Browsing by Subject "Computational Fluid Dynamics"

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    Correlation of Cloud Based Computational Fluid Dynamics Simulations to Wind Tunnel Test Results for a NASCAR XFINITY Series Vehicle
    (2018-04-25) Catranis, Daniel; Borme, Andrew; Goodman, David W.; Weissbach, Robert S.
    The cost of setting up and maintaining a high performance computing cluster for large scale CFD usage is too expensive for many smaller motorsport organizations, and so the turn to cloud based computing resources is an attractive one. Cloud based computing centers allow users access to a shared computing cluster and charge based on the amount of resources used by each account. Efficient use of a cloud based computing center necessitates optimizing the CFD simulations to maximize accuracy and minimize cost due to the charge structure in place. This paper attempts to optimize steady state RANS simulations through systematically altering the refinement settings within the simulation mesh. These simulations are conducted using OpenFOAM on two NASCAR XFINITY Series vehicles and are validated using wind tunnel data. The effects of mesh refinement near the surface of the model and the refinement level within a bounding box around the vehicle on the aerodynamic forces of the vehicle are studied and related to the cost of running each simulation. A more computationally intensive transient simulation was also conducted and was not found to have a significant influence on the accuracy of the results beyond that of the steady state simulations.
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    Image Segmentation, Parametric Study, and Supervised Surrogate Modeling of Image-based Computational Fluid Dynamics
    (2022-05) Islam, Md Mahfuzul; Yu, Huidan (Whitney); Du, Xiaoping; Wagner, Diane
    With the recent advancement of computation and imaging technology, Image-based computational fluid dynamics (ICFD) has emerged as a great non-invasive capability to study biomedical flows. These modern technologies increase the potential of computation-aided diagnostics and therapeutics in a patient-specific environment. I studied three components of this image-based computational fluid dynamics process in this work. To ensure accurate medical assessment, realistic computational analysis is needed, for which patient-specific image segmentation of the diseased vessel is of paramount importance. In this work, image segmentation of several human arteries, veins, capillaries, and organs was conducted to use them for further hemodynamic simulations. To accomplish these, several open-source and commercial software packages were implemented. This study incorporates a new computational platform, called InVascular, to quantify the 4D velocity field in image-based pulsatile flows using the Volumetric Lattice Boltzmann Method (VLBM). We also conducted several parametric studies on an idealized case of a 3-D pipe with the dimensions of a human renal artery. We investigated the relationship between stenosis severity and Resistive index (RI). We also explored how pulsatile parameters like heart rate or pulsatile pressure gradient affect RI. As the process of ICFD analysis is based on imaging and other hemodynamic data, it is often time-consuming due to the extensive data processing time. For clinicians to make fast medical decisions regarding their patients, we need rapid and accurate ICFD results. To achieve that, we also developed surrogate models to show the potential of supervised machine learning methods in constructing efficient and precise surrogate models for Hagen-Poiseuille and Womersley flows.