75th NIA CFD Seminar Webcast: A Data-Driven, Physics-Informed Approach towards Predictive Turbulence Modeling by Heng Xiao

April 29, 2016 - Leave a Response

75th NIA CFD Seminar

Topic: A Data-Driven, Physics-Informed Approach towards Predictive Turbulence Modeling

Date: Wednesday, May 4, 2016

Time: 11:00am-noon (EST)

Room: NIA, Rm137

Speaker: Heng Xiao

Speaker Bio: Dr. Heng Xiao is an Assistant Professor in the Department of Aerospace and Ocean Engineering at Virginia Tech. He holds a bachelor’s degree in Civil Engineering from Zhejiang University, China, a master’s degree in Mathematics from the Royal Institute of Technology (KTH), Sweden, and a Ph.D. degree in Civil Engineering from Princeton University, USA. Before joining Virginia Tech in 2013, he worked as a postdoctoral researcher at the Institute of Fluid Dynamics in ETH Zurich, Switzerland, from 2009 to 2012. His current research interests lie in model uncertainty quantification in turbulent flow simulations. He is also interested in developing novel algorithms for high-fidelity simulations of particle-laden flows with application to sediment transport problems. More information can be found in the manuscripts below or from the presenter’s website:
https://sites.google.com/a/vt.edu/hengxiao/papers

Abstract: Despite their well-known limitations, Reynolds-Averaged Navier-Stokes (RANS) models are still the workhorse tools for engineering turbulent flow simulations. In this talk we present a data-driven, physics-informed approach for quantifying and reducing model-form uncertainties in RANS simulations. The framework utilizes an ensemble-based Bayesian inference method to incorporate all sources of available information, including empirical prior knowledge, physical constraints (e.g., realizability, smoothness, and symmetric), and available observation data [1-2]. When there are no available data on the flow to be predicted, we showed that the Reynolds stress discrepancy can be calibrated on related flows where data are available [3]. This finding has profound physical and modeling implications, i.e., the errors in RANS modeled Reynolds stresses are not random, but can be well explained by the mean flow features. This work demonstrates the potential of the data-driven predictive turbulence modeling approach based on standard RANS models, which is an alternative to advanced RANS models.

Additional information, including the webcast link, can be found at the NIA CFD Seminar website, which is temporarily located at

http://www.hiroakinishikawa.com/niacfds/index.html

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TODAY: 74th NIA CFD Seminar Webcast: Robust Adaptive High-Order Geometric and Numerical Methods Based on Weighted Least Squares by Xiangmin (Jim) Jiao

March 24, 2016 - Leave a Response

74th NIA CFD Seminar

Topic: Robust Adaptive High-Order Geometric and Numerical Methods Based on Weighted Least Squares

Date: Thursday, March 24, 2016

Time: 2:00pm-3:00pm (EST)

Room: NIA, Rm137

Speaker: Xiangmin (Jim) Jiao

Speaker Bio: Dr. Xiangmin (Jim) Jiao is an Associated Professor in Applied Mathematics and Computer Science, and also a core faculty member of the Institute for Advanced Computational Science at Stony Brook University. He received his Ph.D. in Computer Science in 2001 from University of Illinois at Urbana-Champaign (UIUC). He was a Research Scientist at the Center for Simulation of Advanced Rockets (CSAR) at UIUC between 2001 and 2005, and then an Assistant Professor in College of Computing at Georgia Institute of Technology between 2005 and 2007. His research interests focus on high-performance geometric and numerical computing, including applied computational and differential geometry, generalized finite difference and finite element methods, multigrid and iterative methods for sparse linear systems, multiphysics coupling, and problem solving environments, with applications in computational fluid dynamics, structural mechanics, biomedical engineering, climate modeling, etc.

Abstract: Numerical solutions of partial differential equations (PDEs) are important for modeling and simulations in many scientific and engineering applications. Their solutions over com- plex geometries pose significant challenges in efficient surface and volume mesh generation and robust numerical discretizations. In this talk, we present our recent work in tackling these challenges from two aspects. First, we will present accurate and robust high-order geomet- ric algorithms on discrete surface, to support high-order surface reconstruction, surface mesh generation and adaptation, and computation of differential geometric operators, without the need to access the CAD models. Secondly, we present some new numerical discretization tech- niques, including a generalized finite element method based on adaptive extended stencils, and a novel essentially nonoscillatory scheme for hyperbolic conservation laws on unstructured meshes. These new discretizations are more tolerant of mesh quality and allow accurate, stable and efficient computations even on meshes with poorly shaped elements. Based on a unified theoretical framework of weighted least squares, these techniques can significantly simplify the mesh generation processes, especially on supercomputers, and also enable more efficient and robust numerical computations. We will present the theoretical foundation of our methods and demonstrate their applications for mesh generation and numerical solutions of PDEs.

Additional information, including the webcast link, can be found at the NIA CFD Seminar website, which is temporarily located at

http://www.hiroakinishikawa.com/niacfds/index.html

niacfds_square

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