Computational Fluid Dynamics: Algorithms I
ORAL · H18 · ID: 22866
Presentations
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Local Extreme Learning Machines: A Neural Network Based Spectral Element-Like Method
ORAL
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Publication: S. Dong & Z. Li, "Local Extreme Learning Machines and Domain Decomposition for Solving Linear and Nonlinear Partial Differential Equations", arXiv:2012.02895
S. Dong & Z. Li, "A Modified Batch Intrinsic Plasticity Method for Pre-training the Random Coefficients of Extreme Learning Machines", Journal of Computational Physics, 110585, available online. DOI: https://doi.org/10.1016/j.jcp.2021.110585Presenters
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Suchuan Dong
Purdue University
Authors
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Suchuan Dong
Purdue University
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Zongwei Li
Purdue University
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Evolutional deep neural networks for accurate Navier-Stokes solutions and forecasts of turbulence
ORAL
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Publication: Du, Y., & Zaki, T. A. (2021). Evolutional deep neural network. arXiv preprint arXiv:2103.09959.
Presenters
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Yifan Du
Johns Hopkins University
Authors
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Yifan Du
Johns Hopkins University
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Tamer A Zaki
Johns Hopkins University
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A novel recursive unsupervised clustering MoE to represent flamelet tables
ORAL
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Presenters
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Rohit Mishra
Texas A&M University
Authors
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Rohit Mishra
Texas A&M University
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Sarvesh Mayilvahanan
Texas A&M University
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Dorrin Jaranbashi
Texas A&M University
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Grid Convergence Index (GCI) Analysis of the Computational Mesh Resolution for Two Numerical Simulation Schemes for the Liquid-Gas Interfacial Reconstruction/Advection in Micron and Submicron Scales
ORAL
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Publication: Numerical and Experimental Investigation of Gas Flow Field Variations in Gas-DynamicVirtual Nozzles
References of the abstract:
[1] W. Oberkampf and T. Trucano, "Verification and Validation in Computational Fluid Dynamics," 2002, doi: 10.1016/S0376-0421(02)00005-2.
[2] M. Herrmann, "A sub-grid surface dynamics model for sub-filter surface tension induced interface dynamics," Comput. Fluids, vol. 87, pp. 92–101, Oct. 2013, doi: 10.1016/j.compfluid.2013.02.008.
[3] S. S. Deshpande, L. Anumolu, and M. F. Trujillo, "Evaluating the performance of the two-phase flow solver interFoam," Comput. Sci. Discov., vol. 5, no. 1, p. 014016, Nov. 2012, doi: 10.1088/1749-4699/5/1/014016.
[4] J. Roenby, H. Bredmose, and H. Jasak, "A computational method for sharp interface advection," R. Soc. Open Sci., vol. 3, no. 11, p. 160405, Nov. 2016, doi: 10.1098/rsos.160405.
[5] H. Scheufler and J. Roenby, "Accurate and efficient surface reconstruction from volume fraction data on general meshes," J. Comput. Phys., vol. 383, pp. 1–23, Apr. 2019, doi: 10.1016/j.jcp.2019.01.009.Presenters
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Reza Nazari
Arizona State University
Authors
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Reza Nazari
Arizona State University
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Adil Ansari
Arizona State University
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Ronald J Adrian
Arizona State University
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Richard Kirian
Arizona State University
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Marcus Herrmann
Arizona State University
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Adaptive chemistry reduction using Deep Neural Networks and Global Pathway Selection
ORAL
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Presenters
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Aaron Nelson
Texas A&M University
Authors
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Rohit Mishra
Texas A&M University
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Aaron Nelson
Texas A&M University
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Dorrin Jaranbashi
Texas A&M University
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Robust, segregated time integration for direct numerical simulation of low-Mach, variable-density, turbulent flows
ORAL
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Publication: A pseudospectral method for direct numerical simulation of low-Mach, variable-density, turbulent flows -- in preparation
Presenters
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Bryan W Reuter
Sandia National Laboratories
Authors
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Bryan W Reuter
Sandia National Laboratories
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Todd A Oliver
University of Texas at Austin
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Robert D Moser
University of Texas at Austin, The University of Texas at Austin, UT Austin
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Temporally Adaptive Conservative Scheme for Unsteady Compressible Flow
ORAL
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Publication: Valérie Kulka and Patrick Jenny. Temporally Adaptive Conservative Scheme for Unsteady Compressible Flow. Will be submitted to JCOMP.
Presenters
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Patrick Jenny
ETH Zurich
Authors
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Patrick Jenny
ETH Zurich
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Valérie Kulka
ETH Zürich
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Predicting drag on rough surfaces by transfer learning of empirical correlations
ORAL
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Publication: Sangseung Lee, Jiasheng Yang, Pourya Forooghi, Alexander Stroh, and Shervin Bagheri. "Predicting drag on rough surfaces by transfer learning of empirical correlations." arXiv preprint arXiv:2106.05995 (2021).
Presenters
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Sangseung Lee
KTH Royal Institute of Technology
Authors
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Sangseung Lee
KTH Royal Institute of Technology
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Jiasheng Yang
Karlsruhe Institute of Technology
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Pourya Forooghi
Aarhus University
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Alexander Stroh
Karlsruhe Institute of Technology
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Shervin Bagheri
KTH Royal Institute of Technology
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A Novel Adaptive Spectral Method for Fluid Flow Simulations in Non-periodic Domains
ORAL
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Presenters
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Narsimha R Rapaka
King Abdullah Univ of Sci & Tech (KAUST)
Authors
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Narsimha R Rapaka
King Abdullah Univ of Sci & Tech (KAUST)
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Ravi Samtaney
King Abdullah Univ of Sci & Tech (KAUST), Mechanical Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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A Novel Adaptive Variational Fully-Eulerian Scheme for Fluid-Structure Interaction
ORAL
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Presenters
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Biswajeet Rath
University of British columbia
Authors
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Biswajeet Rath
University of British columbia
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Xiaoyu Mao
University of British Columbia
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Rajeev K Jaiman
Mechanical Engineering, University of British Columbia, University of British Columbia
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