Bulletin of the American Physical Society
72nd Annual Meeting of the APS Division of Fluid Dynamics
Volume 64, Number 13
Saturday–Tuesday, November 23–26, 2019; Seattle, Washington
Session C30: Biological Fluid Dynamics : Cardiovascular Flows |
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Chair: Jian-Xun Wang, University of Notre Dame Room: 612 |
Sunday, November 24, 2019 8:00AM - 8:13AM |
C30.00001: Multiscale modeling of the cardiovascular deconditioning during spaceflight Stefania Scarsoglio, Caterina Gallo, Luca Ridolfi Permanence in microgravity characterizing long-term spaceflights causes a general deconditioning of the cardiovascular system. A constellation of hemodynamic mechanisms - such as fluid shift, blood volume reduction, vessel elasticity changes, and cardiac atrophy - concurs to define a 0G adaptation point, which is identified by an overall relaxation of the cardiovascular system. This scenario promotes orthostatic intolerance on reentry and poses open questions on re-adaptation for spaceflights beyond 1 year. We here present a computational approach to compare the cardiovascular response in supine position on Earth and at 0G adaptation point during spaceflight. The proposed aim is twofold: (i) understand the underlying mechanisms leading to cardiovascular deconditioning; (ii) describe the hemodynamics of districts for which clinical data are not always feasible and accurate even on Earth. The present approach relies on a validated multiscale modeling of the cardiovascular system combining a 1D description of the arterial tree together with a lumped parameterization of the remaining regions (i.e., venous return, heart chambers, pulmonary circulation, baroreceptor regulation). [Preview Abstract] |
Sunday, November 24, 2019 8:13AM - 8:26AM |
C30.00002: Rich Dynamic Behaviors of Self-excited Oscillation of Collapsible Channel Qiuxiang Huang, Fang-Bao Tian, John Young, Joseph C. S. Lai Fluid-structure interaction (FSI) in collapsible channel flow is numerically studied with an immersed boundary-lattice Boltzmann method. Compared with previous studies, current method is able to simulate nonlinear fully coupled FSI in two-sided collapsible channel and high Reynolds numbers flow (Re up to 2000). The stability of the hydrodynamic flow and collapsible channel walls are examined for a wide range of Reynolds numbers, structure-to-fluid mass ratio, external pressure and wall thickness. Based on the numerical simulations, we (i) explore the physical mechanisms responsible for the onset of self-excited oscillations, and (ii) characterise the chaotic behavior of the collapsible channel walls. Rich dynamic behaviors of self-excited oscillation are observed. Regarding point (i), we identify that the flow bifurcate to bistable mode at Re=320 due to the symmetry breaking as the increase of Reynolds number. Besides, the external pressure applied on the elastic beams plays an important role in triggering the self-excited oscillation of the beam. And then for point (ii), the existence of chaotic behavior of the collapsible channel walls is confirmed by a very positive dominant Lyapunove exponent and the chaotic attractor in the velocity-displacement phase portrait. [Preview Abstract] |
Sunday, November 24, 2019 8:26AM - 8:39AM |
C30.00003: Optogenetics-Biofluid Model for Engineered Beating Cardiomyocytes using Meshfree Method Yasser Aboelkassem Optogenetic techniques make use of genetically encoded, light sensitive ion channels to manipulate cellular function with light. More recently, microbial opsins such as the light-gated ion channel channelrhodopsin-2 (ChR2) have been transfected into cardiomyocytes, allowing cardiac muscle contractions to be initiated by pulses of light. Cardiac optogenetics raises numerous interesting possibilities, including optical pacemakers, defibrillators, and flow pumping assistant devices accomplished through the precise spatiotemporal application of light excitations. In this study, an optogenetics-fluid mathematical model is proposed to study the flow motions induced by a single engineered cardiac cell. The optogenetics module is based on Monte Carlo type of simulations to control light stimulus events, which are then used to probe the cellular wall contractions. The fluid module is derived based on a two-dimensional meshfree-Stokeslets computational framework. The results show that, cells with a slightly different beating profile can induce different flow field that is characterized by coherent vortices with different strengths and core sizes. This implies that, each cell induces unique flow biomarker ``signature'', which can be used to better understand the intrinsic sub-cellular excitation-contraction processes of cardiac cells. [Preview Abstract] |
Sunday, November 24, 2019 8:39AM - 8:52AM |
C30.00004: A Multi-fidelity Ensemble Kalman Method for Inverse Problems in Cardiovascular Flows Han Gao, Jian-Xun Wang In cardiovascular modeling, parameters associated with boundary conditions and mechanical properties are often unknown or uncertain, which can be calibrated using indirect and/or sparse clinical measurements based on data assimilation (DA) techniques. The ensemble Kalman filter (EnKF), as a derivative-free DA approach, has started to gain popularity for solving inverse problems in physiological modeling. However, the computational cost of the EnKF could be considerably high due to a large ensemble of costly forward simulations, in particular when the iterative Kalman updates are needed for nonlinear inversion (i.e., iterative ensemble Kalman method). In this work, we propose an efficient multi-fidelity ensemble Kalman inversion approach, where both the low- and high-fidelity forward models are utilized to accelerate the statistical convergence. Moreover, to improve the identifiability of the parameters to be inferred, additional physical/physiological constraints will be imposed by re-weighting the ensemble members in a Bayesian manner. Numerical examples of vascular flows in patient-specific geometries are presented to demonstrate the effectiveness and merit of the proposed framework. [Preview Abstract] |
Sunday, November 24, 2019 8:52AM - 9:05AM |
C30.00005: Super-resolution and Denoising of Flow MRI Data using Physics-Constrained Deep Learning Luning Sun, Jian-Xun Wang The recent advances in the flow magnetic resonance (MR) imaging enable \emph{noninvasive} and \emph{in vivo} measurement of the blood flow velocity field. However, the resolution and signal-to-noise ratio (SNR) of flow MR images still remain the limiting factors in clinical practice. This is especially true when investigating small vascular structures such as intracranial aneurysms or treating near-wall regions where wall shear stress is calculated. Therefore, super-resolution and denoising of flow fields from MR images are of great importance and remain active research areas. In this work, we propose an innovative deep learning framework to upscale low-resolution flow fields and to reduce the measurement noise using physics-constrained deep neural networks (DNN). Specifically, a generative DNN will be trained on the low-resolution data to capture the flow field. In the meantime, the violation of physical laws will be penalized on a large number of spatiotemporal points where measurements are not available and noises are expected to be reduced. The trained generative model can reconstruct the flow field with arbitrarily high resolution. Several test cases with synthetic vascular flow data are studied to demonstrate the merit of the proposed method. [Preview Abstract] |
Sunday, November 24, 2019 9:05AM - 9:18AM |
C30.00006: Comparison of Multi-scale Models for Blood Flow in Zebrafish Brain Minglang Yin, Xiaoning Zheng, Ansel Blumers, Hiroyuki Nakajima, Yosuke Hasegawa, George Karniadakis The contribution of hemodynamics in developing zebrafish vasculature has long been recognized as one of the main factors in the mechanisms of vessel pruning. Using the modern computational fluid dynamics models, such as the three-dimensional(3D) Navier-Stokes model, the one-dimensional(1D) blood flow model, or the Dissipative Particle Dynamics(DPD), we performed the first detailed simulations to investigate the hemodynamics in zebrafish hindbrain. The simulations were performed on the same zebrafish hindbrain vasculature with the same Dirichlet boundary condition at its inlets. The flow rate and pressure profiles at outlets and inner points show a good agreement between the 1D and the other two models. This validates the 1D model accuracy in simulating blood flow at low Reynolds. Further investigations on non-Newtonian effect are ongoing. The performance of the 1D model facilitates its applications to further investigations on transport properties in physiological processes such as angiogenesis in zebrafish vasculature, mouse retinal plexus, or even a tumor. [Preview Abstract] |
Sunday, November 24, 2019 9:18AM - 9:31AM |
C30.00007: From whole-organ imaging to in-silico blood flow modeling : a new multi-scale network analysis for revisiting tissue functional anatomy Franck Plouraboue We present a multi-disciplinary image-based blood flow perfusion modeling of a whole organ vascular network for analyzing both its structural and functional properties. We show how the use of Light-Sheet Fluorescence Microscopy (LSFM) permits whole organ micro-vascular imaging, analysis and modelling. By using adapted image post-treatments workflow, we could segment, vectorize and reconstruct the entire micro-vascular network composed of 1.7 millions vessels, from the tissue-scale, inside a $\sim 25 \times 5 \times 1=125$mm$^3$ volume of mouse fat pad, hundred time larger than previous studies, down to the cellular scale at micron resolution, with the entire blood perfusion is modeled. Adapted network analysis revealed the structural and functional organization of meso-scale tissue as strongly connected communities of vessels. These communities share out a distinct heterogeneous core region and a more homogeneous peripheral region, consistently with known biological functions of fat tissue. Graph clustering analysis also revealed two distinct robust meso-scale typical sizes (from 10 to several hundred times the cellular size), revealing, for the first time, strongly connected functional vascular communities. These communities networks support [Preview Abstract] |
Sunday, November 24, 2019 9:31AM - 9:44AM |
C30.00008: Reduced-order modeling of flow in the circulatory system Chenwei Meng, Mahdi Esmaily Lumped parameter networks (LPN) coupled with 3D CFD simulations provide a mean to simulate the flow in the circulatory system at a reasonable accuracy and affordable cost. In this method, the flow in major vessels is fully resolved, whereas it is modeled in the rest of the circulatory system using lumped components. Despite these attractive properties, the usage of 3D-LPN coupling method is hindered by need for reverse-engineering the LPN components through a tuning process, which itself requires performing many CFD simulations. To reduce the cost of such expensive calculations, a reduced-order model is introduced to replace the 3D model that is orders of magnitude less expensive to solve. The introduced reduced-order model is constructed using a circuit network analogy that can capture flow inertial and viscous losses in the 3D fluid domain. Additionally, we show that the reduced-order model can be constructed directly through the Jacobian matrix in the CFD solver. Reasonable accuracy and computational efficiency are demonstrated by comparing the reduced-order model result with the reference CFD result. [Preview Abstract] |
Sunday, November 24, 2019 9:44AM - 9:57AM |
C30.00009: Characterization of Flow Mediated Dilation via a Physics-Based Model Bchara Sidnawi, Zhen Chen, Chandra Sehgal, Srihdar Santhanam, Qianhong Wu In this work, a preliminary physics-based model describing the transient behavior of the brachial artery during the Flow Mediated Dilation (FMD) test, is developed. Experimental diameter vs. time data were collected, via in-vivo ultrasound imaging. The model, which also accounts for mechano-transduction, was able to capture a key feature of the experimentally observed responses which a conventional viscoelastic model fails to explain. Characteristic dimensionless groups quantifying the physical state of the artery emerged from the model. The values of these dimensionless quantities, that predicted a response that best matched the experimental counterpart corresponding to a specific artery, were considered the values characterizing it. The meaning of these parameters and how they can be related to the cardiovascular health are discussed and explained. The transient physics manifested in the two-way Fluid-Structure Interaction (FSI) driving the FMD process, present an interesting opportunity to explore the nature of living materials making up the arterial walls, which would in turn lead to a better understanding and therefore detection of the onset of some forms of Cardiovascular Disease (CVD). [Preview Abstract] |
Sunday, November 24, 2019 9:57AM - 10:10AM |
C30.00010: Towards Blood Flow Velocimetry with X-Ray CT Brendan Colvert, Eric Yu, Francisco Contijoch, Elliot McVeigh Cardiovascular disease (CVD) is a tremendous burden in terms of morbidity, mortality, and costs to the healthcare system. Various forms of CVD including atherosclerosis, valve and ventricular dysfunction, aneurysms, and thrombogenesis are associated with localized blood flow abnormalities. Accordingly, the ability to noninvasively interrogate physiological flows enables identification and diagnosis of disease, monitoring of therapies, and research on the hemodynamics of CVD. In the clinic, blood flow measurements are primarily made using phase contrast magnetic resonance imaging (PC-MRI) and ultrasonic color Doppler imaging. Certain limitations of these techniques for patients who have contraindications or suffer from arrhythmias, as well as the desire for volumetric flow information necessitate the development of a new modality for blood flow velocimetry. In this work, we propose a strategy to optimally integrate imaging data from contrast-enhanced X-ray CT scans with flow solvers. We evaluate the effectiveness of this strategy in the context of a simplified flow model. Our initial findings provide insight into the theoretical foundations of the proposed technique and lay the groundwork for further research on the use of X-ray CT for blood flow velocimetry. [Preview Abstract] |
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