The Fluid Dynamics of Heart Development: The effect of morphology on flow at several stages

Proper cardiogenesis requires a delicate balance between genetic and environmental (epigenetic) signals, and mechanical forces. While many cellular biologists and geneticists have extensively studied heart morphogenesis using various experimental techniques, only a few scientists have begun using mathematical modeling as a tool for studying cardiogenic events. Hemodynamic processes, such as vortex formation, are important in the generation of shear at the endothelial surface layer and strains at the epithelial layer, which aid in proper morphology and functionality. The purpose of this thesis is to study the underlying fluid dynamics in various stages on heart development, in particular, the morphogenic stages when the heart is a linear heart tube as well as during the onset of ventricular trabeculation. Previous mathematical models of the linear heart tube stage have focused on mechanisms of valveless pumping, whether dynamic suction pumping (impedance pumping) or peristalsis; however, they all have neglected hematocrit. The impact of blood cells was examined by fluid-structure interaction simulations, via the immersed boundary method. Moreover, electrophysiology models were incorporated into an immersed boundary framework, and bifurcations within the morphospace were studied that give rise to a spectrum of pumping regimes, with peristaltic-like waves of contraction and impedance pumping at the extremes. Lastly, effects of resonant pumping, damping, and boundary inertial effects (added mass) were studied for dynamic suction pumping. The other stage of heart development considered here is during the onset of ventricular trabeculation. This occurs after the heart has undergone the cardiac looping stage and now is a multi-chambered pumping system with primitive endocardial cushions, which act as precursors to valve leaflets. Trabeculation introduces complex morphology onto the inner lining of the endocardium in the ventricle. This transition of a smooth endocardium to one with complex geometry, may have significant effect on the intracardial fluid dynamics and stress distribution within emrbyonic hearts. Previous studies have not included these geometric perturbations along the ventricular endocardium. The role of trabeculae on intracardial (and intertrabecular) flows was studied using two different mathematical models implemented within an immersed boundary framework. It is shown that the trabecular geometry and number density have a significant effect on such flows. Furthermore this thesis also focused attention to the creation of software for scientists and engineers to perform fluid-structure interaction simulations at an accelerated rate, in user-friendly environments for beginner programmers, e.g., MATLAB or Python 3.5. The software, IB2d, performs fully coupled fluid-structure interaction problems using Charles Peskin's immersed boundary method. IB2d is capable of running a vast range of biomechanics models and contains multiple options for constructing material properties of the fiber structure, advection-diffusion of a chemical gradient, muscle mechanics models, Boussinesq approximations, and artificial forcing to drive boundaries with a preferred motion. The software currently contains over 50 examples, ranging from rubber-bands oscillating to flow past a cylinder to a simple aneurysm model to falling spheres in a chemical gradient to jellyfish locomotion to a heart tube pumping coupled with electrophysiology, muscle, and calcium dynamics modelsDoctor of Philosoph

Predictive Maintenance of an External Gear Pump using Machine Learning Algorithms

The importance of Predictive Maintenance is critical for engineering industries, such as manufacturing, aerospace and energy. Unexpected failures cause unpredictable downtime, which can be disruptive and high costs due to reduced productivity. This forces industries to ensure the reliability of their equip-ment. In order to increase the reliability of equipment, maintenance actions, such as repairs, replacements, equipment updates, and corrective actions are employed. These actions affect the flexibility, quality of operation and manu-facturing time. It is therefore essential to plan maintenance before failure occurs.Traditional maintenance techniques rely on checks conducted routinely based on running hours of the machine. The drawback of this approach is that maintenance is sometimes performed before it is required. Therefore, conducting maintenance based on the actual condition of the equipment is the optimal solu-tion. This requires collecting real-time data on the condition of the equipment, using sensors (to detect events and send information to computer processor).Predictive Maintenance uses these types of techniques or analytics to inform about the current, and future state of the equipment. In the last decade, with the introduction of the Internet of Things (IoT), Machine Learning (ML), cloud computing and Big Data Analytics, manufacturing industry has moved forward towards implementing Predictive Maintenance, resulting in increased uptime and quality control, optimisation of maintenance routes, improved worker safety and greater productivity.The present thesis describes a novel computational strategy of Predictive Maintenance (fault diagnosis and fault prognosis) with ML and Deep Learning applications for an FG304 series external gear pump, also known as a domino pump. In the absence of a comprehensive set of experimental data, synthetic data generation techniques are implemented for Predictive Maintenance by perturbing the frequency content of time series generated using High-Fidelity computational techniques. In addition, various types of feature extraction methods considered to extract most discriminatory informations from the data. For fault diagnosis, three types of ML classification algorithms are employed, namely Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB) algorithms. For prognosis, ML regression algorithms, such as MLP and SVM, are utilised. Although significant work has been reported by previous authors, it remains difficult to optimise the choice of hyper-parameters (important parameters whose value is used to control the learning process) for each specific ML algorithm. For instance, the type of SVM kernel function or the selection of the MLP activation function and the optimum number of hidden layers (and neurons).It is widely understood that the reliability of ML algorithms is strongly depen-dent upon the existence of a sufficiently large quantity of high-quality training data. In the present thesis, due to the unavailability of experimental data, a novel high-fidelity in-silico dataset is generated via a Computational Fluid Dynamic (CFD) model, which has been used for the training of the underlying ML metamodel. In addition, a large number of scenarios are recreated, ranging from healthy to faulty ones (e.g. clogging, radial gap variations, axial gap variations, viscosity variations, speed variations). Furthermore, the high-fidelity dataset is re-enacted by using degradation functions to predict the remaining useful life (fault prognosis) of an external gear pump.The thesis explores and compares the performance of MLP, SVM and NB algo-rithms for fault diagnosis and MLP and SVM for fault prognosis. In order to enable fast training and reliable testing of the MLP algorithm, some predefined network architectures, like 2n neurons per hidden layer, are used to speed up the identification of the precise number of neurons (shown to be useful when the sample data set is sufficiently large). Finally, a series of benchmark tests are presented, enabling to conclude that for fault diagnosis, the use of wavelet features and a MLP algorithm can provide the best accuracy, and the MLP al-gorithm provides the best prediction results for fault prognosis. In addition, benchmark examples are simulated to demonstrate the mesh convergence for the CFD model whereas, quantification analysis and noise influence on training data are performed for ML algorithms

Advanced Computational Fluid Dynamics for Emerging Engineering Processes

As researchers deal with processes and phenomena that are geometrically complex and phenomenologically coupled the demand for high-performance computational fluid dynamics (CFD) increases continuously. The intrinsic nature of coupled irreversibility requires computational tools that can provide physically meaningful results within a reasonable time. This book collects the state-of-the-art CFD research activities and future R&D directions of advanced fluid dynamics. Topics covered include in-depth fundamentals of the Navier-Stokes equation, advanced multi-phase fluid flow, and coupling algorithms of computational fluid and particle dynamics. In the near future, true multi-physics and multi-scale simulation tools must be developed by combining micro-hydrodynamics, fluid dynamics, and chemical reactions within an umbrella of irreversible statistical physics

A numerical method for fluid-structure interactions of slender rods in turbulent flow

This thesis presents a numerical method for the simulation of fluid-structure interaction (FSI) problems on high-performance computers. The proposed method is specifically tailored to interactions between Newtonian fluids and a large number of slender viscoelastic structures, the latter being modeled as Cosserat rods. From a numerical point of view, such kind of FSI requires special techniques to reach numerical stability. When using a partitioned fluid-structure coupling approach this is usually achieved by an iterative procedure, which drastically increases the computational effort. In the present work, an alternative coupling approach is developed based on an immersed boundary method (IBM). It is unconditionally stable and exempt from any global iteration between the fluid part and the structure part. The proposed FSI solver is employed to simulate the flow over a dense layer of vegetation elements, usually designated as canopy flow. The abstracted canopy model used in the simulation consists of 800 strip-shaped blades, which is the largest canopy-resolving simulation of this type done so far. To gain a deeper understanding of the physics of aquatic canopy flows the simulation data obtained are analyzed, e.g., concerning the existence and shape of coherent structures

Integrated Heart - Coupling multiscale and multiphysics models for the simulation of the cardiac function

Mathematical modelling of the human heart and its function can expand our understanding of various cardiac diseases, which remain the most common cause of death in the developed world. Like other physiological systems, the heart can be understood as a complex multiscale system involving interacting phenomena at the molecular, cellular, tissue, and organ levels. This article addresses the numerical modelling of many aspects of heart function, including the interaction of the cardiac electrophysiology system with contractile muscle tissue, the sub-cellular activation-contraction mechanisms, as well as the hemodynamics inside the heart chambers. Resolution of each of these sub-systems requires separate mathematical analysis and specially developed numerical algorithms, which we review in detail. By using specific sub-systems as examples, we also look at systemic stability, and explain for example how physiological concepts such as microscopic force generation in cardiac muscle cells, translate to coupled systems of differential equations, and how their stability properties influence the choice of numerical coupling algorithms. Several numerical examples illustrate three fundamental challenges of developing multiphysics and multiscale numerical models for simulating heart function, namely: (i) the correct upscaling from single-cell models to the entire cardiac muscle, (ii) the proper coupling of electrophysiology and tissue mechanics to simulate electromechanical feedback, and (iii) the stable simulation of ventricular hemodynamics during rapid valve opening and closure

Numerical Simulation

Nowadays mathematical modeling and numerical simulations play an important role in life and natural science. Numerous researchers are working in developing different methods and techniques to help understand the behavior of very complex systems, from the brain activity with real importance in medicine to the turbulent flows with important applications in physics and engineering. This book presents an overview of some models, methods, and numerical computations that are useful for the applied research scientists and mathematicians, fluid tech engineers, and postgraduate students

3D cine DENSE MRI: ventricular segmentation and myocardial stratin analysis

Includes abstract. Includes bibliographical references

Advanced Fluid Dynamics

This book provides a broad range of topics on fluid dynamics for advanced scientists and professional researchers. The text helps readers develop their own skills to analyze fluid dynamics phenomena encountered in professional engineering by reviewing diverse informative chapters herein