55 research outputs found
Interactive visualization of computational fluid dynamics data.
This thesis describes a literature study and a practical research in the area of flow visualization, with special emphasis on the interactive visualization of Computational Fluid Dynamics (CFD) datasets. Given the four main categories of flow visualization methodology; direct, geometric, texture-based and feature-based flow visualization, the research focus of our thesis is on the direct, geometric and feature-based techniques. And the feature-based flow visualization is highlighted in this thesis. After we present an overview of the state-of-the-art of the recent developments in the flow visualization in higher spatial dimensions (2.5D, 3D and 4D), we propose a fast, simple, and interactive glyph placement algorithm for investigating and visualizing boundary flow data based on unstructured, adaptive resolution boundary meshes from CFD dataset. Afterward, we propose a novel, automatic mesh-driven vector field clustering algorithm which couples the properties of the vector field and resolution of underlying mesh into a unified distance measure for producing high-level, intuitive and suggestive visualization of large, unstructured, adaptive resolution boundary CFD meshes based vector fields. Next we present a novel application with multiple-coordinated views for interactive information-assisted visualization of multidimensional marine turbine CFD data. Information visualization techniques are combined with user interaction to exploit our cognitive ability for intuitive extraction of flow features from CFD datasets. Later, we discuss the design and implementation of each visualization technique used in our interactive flow visualization framework, such as glyphs, streamlines, parallel coordinate plots, etc. In this thesis, we focus on the interactive visualization of the real-world CFD datasets, and present a number of new methods or algorithms to address several related challenges in flow visualization. We strongly believe that the user interaction is a crucial part of an effective data analysis and visualization of large and complex datasets such as CFD datasets we use in this thesis. In order to demonstrate the use of the proposed techniques in this thesis, CFD domain experts reviews are also provided
Numerical simulation of fracture pattern development and implications for fuid flow
Simulations are instrumental to understanding
flow through discrete fracture
geometric representations that capture the large-scale permeability structure of
fractured porous media. The contribution of this thesis is threefold: an efficient
finite-element finite-volume discretisation of the advection/diffusion
flow equations, a
geomechanical fracture propagation algorithm to create fractured rock analogues,
and a study of the effect of growth on hydraulic conductivity. We describe an
iterative geomechanics-based finite-element model to simulate quasi-static crack
propagation in a linear elastic matrix from an initial set of random
flaws. The
cornerstones are a failure and propagation criterion as well as a geometric kernel for
dynamic shape housekeeping and automatic remeshing. Two-dimensional patterns
exhibit connectivity, spacing, and density distributions reproducing en echelon crack
linkage, tip hooking, and polygonal shrinkage forms. Differential stresses at the
boundaries yield fracture curving. A stress field study shows that curvature can be
suppressed by layer interaction effects. Our method is appropriate to model layered
media where interaction with neighbouring layers does not dominate deformation.
Geomechanically generated fracture patterns are the input to single-phase
flow
simulations through fractures and matrix. Thus, results are applicable to fractured
porous media in addition to crystalline rocks. Stress state and deformation history
control emergent local fracture apertures. Results depend on the number of initial
flaws, their initial random distribution, and the permeability of the matrix. Straightpath
fracture pattern simplifications yield a lower effective permeability in comparison
to their curved counterparts. Fixed apertures overestimate the conductivity of
the rock by up to six orders of magnitude. Local sample percolation effects
are representative of the entire model
flow behaviour for geomechanical apertures.
Effective permeability in fracture dataset subregions are higher than the overall
conductivity of the system. The presented methodology captures emerging patterns
due to evolving geometric and
flow properties essential to the realistic simulation of
subsurface processes
Recommended from our members
7th International Meshing Roundtable '98
The goal of the 7th International Meshing Roundtable is to bring together researchers and developers from industry, academia, and government labs in a stimulating, open environment for the exchange of technical information related to the meshing process. In the past, the Roundtable has enjoyed significant participation from each of these groups from a wide variety of countries
Ultra-fast screening of stress-sensitive (naturally fractured) reservoirs using flow diagnostics
Quantifying the impact of poro-mechanics on reservoir performance is critical to the
sustainable management of subsurface reservoirs containing either hydrocarbons,
groundwater, geothermal heat, or being targeted for geological storage of fluids (e.g., CO2
or H2). On the other hand, accounting for poro-mechanical effects in full-field reservoir
simulation studies and uncertainty quantification workflows in complex reservoir models
is challenging, mainly because exploring and capturing the full range of geological and
mechanical uncertainties requires a large number of numerical simulations and is hence
computationally intensive. Specifically, the integration of poro-mechanical effects in
full-field reservoir simulation studies is still limited, mainly because of the high
computational cost. Consequently, poro-mechanical effects are often ignored in reservoir
engineering workflows, which may result in inadequate reservoir performance forecasts.
This thesis hence develops an alternative approach that couples hydrodynamics using
existing flow diagnostics simulations for single- and dual-porosity models with poro mechanics to screen the impact of coupled poro-mechanical processes on reservoir
performance. Due to the steady-state nature of the calculations and the effective proposed
coupling strategy, these calculations remain computationally efficient while providing
first-order approximations of the interplay between poro-mechanics and hydrodynamics,
as we demonstrate through a series of case studies. This thesis also introduces a new
uncertainty quantification workflow using the proposed poro-mechanical informed flow
diagnostics and proxy models. These computationally efficient calculations allow us to
quickly screen poro-mechanics and assess a broader range of geological, petrophysical,
and mechanical uncertainties to rank, compare, and cluster a large ensemble of models to
select representative candidates for more detailed full-physics coupled reservoir
simulations.James Watt Scholarshi
A Deep Learning Approach to Evaluating Disease Risk in Coronary Bifurcations
Cardiovascular disease represents a large burden on modern healthcare systems, requiring significant resources for patient monitoring and clinical interventions. It has been shown that the blood flow through coronary arteries, shaped by the artery geometry unique to each patient, plays a critical role in the development and progression of heart disease. However, the popular and well tested risk models such as Framingham and QRISK3 current cardiovascular disease risk models are not able to take these differences when predicting disease risk.
Over the last decade, medical imaging and image processing have advanced to the point that non-invasive high-resolution 3D imaging is routinely performed for any patient suspected of coronary artery disease. This allows for the construction of virtual 3D models of the coronary anatomy, and in-silico analysis of blood flow within the coronaries. However, several challenges still exist which preclude large scale patient-specific simulations, necessary for incorporating haemodynamic risk metrics as part of disease risk prediction. In particular, despite a large amount of available coronary medical imaging, extraction of the structures of interest from medical images remains a manual and laborious task. There is significant variation in how geometric features of the coronary arteries are measured, which makes comparisons between different studies difficult. Modelling blood flow conditions in the coronary arteries likewise requires manual preparation of the simulations and significant computational cost.
This thesis aims to solve these challenges. The "Automated Segmentation of Coronary Arteries (ASOCA)" establishes a benchmark dataset of coronary arteries and their associated 3D reconstructions, which is currently the largest openly available dataset of coronary artery models and offers a wide range of applications such as computational modelling, 3D printed for experiments, developing, and testing medical devices such as stents, and Virtual Reality applications for education and training. An automated computational modelling workflow is developed to set up, run and postprocess simulations on the Left Main Bifurcation and calculate relevant shape metrics. A convolutional neural network model is developed to replace the computational fluid dynamics process, which can predict haemodynamic metrics such as wall shear stress in minutes, compared to several hours using traditional computational modelling reducing the computation and labour cost involved in performing such simulations
Software for Exascale Computing - SPPEXA 2016-2019
This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest
Computational Aspects of Heat Transfer in Structures
Techniques for the computation of heat transfer and associated phenomena in complex structures are examined with an emphasis on reentry flight vehicle structures. Analysis methods, computer programs, thermal analysis of large space structures and high speed vehicles, and the impact of computer systems are addressed
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