Transition to turbulence in an oscillatory flow through stenosis

Accelerated simulation methodologies for computational vascular flow modelling

Vascular flow modelling can improve our understanding of vascular pathologies and aid in developing safe and effective medical devices. Vascular flow models typically involve solving the nonlinear Navier-Stokes equations in complex anatomies and using physiological boundary conditions, often presenting a multi-physics and multi-scale computational problem to be solved. This leads to highly complex and expensive models that require excessive computational time. This review explores accelerated simulation methodologies, specifically focusing on computational vascular flow modelling. We review reduced order modelling (ROM) techniques like zero-/one-dimensional and modal decomposition-based ROMs and machine learning (ML) methods including ML-augmented ROMs, ML-based ROMs and physics-informed ML models. We discuss the applicability of each method to vascular flow acceleration and the effectiveness of the method in addressing domain-specific challenges. When available, we provide statistics on accuracy and speed-up factors for various applications related to vascular flow simulation acceleration. Our findings indicate that each type of model has strengths and limitations depending on the context. To accelerate real-world vascular flow problems, we propose future research on developing multi-scale acceleration methods capable of handling the significant geometric variability inherent to such problems.

Microarchitectural mimicking of stroma-induced vasculature compression in pancreatic tumors using a 3D engineered model

Fibrotic tumors, such as pancreatic ductal adenocarcinoma (PDAC), are characterized for high desmoplastic reaction, which results in high intra-tumoral solid stress leading to the compression of blood vessels. These microarchitectural alterations cause loss of blood flow and poor intra-tumoral delivery of therapeutics. Currently, there is a lack of relevant in vitro models capable of replicating these mechanical characteristics and to test anti-desmoplastic compounds. Here, a multi-layered vascularized 3D PDAC model consisting of primary human pancreatic stellate cells (PSCs) embedded in collagen/fibrinogen (Col/Fib), mimicking tumor tissue within adjunct healthy tissue, is presented to study the fibrosis-induced compression of vasculature in PDAC. It is demonstrated how the mechanical and biological stimulation induce PSC activation, extracellular matrix production and eventually vessel compression. The clinical relevance is confirmed by correlating with patient transcriptomic data. Furthermore, the effects of gradual vessel compression on the fluid dynamics occurring within the channel is evaluated in silico. Finally, it is demonstrated how cancer-associated fibroblast (CAF)-modulatory therapeutics can inhibit the cell-mediated compression of blood vessels in PDAC in vitro, in silico and in vivo. It is envisioned that this 3D model is used to improve the understanding of mechanical characteristics in tumors and for evaluating novel anti-desmoplastic therapeutics.

Computational analysis of human upper airway aerodynamics

There is a considerable interest in understanding transient human upper airway aerodynamics, especially in view of assessing the effects of various ventilation therapies. Experimental analyses in a patient-specific manner pose challenges as the upper airway consists of a narrow confined region with complex anatomy. Pressure measurements are feasible, but, for example, PIV experiments require special measures to accommodate for the light refraction by the model. Computational fluid dynamics can bridge the gap between limited experimental data and detailed flow features. This work aims to validate the use of combined lattice Boltzmann method and a large eddy scale model for simulating respiration, and to identify clinical features of the flow and show the clinical potential of the method. Airflow was computationally analyzed during a realistic, transient, breathing profile in an upper airway geometry ranging from nose to trachea, and the resulting pressure calculations were compared against in vitro experiments. Simulations were conducted on meshes containing about 1 billion cells to ensure accuracy and to capture intrinsic flow features. Airway pressures obtained from simulations and in vitro experiments are in good agreement both during inhalation and exhalation. High velocity pharyngeal and laryngeal jets and recirculation in the region of the olfactory cleft are observed. Graphical Abstract The Lattice-Boltzmann Method combined with Large Eddy Simulations was used to compute the aerodynamics in a human upper airway geometry. The left side of this graphical abstract shows the velocity and vorticity (middle figure in bottom row, and right figure of the right bottom figure) profiles at peak exhalation. The simulations were validated against experiments on a 3D-print of the geometry (shown in the top figures on the right hand side). The pressure drop (right bottom corner) shows a good agreement between experiments and simulations.

The effect of varying degrees of stenosis on transition to turbulence in oscillatory flows

Many complications in physiology are associated with a deviation in flow in arteries due to a stenosis. The presence of stenosis may transition the flow to weak turbulence. The degree of stenosis as well as its configuration whether symmetric or non-symmetric to the parent artery influences whether the flow would stay laminar or transition to turbulence. Plenty of research efforts focus on investigating the role of varying degrees of stenosis in the onset of turbulence under steady and pulsatile flow conditions. None of the studies, however, have focused on investigating this under oscillatory flow conditions as flow reversal is a major occurrence in a number of physiologic flows, and is of particular relevance in cerebrospinal fluid flow research. Following up on the previous work in which a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$75\%$$\end{document}75% stenosis was studied, this contribution is a detailed investigation of the role of degrees of stenosis on transition in an oscillatory flow. A cylindrical pipe with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$25\%$$\end{document}25%, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$50\%$$\end{document}50% and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$60\%$$\end{document}60% reductions in area in axisymmetric and eccentric configurations is studied for transition with 3 different pulsation frequencies of a purely oscillatory flow. Cycle averaged Reynolds numbers between 1800 and 2100 in steps of 100 are studied for each configuration resulting in 72 simulations each conducted on 76,800 CPU cores of a modern supercomputer. It is found that a higher degree of stenosis and eccentricity causes earlier transition to turbulence in oscillatory flow. The results further demonstrate that a higher frequency of oscillation results in larger hydrodynamic instability in the flow, which is more prominent in smaller degrees of stenosis.

Fluid-Structure Interaction in Coronary Stents: A Discrete Multiphysics Approach

Stenting is a common method for treating atherosclerosis. A metal or polymer stent is deployed to open the stenosed artery or vein. After the stent is deployed, the blood flow dynamics influence the mechanics by compressing and expanding the structure. If the stent does not respond properly to the resulting stress, vascular wall injury or re-stenosis can occur. In this work, a Discrete Multiphysics modelling approach is used to study the mechanical deformation of the coronary stent and its relationship with the blood flow dynamics. The major parameters responsible for deforming the stent are sorted in terms of dimensionless numbers and a relationship between the elastic forces in the stent and pressure forces in the fluid is established. The blood flow and the stiffness of the stent material contribute significantly to the stent deformation and affect its rate of deformation. The stress distribution in the stent is not uniform with the higher stresses occurring at the nodes of the structure. From the relationship (correlation) between the elastic force and the pressure force, depending on the type of material used for the stent, the model can be used to predict whether the stent is at risk of fracture or not after deployment.

The pressure characteristics analysis of oil pulsation flow based on VMD

The pressure signal of oil pulsating flow is a kind of multi-component signal; in order to realise the effective separation of the multi-component pressure signal and extract its vibration characteristics, the pressure signal was decomposed by Variational Mode Decomposition (VMD). The slope criterion of the centre frequency is proposed to determine the number of components of VMD decomposition, and the method to judge the main components of the signal by energy value is proposed. The Hilbert envelope demodulation analysis was performed on the main components obtained. The results show that the proposed center frequency slope criterion method is effective in the VMD decomposition of the pressure signal of oil pulsating flow, which is used to decompose the pressure signal into 9 components. Four major components of the pressure signal are obtained by the correlation between each component and the pressure signal, and the energy value calculation of each component. The main component frequency of the pressure signal is one time, 6 times, 11 times and 14 times the frequency of the system spindle rotation; these are the sum of two cosine signals of close frequency and have the characteristic of beat vibration.

Efficacy of the FDA nozzle benchmark and the lattice Boltzmann method for the analysis of biomedical flows in transitional regime

Flows through medical devices as well as in anatomical vessels despite being at moderate Reynolds number may exhibit transitional or even turbulent character. In order to validate numerical methods and codes used for biomedical flow computations, the US Food and Drug Administration (FDA) established an experimental benchmark, which was a pipe with gradual contraction and sudden expansion representing a nozzle. The experimental results for various Reynolds numbers ranging from 500 to 6500 were publicly released. Previous and recent computational investigations of flow in the FDA nozzle found limitations in various CFD approaches and some even questioned the adequacy of the benchmark itself. This communication reports the results of a lattice Boltzmann method (LBM) – based direct numerical simulation (DNS) approach applied to the FDA nozzle benchmark for transitional cases of Reynolds numbers 2000 and 3500. The goal is to evaluate if a simple off the shelf LBM would predict the experimental results without the use of complex models or synthetic turbulence at the inflow. LBM computations with various spatial and temporal resolutions are performed—in the extremities of 45 million to 2.88 billion lattice cells—executed respectively on 32 CPU cores of a desktop to more than 300,000 cores of a modern supercomputer to explore and characterize miniscule flow details and quantify Kolmogorov scales. The LBM simulations transition to turbulence at a Reynolds number 2000 like the FDA’s experiments and acceptable agreement in jet breakdown locations, average velocity, shear stress, and pressure is found for both the Reynolds numbers.

Graphical Abstract