scholarly journals Fluid mechanics of human fetal right ventricles from image-based computational fluid dynamics using 4D clinical ultrasound scans

2016 ◽  
Vol 311 (6) ◽  
pp. H1498-H1508 ◽  
Author(s):  
Hadi Wiputra ◽  
Chang Quan Lai ◽  
Guat Ling Lim ◽  
Joel Jia Wei Heng ◽  
Lan Guo ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Kinetic Energy ◽  
Fluid Mechanics ◽  
Animal Studies ◽  
Shear Stresses ◽  
Complex Flow ◽  
Congenital Heart Malformations ◽  
Wall Shear ◽  
Dynamics Simulations

There are 0.6–1.9% of US children who were born with congenital heart malformations. Clinical and animal studies suggest that abnormal blood flow forces might play a role in causing these malformation, highlighting the importance of understanding the fetal cardiovascular fluid mechanics. We performed computational fluid dynamics simulations of the right ventricles, based on four-dimensional ultrasound scans of three 20-wk-old normal human fetuses, to characterize their flow and energy dynamics. Peak intraventricular pressure gradients were found to be 0.2–0.9 mmHg during systole, and 0.1–0.2 mmHg during diastole. Diastolic wall shear stresses were found to be around 1 Pa, which could elevate to 2–4 Pa during systole in the outflow tract. Fetal right ventricles have complex flow patterns featuring two interacting diastolic vortex rings, formed during diastolic E wave and A wave. These rings persisted through the end of systole and elevated wall shear stresses in their proximity. They were observed to conserve ∼25.0% of peak diastolic kinetic energy to be carried over into the subsequent systole. However, this carried-over kinetic energy did not significantly alter the work done by the heart for ejection. Thus, while diastolic vortexes played a significant role in determining spatial patterns and magnitudes of diastolic wall shear stresses, they did not have significant influence on systolic ejection. Our results can serve as a baseline for future comparison with diseased hearts.

Computational fluid dynamics simulations of cerebral aneurysm using Newtonian, power-law and quasi-mechanistic blood viscosity models

2020 ◽  
Vol 234 (7) ◽  
pp. 711-719 ◽  
Author(s):  
Khalid M Saqr
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Shear Stress ◽  
Wall Shear Stress ◽  
Power Law ◽  
The Other ◽  
Lower Wall ◽  
Computational Fluid Dynamics Simulations ◽  
Wall Shear ◽  
Dynamics Simulations

Cerebral aneurysm is a fatal neurovascular disorder. Computational fluid dynamics simulation of aneurysm haemodynamics is one of the most important research tools which provide increasing potential for clinical applications. However, computational fluid dynamics modelling of such delicate neurovascular disorder involves physical complexities that cannot be easily simplified. Recently, it was shown that the Newtonian simplification used to close the shear stress tensor of the Navier–Stokes equation is not sufficient to explore aneurysm haemodynamics. This article explores the differences between the latter simplification, non-Newtonian power-law model and a newly proposed quasi-mechanistic model. The modified Krieger model, which treats blood as a suspension of plasma and particles, was implemented in computational fluid dynamics context here for the first time and is made available to the readers in a C# code in the supplementary material of this article. Two middle-cerebral artery and two anterior-communicating artery aneurysms, all ruptured, were utilized here as case studies. It was shown that the modified Krieger model had higher sensitivity for wall shear stress calculations in comparison with the other two models. The modified Krieger model yielded lower wall shear stress values consistently in comparison with the other two models. Moreover, the modified Krieger model has generally predicted higher pressure in the aneurysm models. Based on published aneurysm rupture studies, it is believed that ruptured aneurysms are usually correlated with lower wall shear stress values than unruptured ones. Therefore, this work concludes that the modified Krieger model is a potential candidate for providing better clinical relevance to aneurysm computational fluid dynamics simulations.

Erosion Evaluations of a Slurry Mixer Tank With Computational Fluid Dynamics Methods

2006 ◽  
Author(s):  
Si Y. Lee ◽  
Richard A. Dimenna ◽  
Glenn A. Taylor
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Leading Edge ◽  
Working Fluid ◽  
Shear Stresses ◽  
Internal Structures ◽  
Wall Shear ◽  
Erosion Loss ◽  
Solids Content ◽  
Cfd Analyses

This paper discusses the use of computational fluid dynamics (CFD) methods to understand and characterize erosion of the floor and internal structures in the slurry mixing vessels in the Defense Waste Processing Facility. An initial literature survey helped identify the principal drivers of erosion for a solids laden fluid: the solids content of the working fluid, the regions of recirculation and particle impact with the walls, and the regions of high wall shear. A series of CFD analyses was performed to characterize slurry-flow profiles, wall shear, and particle impingement distributions in key components such as coil restraints and the vessel floor. The calculations showed that the primary locations of high erosion resulting from abrasion were at the leading edge of the coil guide, the tank floor below the insert plate of the coil guide support, and the upstream lead-in plate. These modeling results based on the calculated high shear regions were in excellent agreement with the observed erosion sites in both location and the degree of erosion. Loss of the leading edge of the coil guide due to the erosion damage during the slurry mixing operation did not affect the erosion patterns on the tank floor. Calculations for a lower impeller speed showed similar erosion patterns but significantly reduced wall shear stresses.

Using FlowLab, A Computational Fluid Dynamics Tool, to Facilitate the Teaching of Fluid Mechanics

2004 ◽  
Author(s):  
John Cimbala ◽  
Shane Moeykens ◽  
Ashish Kulkarni ◽  
Ajay Parihar
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Fluid Mechanics ◽  
Learning Experience ◽  
Learning Objective ◽  
Structured Learning ◽  
Complex Flow ◽  
Homework Problem ◽  
Flow Phenomena ◽  
Computer Based

Traditional fluid mechanics textbooks are generally written with problem sets comprised of closed, analytical solutions. However, it is recognized that complex flow fields are not easily represented in terms of a closed solution. A tool that allows the student to visualize complex flow phenomena in a virtual environment can significantly enhance the learning experience. Such a visualization tool allows the student to perform open-ended analyses and explore cause-effect relationships. Computational fluid dynamics (CFD) brings these benefits into the learning environment for fluid mechanics. With these benefits in mind, FlowLab was introduced by Fluent Inc. in 2002. FlowLab may be described as a virtual fluids laboratory - a computer-based analysis and visualization package. Using this software, students solve predefined CFD exercises, either as homework or in a supervised laboratory or practicum setting. Predefined exercises facilitate the teaching of fluid mechanics and provide students with hands-on CFD experience, while avoiding many of the difficulties associated with learning a generalized CFD package. A new fluid mechanics textbook is scheduled for release in early 2005. This book includes FlowLab as a textbook companion, where student-friendly CFD exercises are employed to convey important concepts to the student. Because of the unique design of end-of-chapter homework problems in this book and the intimate coupling between these problems and the CFD software, students are introduced to engineering problems and concepts, as well as to CFD, via a structured learning process. The CFD exercises are not meant to stand alone; rather, they are designed to support and emphasize the theory and concepts taught in the textbook, which is the primary learning vehicle. Each homework problem has a specific fluid mechanics learning objective. Through use of the software, a second learning objective is also achieved, namely a CFD objective. The scope, content, and presentation of these CFD exercises are discussed in this paper. Additionally, one of the exercises is explained in detail to show the value of using CFD to teach introductory fluid mechanics to undergraduate engineers.

Computational Fluid Dynamics Simulations of Windage Loss in a Spur Gear

2018 ◽  
Author(s):  
Murat K. Aktas ◽  
Mehmet A. Yavuz ◽  
Ali K. Ersan
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Single Phase ◽  
Air Flow ◽  
Theoretical Data ◽  
Spur Gear ◽  
Numerical Approach ◽  
Complex Flow ◽  
Theoretical Investigations ◽  
Dynamics Simulations

Windage losses arise due to the viscous dissipation of air -oil mist flow driven by the rotating faces, sides of the gears in a gearbox. The windage losses can consume up to 3% of the transmitted power. In the open literature, theoretical investigations mostly focused on the single phase (air) flow. In the present study we attempt to elucidate the complex flow physics between the gears by considering the effects of oil particles in the air flow. The goals of this work is to develop a physical understanding of the aerodynamics of gear windage loss. A spur gear geometry was considered, for which experimental and theoretical data are available. A commercial Computational Fluid Dynamics algorithm (CFD++) was utilized for the numerical simulations. The first group of simulations were performed for a single phase (air) flow in order to validate the numerical approach. The results showed good agreement with the experiment. Various shrouding configurations and free spinning cases are studied at different rotation speeds of the gear. The effects of the oil droplets on the windage losses were quantified.

scholarly journals Morphological and Hemodynamic Changes during Cerebral Aneurysm Growth

2021 ◽  
Vol 11 (4) ◽  
pp. 520
Author(s):  
Emily R. Nordahl ◽  
Susheil Uthamaraj ◽  
Kendall D. Dennis ◽  
Alena Sejkorová ◽  
Aleš Hejčl ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Kinetic Energy ◽  
Swirling Flow ◽  
Morphological Changes ◽  
Cerebral Aneurysms ◽  
Patient Specific ◽  
Aneurysm Wall ◽  
Aneurysm Growth ◽  
Computational Fluid Dynamics Cfd

Computational fluid dynamics (CFD) has grown as a tool to help understand the hemodynamic properties related to the rupture of cerebral aneurysms. Few of these studies deal specifically with aneurysm growth and most only use a single time instance within the aneurysm growth history. The present retrospective study investigated four patient-specific aneurysms, once at initial diagnosis and then at follow-up, to analyze hemodynamic and morphological changes. Aneurysm geometries were segmented via the medical image processing software Mimics. The geometries were meshed and a computational fluid dynamics (CFD) analysis was performed using ANSYS. Results showed that major geometry bulk growth occurred in areas of low wall shear stress (WSS). Wall shape remodeling near neck impingement regions occurred in areas with large gradients of WSS and oscillatory shear index. This study found that growth occurred in areas where low WSS was accompanied by high velocity gradients between the aneurysm wall and large swirling flow structures. A new finding was that all cases showed an increase in kinetic energy from the first time point to the second, and this change in kinetic energy seems correlated to the change in aneurysm volume.

scholarly journals Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Azam Marjani ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Kinetic Energy ◽  
Population Size ◽  
Turbulent Kinetic Energy ◽  
Forced Convection ◽  
Inlet Temperature ◽  
Fluid Temperature ◽  
Interface System

AbstractComputational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.

scholarly journals Aerodynamic analysis of uphill drafting in cycling

2021 ◽  
Vol 24 (1) ◽  
Author(s):  
T. van Druenen ◽  
B. Blocken
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
High Speed ◽  
Scientific Literature ◽  
Sensitivity Analyses ◽  
Air Resistance ◽  
Wind Tunnel Measurements ◽  
Aerodynamic Effect ◽  
Computational Fluid Dynamics Simulations ◽  
Dynamics Simulations

AbstractSome teams aiming for victory in a mountain stage in cycling take control in the uphill sections of the stage. While drafting, the team imposes a high speed at the front of the peloton defending their team leader from opponent’s attacks. Drafting is a well-known strategy on flat or descending sections and has been studied before in this context. However, there are no systematic and extensive studies in the scientific literature on the aerodynamic effect of uphill drafting. Some studies even suggested that for gradients above 7.2% the speeds drop to 17 km/h and the air resistance can be neglected. In this paper, uphill drafting is analyzed and quantified by means of drag reductions and power reductions obtained by computational fluid dynamics simulations validated with wind tunnel measurements. It is shown that even for gradients above 7.2%, drafting can yield substantial benefits. Drafting allows cyclists to save over 7% of power on a slope of 7.5% at a speed of 6 m/s. At a speed of 8 m/s, this reduction can exceed 16%. Sensitivity analyses indicate that significant power savings can be achieved, also with varying bicycle, cyclist, road and environmental characteristics.

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