Out-of-Core Streamline Visualization on Large Unstructured Meshes

It's advantageous for computational scientists to have the capability to perform interactive visualization on their desktop workstations. For data on large unstructured meshes, this capability is not generally available. In particular, particle tracing on unstructured grids can result in a high percentage of non-contiguous memory accesses and therefore may perform very poorly with virtual memory paging schemes. The alternative of visualizing a lower resolution of the data degrades the original high-resolution calculations. This paper presents an out-of-core approach for interactive streamline construction on large unstructured tetrahedral meshes containing millions of elements. The out-of-core algorithm uses an octree to partition and restructure the raw data into subsets stored into disk files for fast data retrieval. A memory management policy tailored to the streamline calculations is used such that during the streamline construction only a very small amount of data are brought into the main memory on demand. By carefully scheduling computation and data fetching, the overhead of reading data from the disk is significantly reduced and good memory performance results. This out-of-core algorithm makes possible interactive streamline visualization of large unstructured-grid data sets on a single mid-range workstation with relatively low main-memory capacity: 5-20 megabytes. Our test results also show that this approach is much more efficient than relying on virtual memory and operating system's paging algorithms

Identification of vortex in unstructured mesh with graph neural networks

Deep learning has been employed to identify flow characteristics from Computational Fluid Dynamics (CFD) databases to assist the researcher to better understand the flow field, to optimize the geometry design and to select the correct CFD configuration for corresponding flow characteristics. Convolutional Neural Network (CNN) is one of the most popular algorithms used to extract and identify flow features. However its use, without any additional flow field interpolation, is limited to the simple domain geometry and regular meshes which limits its application to real industrial cases where complex geometry and irregular meshes are usually used. Aiming at the aforementioned problems, we present a Graph Neural Network (GNN) based model with U-Net architecture to identify the vortex in CFD results on unstructured meshes. The graph generation and graph hierarchy construction using algebraic multigrid method from CFD meshes are introduced. A vortex auto-labeling method is proposed to label vortex regions in 2D CFD meshes. We precise our approach by firstly optimizing the input set on CNNs, then benchmarking current GNN kernels against CNN model and evaluating the performances of GNN kernels in terms of classification accuracy, training efficiency and identified vortex morphology. Finally, we demonstrate the adaptability of our approach to unstructured meshes and generality to unseen cases with different turbulence models at different Reynolds numbers.Comment: Accepted by the journal Computers & Fluid

Development of CPANEL, An Unstructured Panel Code, Using a Modified TLS Velocity Formulation

The use of panel codes in the aerospace industry dates back many decades. Recent advances in computer capability have allowed them to evolve, both in speed and complexity, to provide very quick solutions to complex flow fields. By only requiring surface discretization, panel codes offer a faster alternative to volume based methods, delivering a solution in minutes, as opposed to hours or days. Despite their utility, the availability of these codes is very limited due to either cost, or rights restrictions. This work incorporates modern software development practices, such as unit level testing and version control, into the development of an unstructured panel code, CPanel, with an object-oriented approach in C++. CPanel utilizes constant source and doublet panels to define the geometry and a vortex sheet wake representation. An octree data structure is employed to enhance the speed of geometrical queries and lay a framework for the application of a fast tree method. The challenge of accurately calculating surface velocities on an unstructured discretization is addressed with a constrained Hermite Taylor least-squares velocity formulation. Future enhancement was anticipated throughout development, leaving a strong framework from which to perform research on methods to more accurately predict the physical flow field with a tool based in potential flow theory. Program results are verified using the analytical solution for flow around an ellipsoid, vortex lattice method solutions for simple planforms, as well an anchored panel code, CBAERO. CPanel solutions show strong agreement with these methods and programs. Additionally, aerodynamic coefficients calculated via surface integration are consistent with those calculated from a Trefftz plane analysis in CPanel. This consistency is not demonstrated in solutions from CBAERO, suggesting the CHTLS velocity formulation is more accurate than more commonly used vortex core methods

Surface-based flow visualization

This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/computers-and-graphics/.With increasing computing power, it is possible to process more complex fluid simulations. However, a gap between increasing\ud data size and our ability to visualize them still remains. Despite the great amount of progress that has been made in the field of\ud flow visualization over the last two decades, a number of challenges remain. Whilst the visualization of 2D flow has many good\ud solutions, the visualization of 3D flow still poses many problems. Challenges such as domain coverage, speed of computation, and\ud perception remain key directions for further research. Flow visualization with a focus on surface-based techniques forms the basis\ud of this literature survey, including surface construction techniques and visualization methods applied to surfaces. We detail our\ud investigation into these algorithms with discussions of their applicability and their relative strengths and drawbacks. We review the\ud most important challenges when considering such visualizations. The result is an up-to-date overview of the current state-of-the-art\ud that highlights both solved and unsolved problems in this rapidly evolving branch of research

Knowledge-based out-of-core algorithms for data management in visualization

Journal ArticleData management is the very first issue in handling very large datasets. Many existing out-of-core algorithms used in visualization are closely coupled with application-specific logic. This paper presents two knowledgebased out-of-core prefetching algorithms that do not use hard-coded rendering-related logic. They acquire the knowledge of the access history and patterns dynamically, and adapt their prefetching strategies accordingly. We have compared the algorithms with a demand-based algorithm, as well as a more domain-specific out-of-core algorithm. We carried out our evaluation in conjunction with an example application where rendering multiple point sets in a volume scene graph put a great strain on the rendering algorithm in terms of memory management. Our results have shown that the knowledge-based approach offers a better cache-hit to disk-access trade-off. This work demonstrates that it is possible to build an out-of-core prefetching algorithm without depending on rendering-related application-specific logic. The knowledge based approach has the advantage of being generic, efficient, flexible and self-adaptive

Effects of Cavities and Protuberances on Transition over Hypersonic Vehicles

Surface protuberances and cavities on a hypersonic vehicle are known to cause several aerodynamic or aerothermodynamic issues. Most important of all, premature transition due to these surface irregularities can lead to a significant rise in surface heating. To help understand laminar-turbulent transition induced by protuberances or cavities on a Crew Exploration Vehicle (CEV) surface, high-fidelity numerical simulations are carried out for both types of trips on a CEV wind tunnel model. Due to the large bluntness, these surface irregularities reside in an accelerating subsonic boundary layer. For the Mach 6 wind tunnel conditions with a roughness Reynolds number Re(sub kk) of 800, it was found that a protuberance with a height to boundary layer thickness ratio of 0.73 leads to strong wake instability and spontaneous vortex shedding, while a cavity with identical geometry only causes a rather weak flow unsteadiness. The same cavity with a larger Reynolds number also leads to similar spontaneous vortex shedding and wake instability. The wake development and the formation of hairpin vortices for both protuberance and cavity were found to be qualitatively similar to that observed for an isolated hemisphere submerged in a subsonic, low speed flat-plate boundary layer. However, the shed vortices and their accompanying instability waves were found to be slightly stabilized downstream by the accelerating boundary layer along the CEV surface. Despite this stabilizing influence, it was found that the wake instability spreads substantially in both wall-normal and azimuthal directions as the flow is evolving towards a transitional state. Similarities and differences between the wake instability behind a protuberance and a cavity are investigated. Computations for the Mach 6 boundary layer over a slender cylindrical roughness element with a height to the boundary layer thickness of about 1.1 also shows spontaneous vortex shedding and strong wake instability. Comparisons of detailed flow structures associated with protuberances at subsonic and supersonic edge Mach numbers indicate distinctively different instability mechanisms

CFD investigation of a core-mounted-target-type thrust reverser, Part 2: reverser deployed configuration

CMTTTR design was proposed by NASA in the second half of the 90's. NASA carried out several experiments at static conditions, and their acquired results suggested that the performance characteristics of the CMTTTR design falls short to comply with the mandatory TR performance criteria, and were therefore regarded as an infeasible design. However, the authors of this paper believe that the results presented by NASA for CMTTTR design require further exploration to facilitate the complete understanding of its true performance potential. This Part2 paper is a continuation from Part1and presents a comprehensive three-dimensional (CFD) analyses of the CMTTTR in deployed configuration; the analyses at forward flight conditions will be covered in Part 3. The key objectives of this paper are: first, to validate the acquired CFD results with the experimental data provided by NASA: this is achieved by measuring the static pressure values on various surfaces of the deployed CMTTTR model. The second objective is to estimate the performance characteristics of the CMTTTR design and corroborate the results with experimental data. The third objective is to estimate the Pressure Thrust (i.e. axial thrust generated due to the pressure difference across various reverser surfaces) and discuss its significance for formulating the performance of any thrust reverser design. The fourth objective is to investigate the influence of kicker plate installation on overall TR performance. The fifth and final objective is to examine and discuss the overall flow physics associated with the thrust reverser under deployed configuration

Doctor of Philosophy

dissertationSmoothness-increasing accuracy-conserving (SIAC) filters were introduced as a class of postprocessing techniques to ameliorate the quality of numerical solutions of discontinuous Galerkin (DG) simulations. SIAC filtering works to eliminate the oscillations in the error by introducing smoothness back to the DG field and raises the accuracy in the L2-n o rm up to its natural superconvergent accuracy in the negative-order norm. The increased smoothness in the filtered DG solutions can then be exploited by simulation postprocessing tools such as streamline integrators where the absence of continuity in the data can lead to erroneous visualizations. However, lack of extension of this filtering technique, both theoretically and computationally, to nontrivial mesh structures along with the expensive core operators have been a hindrance towards the application of the SIAC filters to more realistic simulations. In this dissertation, we focus on the numerical solutions of linear hyperbolic equations solved with the discontinuous Galerkin scheme and provide a thorough analysis of SIAC filtering applied to such solution data. In particular, we investigate how the use of different quadrature techniques could mitigate the extensive processing required when filtering over the whole computational field. Moreover, we provide detailed and efficient algorithms that a numerical practitioner requires to know in order to implement this filtering technique effectively. In our first attempt to expand the application scope of this filtering technique, we demonstrate both mathematically and through numerical examples that it is indeed possible to observe SIAC filtering characteristics when applied to numerical solutions obtained over structured triangular meshes. We further provide a thorough investigation of the interplay between mesh geometry and filtering. Building upon these promising results, we present how SIAC filtering could be applied to gain higher accuracy and smoothness when dealing with totally unstructured triangular meshes. Lastly, we provide the extension of our filtering scheme to structured tetrahedral meshes. Guidelines and future work regarding the application of the SIAC filter in the visualization domain are also presented. We further note that throughout this document, the terms postprocessing and filtering will be used interchangeably