BACKGROUND AND PURPOSE: Hemodynamic factors are thought to play an important role in the initiation, growth, and rupture of cerebral aneurysms. This report describes a pilot clinical study of the association between intra-aneurysmal hemodynamic characteristics from computational fluid dynamic models and the rupture of cerebral aneurysms. METHODS: A total of 62 patient-specific models of cerebral aneurysms were constructed from 3D angiography images. Computational fluid dynamics simulations were performed under pulsatile flow conditions measured on a normal subject. The aneurysms were classified into different categories, depending on the complexity and stability of the flow pattern, the location and size of the flow impingement region, and the size of the inflow jet. The 62 models consisted of 25 ruptured and 34 unruptured aneurysms and 3 cases with unknown histories of hemorrhage. The hemodynamic features were analyzed for associations with history of rupture. RESULTS: A large variety of flow patterns was observed: 72% of ruptured aneurysms had complex or unstable flow patterns, 80% had small impingement regions, and 76% had small jet sizes. By contrast, unruptured aneurysms accounted for 73%, 82%, and 75% of aneurysms with simple stable flow patterns, large impingement regions, and large jet sizes, respectively. Aneurysms with small impingement sizes were 6.3 times more likely to have experienced rupture than those with large impingement sizes (P = .01). CONCLUSIONS: Image-based patient-specific numeric models can be constructed in an efficient manner that allows clinical studies of intra-aneurysmal hemodynamics. A simple flow characterization system was proposed, and interesting trends in the association between hemodynamic features and aneurysmal rupture were found. Simple stable patterns, large impingement regions, and jet sizes were more commonly seen with unruptured aneurysms. By contrast, ruptured aneurysms were more likely to have disturbed flow patterns, small impingement regions, and narrow jets.

http://www.ajnr.org/content/ajnr/26/10/2550.full.pdf

Characterization of cerebral aneurysms for assessing risk of rupture by using patient-specific computational hemodynamics models.

This paper presents the main objectives and a description of the SU2 suite, including the novel software architecture and open-source software engineering strategy. SU2 is a computational analysis and design package that has been developed to solve multiphysics analysis and optimization tasks using unstructured mesh topologies. Its unique architecture is well suited for extensibility to treat partial-differential-equation-based problems not initially envisioned. The common framework adopted enables the rapid implementation of new physics packages that can be tightly coupled to form a powerful ensemble of analysis tools to address complex problems facing many engineering communities. The framework is demonstrated on a number, solving both the flow and adjoint systems of equations to provide a high-fidelity predictive capability and sensitivity information that can be used for optimal shape design using a gradient-based framework, goal-oriented adaptive mesh refinement, or uncertainty quantification.

SU2: An Open-Source Suite for Multiphysics Simulation and Design

Hemodynamic factors are thought to be implicated in the progression and rupture of intracranial aneurysms. Current efforts aim to study the possible associations of hemodynamic characteristics such as complexity and stability of intra-aneurysmal flow patterns, size and location of the region of flow impingement with the clinical history of aneurysmal rupture. However, there are no reliable methods for measuring blood flow patterns in vivo. In this paper, an efficient methodology for patient-specific modeling and characterization of the hemodynamics in cerebral aneurysms from medical images is described. A sensitivity analysis of the hemodynamic characteristics with respect to variations of several variables over the expected physiologic range of conditions is also presented. This sensitivity analysis shows that although changes in the velocity fields can be observed, the characterization of the intra-aneurysmal flow patterns is not altered when the mean input flow, the flow division, the viscosity model, or mesh resolution are changed. It was also found that the variable that has the greater impact on the computed flow fields is the geometry of the vascular structures. We conclude that with the proposed modeling pipeline clinical studies involving large numbers cerebral aneurysms are feasible.

/pdf/efficient-pipeline-for-image-based-patient-specific-analysis-3imnn9fd8f.pdf

Efficient pipeline for image-based patient-specific analysis of cerebral aneurysm hemodynamics: technique and sensitivity

https://apps.dtic.mil/sti/pdfs/ADA558914.pdf

An Isogeometric design-through-analysis methodology based on adaptive hierarchical refinement of NURBS, immersed boundary methods, and T-spline CAD surfaces

This paper describes the history, objectives, structure, and current capabilities of the Stanford University Unstructured (SU 2 ) tool suite. This computational analysis and design software collection is being developed to solve complex, multi-physics analysis and optimization tasks using arbitrary unstructured meshes, and it has been designed so that it is easily extensible for the solution of Partial Differential Equation-based (PDE) problems not directly envisioned by the authors. At its core, SU 2 is an open-source collection of C++ software tools to discretize and solve problems described by PDEs and is able to solve PDE-constrained optimization problems, including optimal shape design. Although the toolset has been designed with Computational Fluid Dynamics (CFD) and aerodynamic shape optimization in mind, it has also been extended to treat other sets of governing equations including potential flow, electrodynamics, chemically reacting flows, and several others. In our experience, capabilities for computational analysis and optimization have improved considerably over the past two decades. However, the ability to integrate the resulting software packages into coupled multi-physics analysis and design optimization solvers has remained a challenge: the variety of approaches chosen for the independent components of the overall problem (flow solvers, adjoint solvers, optimizers, shape parameterization, shape deformation, mesh adaption, mesh deformation, etc) make it difficult to (a) expand the range of applicability to situations not originally envisioned, and (b) to reduce the overall burden of creating integrated applications. By leveraging well-established object-oriented software architectures (using C++) and by enabling a common interface for all the necessary components, SU 2 is able to remove these barriers for both the beginner and the seasoned analyst. In this paper we attempt to describe our efforts to develop SU 2 as an integrated platform. In some senses, the paper can also be used as a software reference manual for those who might be interested in modifying it to suit their own needs. We carefully describe the C++ framework and object hierarchy, the sets of equations that can be currently modeled by SU 2 , the available choices for numerical discretization, and conclude with a set of relevant validation and verification test cases that are included with the SU 2 distribution. We intend for SU 2 to remain open source and to serve as a starting point for new capabilities not included in SU 2 today, that will hopefully be contributed by users in both academic and industrial environments.

Stanford University Unstructured (SU 2 ): An open-source integrated computational environment for multi-physics simulation and design

Computational fluid dynamics (CFD) model simulations of urban boundary layers have improved in speed and accuracy so that they are useful in assisting in planning emergency response activities related to releases of chemical or biological agents into the atmosphere in large cities such as New York, New York. In this paper, five CFD models [CFD-Urban, Finite Element Flow (FEFLO), Finite Element Model in 3D and Massively-Parallel version (FEM3MP), FLACS, and FLUENT–Environmental Protection Agency (FLUENT-EPA)] have been applied to the same 3D building data and geographic domain in Manhattan, using approximately the same wind input conditions. Wind flow observations are available from the Madison Square Garden 2005 (MSG05) field experiment. Plots of the CFD models' simulations and the observations of near-surface wind fields lead to the qualitative conclusion that the models generally agree with each other and with field observations over most parts of the computational domain, within typical atmosp...

Detailed Simulations of Atmospheric Flow and Dispersion in Downtown Manhattan: An Application of Five Computational Fluid Dynamics Models

Techniques used to implement an unstructured grid solver on modern graphics hardware are described. The three-dimensional Euler equations for inviscid, compressible ow are considered. Eective memory bandwidth is improved by reducing total global memory access and overlapping redundant computation, as well as using an appropriate numbering scheme and data layout. The applicability of per-block shared memory is also considered. The performance of the solver is demonstrated on two benchmark cases: a missile and the NACA0012 wing. For a variety of mesh sizes, an average speed-up factor of roughly 9.5x is observed over the equivalent parallelized OpenMP-code running on a quad-core CPU, and roughly 33x over the equivalent code running in serial.

Running unstructured grid‐based CFD solvers on modern graphics hardware

Adaptive embedded and immersed unstructured grid techniques

Running Unstructured Grid Based CFD Solvers on Modern Graphics Hardware

The development of automatic techniques to port a substantial portion of FEFLO, a general-purpose legacy CFD code operating on unstructured grids, to run on GPUs is described. FEFLO is a typical adaptive, edge-based finite element code for the solution of compressible and incompressible flows, which is primarily written in Fortran 77 and has previously been ported to vector, shared memory parallel and distributed memory parallel machines. Owing to the large size of FEFLO and the likelihood of human error in porting, as well as the desire for continued development within a single codebase, a specialized Python script, based on FParser (Int. J. Comput. Sci. Eng. 2009; 4:296–305), was written to perform automated translation from the OpenMP-parallelized edge and point loops to GPU kernels implemented in CUDA, along with GPU memory management. The results of verification benchmarks and performance indicate that performances achieved by such a translator can rival those of codes rewritten by specialists. The approach should be of general interest, as how best to run on GPUs is being presently considered for many so-called legacy codes. Copyright 2011 John Wiley & Sons, Ltd.

Fernando Camelli

Papers

Detailed Simulations of Atmospheric Flow and Dispersion in Downtown Manhattan: An Application of Five Computational Fluid Dynamics Models

Running unstructured grid‐based CFD solvers on modern graphics hardware

Adaptive embedded and immersed unstructured grid techniques

Running Unstructured Grid Based CFD Solvers on Modern Graphics Hardware

Semi‐automatic porting of a large‐scale Fortran CFD code to GPUs