In-vitro and In-Vivo Assessment of 4D Flow MRI Reynolds Stress Mapping for Pulsatile Blood Flow

Imaging hemodynamics play an important role in the diagnosis of abnormal blood flow due to vascular and valvular diseases as well as in monitoring the recovery of normal blood flow after surgical or interventional treatment. Recently, characterization of turbulent blood flow using 4D flow magnetic resonance imaging (MRI) has been demonstrated by utilizing the changes in signal magnitude depending on intravoxel spin distribution. The imaging sequence was extended with a six-directional icosahedral (ICOSA6) flow-encoding to characterize all elements of the Reynolds stress tensor (RST) in turbulent blood flow. In the present study, we aimed to demonstrate the feasibility of full RST analysis using ICOSA6 4D flow MRI under physiological conditions. First, the turbulence analysis was performed through in vitro experiments with a physiological pulsatile flow condition. Second, a total of 12 normal subjects and one patient with severe aortic stenosis were analyzed using the same sequence. The in-vitro study showed that total turbulent kinetic energy (TKE) was less affected by the signal-to-noise ratio (SNR), however, maximum principal turbulence shear stress (MPTSS) and total turbulence production (TP) had a noise-induced bias. Smaller degree of the bias was observed for TP compared to MPTSS. In-vivo study showed that the subject-variability on turbulence quantification was relatively low for the consistent scan protocol. The in vivo demonstration of the stenosis patient showed that the turbulence analysis could clearly distinguish the difference in all turbulence parameters as they were at least an order of magnitude larger than those from the normal subjects.

Liutex Core Line for Vortex Structure in Turbulence

Liutex is a vortex identification method that provides a vector interpretation of local fluid rotation. Liutex produces a vector quantity which can be used to determine the absolute and relative strength of a vortex, the local rotation axis of a vortex, the vortex core center, the size of the vortex core, and the vortex boundary. Vortex identification and visualization is essential in computational fluid turbulence analysis and fluid mechanics in general. Until Liutex, there has not been a way to identify the core of a vortex structure or even the center of rotation of a vortex structure. Since Liutex, tools have been created to assist in the identification and analysis of vortical structures. The Liutex Core Line has been developed to better understand turbulent fluid structures. A Liutex core is defined as a concentration of Liutex vectors and defined to be unique and the Liutex core line is the center of rotation of that Liutex core. Currently, iso-surfaces are the most popular way to visualize the structure of turbulent flow but there is no reason to believe that it is the best way to represent a vortex’s structure. Previous methods that use iso-surface are strongly threshold dependent and since the Liutex core line is unique, it is independent of threshold and can show the real vortex structure. In this paper we show the benefits and promises of the Liutex Core Line as a better way of representing vortex structures.

Distinct Turbulent Regions in the Wake of a Wind Turbine and Their Inflow-Dependent Locations: The Creation of a Wake Map

Wind turbines are usually clustered in wind farms which causes the downstream turbines to operate in the turbulent wakes of upstream turbines. As turbulence is directly related to increased fatigue loads, knowledge of the turbulence in the wake and its evolution are important. Therefore, the main objective of this study is a comprehensive exploration of the turbulence evolution in the wind turbine’s wake to identify characteristic turbulence regions. For this, we present an experimental study of three model wind turbine wake scenarios that were scanned with hot-wire anemometry with a very high downstream resolution. The model wind turbine was exposed to three inflows: laminar inflow as a reference case, a central wind turbine wake, and half of the wake of an upstream turbine. A detailed turbulence analysis reveals four downstream turbulence regions by means of the mean velocity, variance, turbulence intensity, energy spectra, integral and Taylor length scales, and the Castaing parameter that indicates the intermittency, or gustiness, of turbulence. In addition, a wake core with features of homogeneous isotropic turbulence and a ring of high intermittency surrounding the wake can be identified. The results are important for turbulence modeling in wakes and optimization of wind farm wake control.