View-dependent adaptive cloth simulation

This paper describes a method for view-dependent cloth simulation using dynamically adaptive mesh refinement and coarsening. Given a prescribed camera motion, the method adjusts the criteria controlling refinement to account for visibility and apparent size in the camera's view. Objectionable dynamic artifacts are avoided by anticipative refinement and smoothed coarsening. This approach preserves the appearance of detailed cloth throughout the animation while avoiding the wasted effort of simulating details that would not be discernible to the viewer. The computational savings realized by this method increase as scene complexity grows, producing a 2× speed-up for a single character and more than 4× for a small group

Quantification of Turbulence Statistics for the Near-Wall Region in Unstructured Pebble Beds Using Direct Numerical Simulation

The aim of this study was to employing high-fidelity computational fluid dynamics to investi-gate and quantify the turbulent flow effects for incompressible, isothermal fluid flows, in the near wall region of unstructured, randomly packed spheres. The flow domain treated in this study is a replication of an experimental setup and analogous to those encountered in pebble bed based high-temperature reactors. A new meshing strategy and meshing assumptions have been employed to decrease the mesh size and these were validated with the experimental results. Quantifying turbulent flow effects sever a dual purpose: One, it assists lower-fidelity engineering tool development, such as those incorporating Reynolds averaged Navier-Stokes based methodologies, and two deepens our fundamental understanding of the physics involved for in-compressible flows over complex geometries. Nek5000, an open source spectral element computational fluid dynamics code, developed by Argonne National Lab was used to conduct this study. The code was used to perform a series of direct numerical simulations on the experimental geometry at low to moderate Reynolds numbers, matching the experimental flow parameters, to validate the model. This was done for a the full and section of the geometry to investigate the cross flow dependence in the problem. Presented results include the comparison between experimental and numerical findings, the development of a high-fidelity database of the experimental geometry at low and moderate Reynolds number, identification of possible flow phenomena present in random packed spheres as well as the calculation of the first and second order statistics in the near wall domain of random packed spheres

Finite Element Modeling Driven by Health Care and Aerospace Applications

This thesis concerns the development, analysis, and computer implementation of mesh generation algorithms encountered in finite element modeling in health care and aerospace. The finite element method can reduce a continuous system to a discrete idealization that can be solved in the same manner as a discrete system, provided the continuum is discretized into a finite number of simple geometric shapes (e.g., triangles in two dimensions or tetrahedrons in three dimensions). In health care, namely anatomic modeling, a discretization of the biological object is essential to compute tissue deformation for physics-based simulations. This thesis proposes an efficient procedure to convert 3-dimensional imaging data into adaptive lattice-based discretizations of well-shaped tetrahedra or mixed elements (i.e., tetrahedra, pentahedra and hexahedra). This method operates directly on segmented images, thus skipping a surface reconstruction that is required by traditional Computer-Aided Design (CAD)-based meshing techniques and is convoluted, especially in complex anatomic geometries. Our approach utilizes proper mesh gradation and tissue-specific multi-resolution, without sacrificing the fidelity and while maintaining a smooth surface to reflect a certain degree of visual reality. Image-to-mesh conversion can facilitate accurate computational modeling for biomechanical registration of Magnetic Resonance Imaging (MRI) in image-guided neurosurgery. Neuronavigation with deformable registration of preoperative MRI to intraoperative MRI allows the surgeon to view the location of surgical tools relative to the preoperative anatomical (MRI) or functional data (DT-MRI, fMRI), thereby avoiding damage to eloquent areas during tumor resection. This thesis presents a deformable registration framework that utilizes multi-tissue mesh adaptation to map preoperative MRI to intraoperative MRI of patients who have undergone a brain tumor resection. Our enhancements with mesh adaptation improve the accuracy of the registration by more than 5 times compared to rigid and traditional physics-based non-rigid registration, and by more than 4 times compared to publicly available B-Spline interpolation methods. The adaptive framework is parallelized for shared memory multiprocessor architectures. Performance analysis shows that this method could be applied, on average, in less than two minutes, achieving desirable speed for use in a clinical setting. The last part of this thesis focuses on finite element modeling of CAD data. This is an integral part of the design and optimization of components and assemblies in industry. We propose a new parallel mesh generator for efficient tetrahedralization of piecewise linear complex domains in aerospace. CAD-based meshing algorithms typically improve the shape of the elements in a post-processing step due to high complexity and cost of the operations involved. On the contrary, our method optimizes the shape of the elements throughout the generation process to obtain a maximum quality and utilizes high performance computing to reduce the overheads and improve end-user productivity. The proposed mesh generation technique is a combination of Advancing Front type point placement, direct point insertion, and parallel multi-threaded connectivity optimization schemes. The mesh optimization is based on a speculative (optimistic) approach that has been proven to perform well on hardware-shared memory. The experimental evaluation indicates that the high quality and performance attributes of this method see substantial improvement over existing state-of-the-art unstructured grid technology currently incorporated in several commercial systems. The proposed mesh generator will be part of an Extreme-Scale Anisotropic Mesh Generation Environment to meet industries expectations and NASA\u27s CFD visio

Scene relighting and editing for improved object insertion

Abstract. The goal of this thesis is to develop a scene relighting and object insertion pipeline using Neural Radiance Fields (NeRF) to incorporate one or more objects into an outdoor environment scene. The output is a 3D mesh that embodies decomposed bidirectional reflectance distribution function (BRDF) characteristics, which interact with varying light source positions and strengths. To achieve this objective, the thesis is divided into two sub-tasks. The first sub-task involves extracting visual information about the outdoor environment from a sparse set of corresponding images. A neural representation is constructed, providing a comprehensive understanding of the constituent elements, such as materials, geometry, illumination, and shadows. The second sub-task involves generating a neural representation of the inserted object using either real-world images or synthetic data. To accomplish these objectives, the thesis draws on existing literature in computer vision and computer graphics. Different approaches are assessed to identify their advantages and disadvantages, with detailed descriptions of the chosen techniques provided, highlighting their functioning to produce the ultimate outcome. Overall, this thesis aims to provide a framework for compositing and relighting that is grounded in NeRF and allows for the seamless integration of objects into outdoor environments. The outcome of this work has potential applications in various domains, such as visual effects, gaming, and virtual reality

Quantification of Turbulence Statistics for the Near-Wall Region in Unstructured Pebble Beds Using Direct Numerical Simulation

The aim of this study was to employing high-fidelity computational fluid dynamics to investi-gate and quantify the turbulent flow effects for incompressible, isothermal fluid flows, in the near wall region of unstructured, randomly packed spheres. The flow domain treated in this study is a replication of an experimental setup and analogous to those encountered in pebble bed based high-temperature reactors. A new meshing strategy and meshing assumptions have been employed to decrease the mesh size and these were validated with the experimental results. Quantifying turbulent flow effects sever a dual purpose: One, it assists lower-fidelity engineering tool development, such as those incorporating Reynolds averaged Navier-Stokes based methodologies, and two deepens our fundamental understanding of the physics involved for in-compressible flows over complex geometries. Nek5000, an open source spectral element computational fluid dynamics code, developed by Argonne National Lab was used to conduct this study. The code was used to perform a series of direct numerical simulations on the experimental geometry at low to moderate Reynolds numbers, matching the experimental flow parameters, to validate the model. This was done for a the full and section of the geometry to investigate the cross flow dependence in the problem. Presented results include the comparison between experimental and numerical findings, the development of a high-fidelity database of the experimental geometry at low and moderate Reynolds number, identification of possible flow phenomena present in random packed spheres as well as the calculation of the first and second order statistics in the near wall domain of random packed spheres

Computational Physics II

E-kursuse materjal

Real-time hybrid cutting with dynamic fluid visualization for virtual surgery

It is widely accepted that a reform in medical teaching must be made to meet today's high volume training requirements. Virtual simulation offers a potential method of providing such trainings and some current medical training simulations integrate haptic and visual feedback to enhance procedure learning. The purpose of this project is to explore the capability of Virtual Reality (VR) technology to develop a training simulator for surgical cutting and bleeding in a general surgery