Daniel Banuti, University of New Mexico Room: North 224 B 
Tuesday, November 23, 2021 8:00AM  8:13AM 
Q24.00001: Effect of the GaussLegendre node distribution on wall turbulence in Direct Numerical Simulations of periodic channel flows using a Discontinuous Galerkin method flow solver Marc Bolinches, Todd A Oliver, Karl Shulz

Tuesday, November 23, 2021 8:13AM  8:26AM 
Q24.00002: An RBFbased finite difference discretization of the NavierStokes equations: error analysis and applications Tianyi Chu, Oliver T Scdmidt Radial basis functionfinite differences (RBFFD) are used to solve the incompressible NavierStokes equations on scattered nodes. We present a semiimplicit fractionalstep method that uses a staggered grid arrangement. The RBFQR method devised by Fornberg&Piret (2008) is used to obtain the RBFFD weights for the spatial derivatives. We propose a rigorous error analysis strategy to identify optimal combinations of the shape parameter, Ɛ, and the stencil size, n. A modified wavenumber analysis shows that the accuracy of the RBF differentiation matrices based on the optimal parameters is comparable to 4thorder Padétype finite differences for both first and second derivatives. The internal flow in a liddriven cavity and a transient cylinder wake are studied as examples. The former uses quasiuniform scattered nodes, while the latter refines the mesh near the cylinder and on the wake centerline. We demonstrate that stable solutions are obtained without the need for hyperviscosity. 
Tuesday, November 23, 2021 8:26AM  8:39AM 
Q24.00003: Modeling Natural Ventilation in Refugee Healthcare Shelters John Hochschild, Catherine Gorle Ventilation is imperative in combating airborne viruses such as SARSCoV2. In refugee camp healthcare shelters without mechanical ventilation systems, natural ventilation can be used to lower transmission risk. With the goal of investigating the mitigation of airborne viral concentration while also evaluating thermal comfort, we performed computational fluid dynamics (CFD) simulations to model flow, temperature, and exhaled aerosol dispersion inside a refugee camp healthcare shelter. Lacking field measurements, we used a building thermal model to solve for the CFD boundary conditions. Three recommendations can be made from the results. First, for the climate conditions considered here, ventilation should be maximized at any time of day: in the day the amount of ventilation has little impact on thermal comfort, and at night maximizing ventilation both minimizes viral concentration and improves thermal comfort. Second, window openings should be aligned with the prevailing wind direction to maximize ventilation; if the wind is 60° offnormal, ventilation rates are on average 33% less than when the wind is normal to the windows. Third, infectious patients should be placed next to leeward windows if possible, so that exhaled viral particles are drawn outside and diluted. 
Tuesday, November 23, 2021 8:39AM  8:52AM 
Q24.00004: Datadriven RANS Model Augmentations using Learning and Inference assisted by Featurespace Engineering Vishal Srivastava, Karthik Duraisamy This work presents guiding principles and techniques to enable inference of robust and generalizable augmentations for RANS models from sparse experimental data in a modelconsistent manner such that predictive accuracy is improved across a wide range of physical configurations (geometries/boundary conditions) without any added spurious behavior. The importance of constructing bounded features (inputs to augmentation function) via physicsbased nondimensionalization in appropriate functional forms while maintaining a balance between parsimony in the set of features and a onetoone featurestoaugmentation mapping is underlined. Particular emphasis is laid on different techniques, implementations and tradeoffs associated with localized learning. The framework is tested by creating a datadriven bypass transition model which is trained on two flat plate cases and shows consistent improvements across different flat plate, turbine cascade and compressor cascade cases with varying freestream turbulence intensities, Reynolds numbers and pressure gradients. 
Tuesday, November 23, 2021 8:52AM  9:05AM 
Q24.00005: Parallel solution of Partial Differential Equations on Binarized Octrees shamsulhaq basir, Jaber J Hasbestan, Inanc Senocak Significant savings in computer memory and computational turnaround time can be realized through adaptive mesh refinement (AMR) in fluid dynamics simulations. Treebased implementations of the AMR technique offer many advantages, but their computer implementation can be quite involved. A binarized octree is a recent pointerless implementation of an octree that relies solely on bitwise representations of the elements of an octree where a redblack tree is then used to insert and remove elements to the octree with a guaranteed worstcase performance of O(log N). The strict adherence to the bitwise representation of an octree also enables deep levels of mesh adaptations as there is no need to convert the bitwise representation to an integer. Furthermore, neighborhood information need not be stored as it is inherent in the bitwise representation. Here, we extend a binarized octreebased AMR technique for the parallel solution of partial differential equations on distributed memory platforms. We develop nonblocking collective communication utilities for intraprocessor and interprocessor information exchange and discuss various alternatives to interpolations at the coarsefine interface. We solve the 3D Poisson equation to verify the accuracy of the overall method. 
Tuesday, November 23, 2021 9:05AM  9:18AM 
Q24.00006: An adaptive mesh refinement approach for high Reynolds number flows over immersed bodies Wei Hou, Ke Yu, Benedikt L Dorschner, Tim Colonius The Lattice Green's Function (LGF) has been fruitfully combined with the immersedboundary (IB) method for efficient, scalable simulation of incompressible, external flows. A significant source of computational savings is the snug domain enabled by only requiring grid cells in vortical flow regions and truncating them in the far wake. However, at high Reynolds numbers, the uniform grid required by the LGF is inefficient for resolving thin boundary layers. To alleviate this constraint, we develop an adaptive mesh refinement method compatible with the LGF and IB formulations. We perform accurate DNS of flows over immersed bodies of arbitrary shape and complexity at Reynolds numbers on the order of 10000 while reducing the number of computational cells by 99.5% compared to a nonadaptive method. The framework is also efficient for simulating the effects of gusts on immersed bodies, particularly when they are represented as unsteady potential flows which can be imposed in our method without resolving the potential flow region. We demonstrate both steady and gusting flows over airfoils and compare our results with companion experiments in the IIT unsteady wind tunnel. 
Tuesday, November 23, 2021 9:18AM  9:31AM 
Q24.00007: Datadriven approach to adaptive mesh refinement in PeleC Parvathi Madathil Kooloth, Bruce A Perry We study the viability of datadriven adaptive mesh refinement in PeleC, a fully compressible reacting flow solver that utilizes the AMReX library for structured mesh management. The current strategy for grid cell tagging for refinement employs ad hoc thresholding criteria on a select subset of flow variables and their gradients. We demonstrate that the neural network trained to classify cells based on a spatial discretization error threshold outperforms the existing heuristic tagging in PeleC. Various architectures including fully connected networks and convolutional neural networks are tested for efficacy and universality across regimes of a 3D turbulent CO_2 jet. Extensive testing is carried out to determine the optimal feature tensor to be input to the neural network by comparing localized flow features and global inputs such as 2D flow field slices and by employing feature importance studies. 
Tuesday, November 23, 2021 9:31AM  9:44AM 
Q24.00008: SecondMoment Closure Modelling of Particle Erosion in a Pipe Elbow Sebastian Wegt, Jan Hartmann, Louis Krueger, Jeanette Hussong, Suad Z Jakirlic The erosive surface degradation in pipes, representing e.g. the process frequently encountered in coolant systems, is studied computationally. Different widelyused erosion models are preselected and implemented into an open source computational fluid dynamic code aiming at investigating their influence and quantifying their capability for predicting particle erosion. Influence of the particlewall interaction is discussed, as well as the necessity to account for the secondary particle impact. The background turbulence model adopted is the nearwall secondmoment closure model accounting for both Reynoldsstress and stressdissipation anisotropies (Jakirlic, Maduta, 2015, IJHFF 51 and Wegt, PhD, 2021). Among a limited number of available reference databases for particle erosion process, the high Reynolds number (Re=538.000) experimental study of Solnordal et al. (2015, Wear 36337), investigating the 90degree pipe elbow configuration is used as a reference for the present study. Both the first ovalshaped degradation footprint resulting from the first particle impact and the triangleshaped one representing the outcome of the second particle impact have been returned in close agreement with the experimental findings. This relates also to their overlapping region. 
Tuesday, November 23, 2021 9:44AM  9:57AM 
Q24.00009: Piecewise Linear Dimension Reduction as a Regularization Strategy in Data Assimilation for RANS Simulations Pasha Piroozmand, Oliver Brenner, Patrick Jenny Data Assimilation can reduce the modelform errors of RANS simulations. A spatially distributed corrective parameter field can be introduced to the model, whose optimal values can be efficiently found by an adjoint method and a gradientbased optimization. For assimilation of experimental data, which in most cases are sparsely distributed or are based on a lowresolution grid, the inverse problem will be severely illposed. A regularization strategy is needed to reduce the number of local minima associated with unphysical solutions. Common regularization methods such as Tikhonov and Sobolev gradient are capable of reproducing smooth and physically reasonable internal velocity fields, however, if measurements are located close to walls and an accurate wall shear stress profile is sought, they cannot prevent overfitting at these locations and will result in unphysical jagged profiles. We propose a new regularization strategy based on a projection of the parameter field which prevents overfitting by constraining the parameter field into piecewise linear subdomains. We show that the method provides accurate velocity and wall shear stress profiles. In addition, it introduces no hyperparameters and also leads to faster minimization convergence. 
Tuesday, November 23, 2021 9:57AM  10:10AM 
Q24.00010: Numerical solution of the threedimensional incompressible Euler equations using the Characteristic Mapping Method Xi Yuan Yin, JeanChristophe Nave, Kai Schneider We present an efficient semiLagrangian Characteristic Mapping (CM) method for solving the threedimensional incompressible Euler equations. This method evolves advected quantities by discretizing the flow map associated with the velocity field. Using the properties of the Lie group of volume preserving diffeomorphisms SDiff, longtime deformations are computed from a composition of shorttime submaps which can be accurately evolved on coarse grids. This method is a fundamental extension to the CM method for twodimensional incompressible Euler equations. We take a geometric approach in the 3D case where the vorticity is not a scalar advected quantity, but can be computed as a differential 2form through the pullback of the initial condition by the characteristic map. This formulation is based on the Kelvin circulation theorem and gives pointwise a Lagrangian description of the vorticity field. We demonstrate through numerical experiments the validity of the method and show that energy is not dissipated through artificial viscosity and small scales of the solution are preserved. We provide error estimates and numerical convergence tests showing that the method is globally thirdorder accurate. 
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