National Institute of Aerospace
Computational Fluid Dynamics Seminar

A place to share ideas and problems for barrier-breaking developments

NIA CFD Seminar, Season 9 (2019-2020)
#124:   03-06-2020, Zhanping Liu
High-Performance Flow Visualization: Research, Development, and Application
#123:   09-04-2019, Lingquan Li
Hyperbolic Navier-Stokes Method Based on Reconstructed-Discontinuous-Galerkin or Reconstructed-Finite-Volume Formulation with Variational Reconstruction

124th NIA CFD Seminar:

03-06-2020   11:00am-noon (EDT)   NIA Room 137      video

High-Performance Flow Visualization: Research, Development, and Application

Scientific visualization is an important application of computer graphics to scientific computing by providing deep insight into the pattern underlying large-scale data. Vector data visualization, or flow visualization, plays a crucial role in a wide variety of areas such as oceanographic- atmospheric modeling, computational fluid dynamics simulation, and electro-magnetic field analysis, to name only a few. It is unique for depicting directional information in addition to the spatial distribution and geometric structures that scalar data visualization, as a sibling, is intended to reveal. The last two decades have seen many geometry-based and texture-based algorithms for visualizing flows ranging from steady to unsteady and from 2D to 3D. As we seek the most effective representations for exploring surface and volume flows, there are signs of revisiting geometry-based methods with further improvement, from texture-based approaches, and resorting to parallel visualization. This talk provides a high-level and sampling description of Dr. Liu's algorithmic research and system development along this path in high-performance flow visualization over the past 20+ years, involving AUFLIC (Accelerated Unsteady Flow Line Integral Convolution), VAUFLIC (Volume AUFLIC), ADVESS (ADVanced Evenly Spaced Streamline placement), IVDESS (Interactive View-Driven Evenly Spaced Streamline placement), his two-year industrial experience at Kitware, Inc. for participating in the development of two large-scale cross-platform open-source general-purpose visualization packages VTK and ParaView, as well as his recent work on Parallel Flow Visualization (via GPU, multi-threading, and MPI). Also demonstrated are three 'Active' systems / packages (irrelevant to any project) that he independently developed for interactive flow visualization, i.e., ActiveLIC (Line Integral Convolution), ActiveIBFV (Image-Based Flow Visualization), and ActiveFLOVE (FLOw Visualization Environment).

[ presentation file (pdf) ] Zhanping Liu

Speaker Bio: Dr. Zhanping Liu is a tenure-track assistant professor with the department of Computational Modeling and Simulation Engineering at Old Dominion University. He was a USRA (Universities Space Research Association) faculty visitor of the division of NASA Advanced Supercomputing (NAS) at NASA Ames Research Center (May ~ July, 2014) and an FRPP (Faculty Research Participation Program) faculty visitor of the division of Mathematics and Computer Science (MCS) at Argonne National Lab (May ~ July, 2013) during his employment as a tenure-track assistant professor with the department of Computer Science at Kentucky State University (2011 ~ 2016). Much earlier, Dr. Liu was a research fellow of the Bio-medical Image Analysis Group of the School of Medicine at the University of Pennsylvania (2010 ~ 2011), a research staff member of the Scientific Computing and Visualization Group at Kitware, Inc. ("Leaders in Visualization Technology", 2008 ~ 2010), a research scientist of the Visualization Analysis and Imaging Lab (VAIL) of the High- Performance Computing Collaboratory (HPC2) at Mississippi State University (2001 ~ 2008), and a post- doctoral associate of the Micro-CT Image Reconstruction and Volume Visualization Lab of the College of Medicine at the University of Iowa (2000 ~ 2001). He received the PhD degree in Computer Science from Peking University (2000) and the BS degree in Mathematics from Nankai University (1992). His research interests consist in scientific visualization (particularly vector / flow data visualization), parallel visualization (via GPU, multi-threading, and MPI), and data analysis. Since 1997, Dr. Liu has been performing not only algorithmic research but also system development (using C/C++) in scientific visualization. He has independently developed 9 data visualization systems / packages (VF-VTK, RadVis, Triton-II-Flow, ActiveLIC, ActiveIBFV, ActiveFLOVE, SynVizer, cuVis, and TB-CLIC), without dependence on any third-party library / tool. He participated in the development of VTK, ParaView, and DOXIV. More information about his work and personal hobbies are available at www.zhanpingliu.org.

123rd NIA CFD Seminar:

09-04-2019   11:00am-noon (EDT)   NIA Room 137      video

Hyperbolic Navier-Stokes Method Based on Reconstructed-Discontinuous-Galerkin or Reconstructed-Finite-Volume Formulation with Variational Reconstruction

The objective of the presented work is to develop an efficient, accurate and compact method for solving compressible Navier-Stokes (NS) equations by combining the hyperbolic Navier-Stokes (HNS) formulation and the reconstructed discontinuous Galerkin method (rDG), which includes the finite-volume (FV) and discontinuous Galerkin methods. A new HNS formulation is derived, so that an efficient high-order construction for compressible NS equations can be derived. The gradients of the primitive variables such as density, velocity and temperature are introduced as additional unknowns. The newly introduced gradients can be recycled to get a higher-order polynomial solution for these primitive variables. An even more accurate method is obtained when reconstruction is performed on these gradient variables. These reconstructed variables are also reused as higher-order derivatives of the primitive variables. In the presented work, a variational formulation is used for reconstruction. This variational reconstruction (VR) can be seen as an extension of the compact finite difference schemes to unstructured grids. The reconstructed variables are obtained by solving an extreme value problem, which minimizes the jumps at cell interfaces, and therefore maximizes the smoothness of the reconstructed polynomials. The spatial discretization is performed by multiplying the HNS system by a test function matrix. If the matrix is taken as a diagonal matrix, then the primitive variables and auxiliary variables are regarded as decoupled. This will generate a FV type formulation, which is denoted as HNS+rFV method. If the matrix is taken as the primitive variables and auxiliary variables are coupled, a Galerkin type formulation is obtained, which is denoted as HNS+rDG method. All the primitive variables, auxiliary variables and reconstructed variables are stored in a consistent way with the Taylor-basis DG counterpart. The fully implicit method is implemented for steady problems, while a third-order implicit Runge-Kutta (IRK), i.e., ESDIRK3 time marching method is implemented for unsteady flows. In these implicit methods, an automatic differentiation tool TAPENADE is used to obtain the resulting flux Jacobian matrices. The approximate system of linear equations arising from the Newton iteration is solved with two methods: symmetric gauss-seidel (SGS) and general minimum residual (GMRES) algorithm with lower-upper symmetric gauss-seidel (LU-SGS) preconditioning.

[ presentation file (pdf) ] Lingquan Li

Speaker Bio: Lingquan Li received the Bachelor's degree of Aerospace Engineering at Xi'an Jiaotong University, Xi'an, Shaanxi Province, China, in July 2011. She then attended the graduate school at Fudan University, Shanghai, China, in September 2013 and studied under the supervision of Dr. Aiming Yang. She received the Master's degree of Aerospace Engineering in July 2016. The author was then recruited by the Department of Mechanical and Aerospace Engineering at North Carolina State University (NCSU), Raleigh, North Carolina, USA, and studied in the doctoral program of Aerospace Engineering since August 2016. Her academic advisor is Dr. Hong Luo.

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