Bulletin of the American Physical Society
75th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 20–22, 2022; Indiana Convention Center, Indianapolis, Indiana.
Session L11: CFD: General I |
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Chair: Faisal Amlani, Université Paris-Saclay Room: 138 |
Monday, November 21, 2022 8:00AM - 8:13AM |
L11.00001: A high-order spectral solver for dye evolution and particle residence time calculations Faisal Amlani, Heng Wei, Niema M Pahlevan Numerical simulation of dyes and non-discrete particle residence time (PRT) calculations, governed by advection-diffusion partial differential equations (PDEs), are useful a posteriori analysis approaches for both experimental and computational fluid dynamics studies. This contribution presents a new Fourier continuation (FC-)based, high-order numerical methodology for solving such systems without numerical diffusion (or "pollution") errors. The efficacy of FC has already been demonstrated for many strictly hyperbolic PDEs, namely heterogeneous elastic and fluid-structure problems. Velocity data for the corresponding governing equations are produced by an in-house fluid-structure simulator based on a lattice Boltzmann method coupled to a Lagrangian-coordinate elasticity solver. The proposed approach can also be applied to velocity data collected through experimental techniques such as from particle image velocimetry. Convergence and error analyses are performed with both analytical and manufactured solutions. Realistic case studies in fluid dynamics and hemodynamics are presented to demonstrate the applicability of the proposed FC-based dye simulator. |
Monday, November 21, 2022 8:13AM - 8:26AM |
L11.00002: Image and Video Compression of Fluid Flow Data Vishal Anantharaman, Kai Fukami, Kunihiko Taira Acquiring and analyzing high fidelity spatio-temporal data is crucial to many problems in fluid mechanics and this results in large data storage requirements. Thus far, modal analyses, sub-sampling and local re-simulation, autoencoders, and generative networks have been explored for data compression with some success but generally remain problem-specific. With explosive demand in the multimedia industry for data storage and sharing, advancements in image and video compression have accelerated with many algorithms producing negligible quality losses at substantial compression ratios. We explore the efficacy of spatial compression techniques such as JPEG and JPEG-2000, and spatio-temporal techniques such as H.264, H.265, and AV1 on various fluid flow data. These multimedia compression techniques are compared for examples of laminar cylinder wake flow, two-dimensional decaying homogenous isotropic turbulence, and three-dimensional turbulent channel flow. We observe that compressed flow fields with such techniques hold physical validity in terms of temporal correlations and kinetic energy distribution. The flexibility and scalability of these multimedia compression algorithms suggest an expansive potential within this field. |
Monday, November 21, 2022 8:26AM - 8:39AM |
L11.00003: Effects of the urban canopy on natural ventilation using LES Nicholas G Bachand, Catherine Gorle Mechanical building cooling increasingly contributes to global energy use. Natural ventilation and cooling, where wind and buoyancy forces drive outside air through a building, can offer an efficient alternative solution. Understanding the potential for natural ventilation can inform urban planning for cooler cities, as well as building systems design. One challenge of quantifying natural ventilation potential (NVP) is capturing the significant influence of the urban canopy wind flow pattern. |
Monday, November 21, 2022 8:39AM - 8:52AM |
L11.00004: State-observer-based data assimilation for the correction of turbulent pressure in numerical simulations JiaCheng Hu, Antonio Martín-Alcántara, David E Rival Assimilation of experimental measurements with computational fluid dynamics (CFD) to improve simulation accuracy has steadily developed over the past years (Li et al., 2022, Zauner et al., 2022). However, recent efforts have focused solely on correcting the solenoidal component of the Reynolds stress tensor with data assimilation. Therefore, this work explores the assimilation of surface pressure measurements to rectify the dilatational part of the Reynolds stress tensor, also known as the turbulent pressure (Perot and Moin, 1996). The proposed state-observer-based data assimilation (SODA) expands upon the proportional-integral-derivative (PID) SODA developed by Neeteson and Rival (2020) to include additional correction terms in both the momentum and pressure equations to achieve turbulent pressure correction. The novel SODA is benchmarked against synthetic and real-world measurements with a focus on separated flow scenarios to demonstrate its ability to reconstruct mean fields with improved accuracy and convergence over conventional CFD methods. |
Monday, November 21, 2022 8:52AM - 9:05AM |
L11.00005: Investigation of Transition Within the Channels of a Gas-Cooled Laser Amplifier Head Using RANS Simulations Edward Lowell, Oliver T. Schmidt, Frantisek Batysta, Thomas Spinka In an effort to improve the thermal management of high-average power, high-intensity lasers, this study focuses on simulating the gas flow through multiple thin channels. In particular, the current approach for cooling such laser systems makes use of passing helium gas through an array of closely-spaced, thin vanes, where each vane contains a thin slab of gain medium. Since the role of turbulence is crucial to both the thermal management of the gain material and to the optical quality of the laser, it is imperative that the state of the flow within these channels is properly understood. In the absence of experimental data, the current work utilizes RANS turbulence and transition models to obtain flow solutions. Initial results indicate that the flow remains laminar further downstream than expected, and beyond typical critical Reynolds numbers. With this in mind, special emphasis is placed on studying flow features relating to relaminarization, where comparisons are made with classical transition metrics of boundary layers. Results of this study, in addition to the possibility of comparisons to experimental and LES data, give insight into the direction for optimal thermal management of similarly designed gas-cooled laser systems. |
Monday, November 21, 2022 9:05AM - 9:18AM |
L11.00006: Benchmark comparison of aerodynamic character between CFD and experimental data sets for an aeroshell in free stream descent Corey Zucker, Michael P Kinzel NASA is funding a space mission to explore Saturn’s moon Titan. Like Mars Ingenuity, it will be a space rotorcraft (Dragonfly). During the entry, descent, and landing (EDL) phase, Dragonfly will approach Titan’s surface and separate from the backshell capsule resulting in complex, interactional aerodynamics. This study will examine these interactions using benchmarked CFD. The benchmark studies analyze the aeroshell’s aerodynamic character over a broad range of angles of attack. The findings suggest that CFD predictions are reasonable and can provide insight prior to more costly experiments. |
Monday, November 21, 2022 9:18AM - 9:31AM |
L11.00007: Acceleration of turbulent combustion simulation through principal components transport and machine learning Anuj Kumar, Martin Rieth, Ope Owoyele, Jacqueline H Chen, Tarek Echekki The dimensionality reduction of the combustion thermochemical state space using principal component analysis (PCA) can yield a significant reduction in the variables of this space. In this study, we investigate the potential of accelerating direct numerical simulations (DNS) in turbulent combustion by the solution of transport equations for the principal components (PCs) of the combustion state space and machine learning to evaluate their chemical source terms and transport coefficients. A reduced set of transport equations for these PCs is used instead of the transport equations for the thermo-chemical scalers (species and energy) as in a traditional DNS. The data needed for the determination of the PCs and their chemical and transport terms is based on a lower-dimensional and smaller domain DNS data spanning the desired composition space. PCA is performed on this data and the desired number of PCs is determined. Moreover, this data is used to determine the chemical source terms and the PCs diffusion coefficients. These quantities are modeled in terms of the transported PCs using artificial neural networks (ANN). The formulation is implemented for a premixed methane-air flame in a three- dimensional slot- bunsen burner. Fluid flow and premixed combustion statistics of DNS based on the PC transport show an agreement with the statistics of DNS of the full thermo-chemical state. Moreover, the results show a two order reduction in the computational cost of the simulation, thus enabling the extension of the simulation to a larger computational domain with complex reaction mechanisms. |
Monday, November 21, 2022 9:31AM - 9:44AM |
L11.00008: Phase-field model for the Brownian motion of droplets Haodong Zhang, Fei Wang, Lorenz Ratke, Britta Nestler Brownian motion (BM) plays an important role in natural science for the stochastic |
Monday, November 21, 2022 9:44AM - 9:57AM |
L11.00009: Physics-Conforming Turbulent Flow Simulations Compression Approach Alberto Olmo Hernandez, Andrew Glaws, Ahmed Zamzam, Ryan King With the growing size of turbulent flow simulations, data compression approaches become an utmost importance to analyze, visualize, or restart the simulations. Recently, in-situ autoencoder-based compression approaches were proposed and shown to be effective in producing dimensionality-reduced representations of flow simulations. However, these approaches tend to focus solely on training the model based on sample quality losses while not taking advantage of the physical properties of turbulent flows. In this paper, we show that training autoencoders with additional physics-informed regularizations, e.g., incompressibility and preservation of enstrophy, improves a baseline model without such regularizations in three ways: i) upon inspection of the trained compression filters of the neural network, we identify changes in the convolutions due to the inclusion of the physics-informed terms ii) the compressions prove to be more physics-conforming to homogeneous isotropic turbulences of different Reynolds numbers given that these adhere to both the divergence free condition and preservation of enstrophy without trading off reconstruction quality, and iii) as a performance byproduct, training shows to converge 4 times faster than the baseline model. |
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