3,911 research outputs found
Numerical Simulations of Cavitating Bubbles in Elastic and Viscoelastic Materials for Biomedical Applications
The interactions of cavitating bubbles with elastic and viscoelastic materials play a central role in many biomedical applications. This thesis makes use of numerical modeling and data-driven approaches to characterize soft biomaterials at high strain rates via observation of bubble dynamics, and to model burst-wave lithotripsy, a focused ultrasound therapy to break kidney stones.
In the first part of the thesis, a data assimilation framework is developed for cavitation rheometry, a technique that uses bubble dynamics to characterize soft, viscoelastic materials at high strain-rates. This framework aims to determine material properties that best fit observed cavitating bubble dynamics. We propose ensemble-based data assimilation methods to solve this inverse problem. This approach is validated with surrogate data generated by adding random noise to simulated bubble radius time histories, and we show that we can confidently and efficiently estimate parameters of interest within 5% given an iterative Kalman smoother approach and an ensemble- based 4D-Var hybrid technique. The developed framework is applied to experimental data in three distinct settings, with varying bubble nucleation methods, cavitation media, and using different material constitutive models. We demonstrate that the mechanical properties of gels used in each experiment can be estimated quickly and accurately despite experimental inconsistencies, model error, and noisy data. The framework is used to further our understanding of the underlying physics and identify limitations of our bubble dynamics model for violent bubble collapse.
In the second part of the thesis, we simulate burst-wave lithotripsy (BWL), a non- invasive treatment for kidney stones that relies on repeated short bursts of focused ultrasound. Numerical approaches to study BWL require simulation of acoustic waves interacting with solid stones as well as bubble clouds which can nucleate ahead of the stone. We implement and validate a hypoelastic material model, which, with the addition of a continuum damage model and calibration of a spherically- focused transducer array, enables us to determine how effective various treatment strategies are with arbitrary stones. We present a preliminary investigation of the bubble dynamics occurring during treatment, and their impact on damage to the stone. Finally, we propose a strategy to reduce shielding by collapsing bubbles ahead of the stone via introduction of a secondary, low-frequency ultrasound pulse during treatment.</p
Functional Nanomaterials and Polymer Nanocomposites: Current Uses and Potential Applications
This book covers a broad range of subjects, from smart nanoparticles and polymer nanocomposite synthesis and the study of their fundamental properties to the fabrication and characterization of devices and emerging technologies with smart nanoparticles and polymer integration
Fluidic Nozzles for Automotive Washer Systems: Computational Fluid Dynamics and Experimental Analysis
One of the main goals of this project was to cultivate an understanding of fluidic nozzle geometries and characteristic flow. Through this knowledge, three new fluidic nozzle concepts were developed to be used as components in several windscreen washer systems for an automotive part supplier, Kautex Textron CVS Ltd.Accurate and conclusive visualisation of flow through fluidic nozzles was vital in understanding how they can be best utilised for different applications. Over the past century, the specific needs of automotive cleaning systems have greatly developed with new technological discoveries, these advances allow the driver further knowledge of their surroundings. These specialised systems each require a different type of maintenance and cleaning system depending on their usage and the different size and shape of the vehicle. By completing this project, it is hoped to allow manufacturers to accurately identify what sort of fluidic nozzles are best for windscreen cleaning systems for a vehicle and how to design a nozzle to suit their specification. Fluidic nozzles have been researched experimentally and computationally to ensure an accurate comparison of results. By guaranteeing a precise comparison it will negate the need for high volume testing of nozzles in experimental situations, greatly reducing time and resources required to analyse a fluidic nozzle.The fluidic nozzles that are investigated and developed in this project were modelled and examined both experimentally and computationally, this ensured valid and accurate results were achieved by both the computational modelling and experimental testing. The development of the nozzles within this project was conducted using several experimental and computational setups to analyse the spray distribution, angle and oscillatory frequency amongst other parameters significant to the nozzle usage on a vehicle. Through this it was possible to tailor nozzle dimensions to allow for a streamlined design approach, this increased efficiency in fluidic nozzle development for any specification given by a vehicle manufacturing company customer. In addition to this the water flow emitted from the outlet was experimentally tested and modelled with both stationary and high surrounding velocities to examine how external variables affect the flow of the water from the nozzle.iiiThis project has been useful in the design manufacturing process of fluidic nozzles, by utilising computational modelling it has allowed a faster and cheaper method of analysing the effect of design alterations to fluidic nozzles. There is a greatly reduced frequency required for rapid prototyping of an array of fluidic chips with minimal dimensional differences to be used in the experimental stages of design, as once the inlet boundary conditions are established the nozzle can be redesigned completely within reason without the need for additional material wastage. This ensures a more easy and precise method of testing the manufacturing tolerances of a fluidic nozzle with a target of reaching customer specifications are always achieved.Three nozzles were aimed developed to satisfy conditions set by the customers, the vehicle manufacturers at which the new nozzle designs are aimed at are Honda, Nissan and Toyota. The nozzles to be established were designed for use on windscreen washer systems with a varying number of nozzles and with diverse windscreen sizes for different vehicles, resulting in a wide variety of specifications that must be met for each vehicle manufacturer. This meant that a single nozzle could not be utilised for all vehicles, instead a base model of fluidic chip was developed for the Nissan vehicle which was then dimensionally changed to suit the other vehicles.Throughout this project there were design specifications changes and ambiguities from the automotive company customers, leading to redesigns of the fluidic chips designed in this project. This means that although only two of the three fluidic nozzle designs are successfully in production, a much greater understanding of the mechanics of the fluid flow within the fluidic nozzle was achieved
Design aspects and characterization of hydrogel-based bioinks for extrusion-based bioprinting
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AI-based design methodologies for hot form quench (HFQ®)
This thesis aims to develop advanced design methodologies that fully exploit the capabilities of the Hot Form Quench (HFQ®) stamping process in stamping complex geometric features in high-strength aluminium alloy structural components. While previous research has focused on material models for FE simulations, these simulations are not suitable for early-phase design due to their high computational cost and expertise requirements. This project has two main objectives: first, to develop design guidelines for the early-stage design phase; and second, to create a machine learning-based platform that can optimise 3D geometries under hot stamping constraints, for both early and late-stage design. With these methodologies, the aim is to facilitate the incorporation of HFQ capabilities into component geometry design, enabling the full realisation of its benefits.
To achieve the objectives of this project, two main efforts were undertaken. Firstly, the analysis of aluminium alloys for stamping deep corners was simplified by identifying the effects of corner geometry and material characteristics on post-form thinning distribution. New equation sets were proposed to model trends and design maps were created to guide component design at early stages. Secondly, a platform was developed to optimise 3D geometries for stamping, using deep learning technologies to incorporate manufacturing capabilities. This platform combined two neural networks: a geometry generator based on Signed Distance Functions (SDFs), and an image-based manufacturability surrogate model. The platform used gradient-based techniques to update the inputs to the geometry generator based on the surrogate model's manufacturability information. The effectiveness of the platform was demonstrated on two geometry classes, Corners and Bulkheads, with five case studies conducted to optimise under post-stamped thinning constraints. Results showed that the platform allowed for free morphing of complex geometries, leading to significant improvements in component quality.
The research outcomes represent a significant contribution to the field of technologically advanced manufacturing methods and offer promising avenues for future research. The developed methodologies provide practical solutions for designers to identify optimal component geometries, ensuring manufacturing feasibility and reducing design development time and costs. The potential applications of these methodologies extend to real-world industrial settings and can significantly contribute to the continued advancement of the manufacturing sector.Open Acces
Data-driven exact model order reduction for computational multiscale methods to predict high-cycle fatigue-damage in short-fiber reinforced plastics
Motiviert durch die Entwicklung energieeffizienterer Maschinen und Transportmittel hat der Leichtbau in den letzten Jahren enorm an Wichtigkeit gewonnen. Eine wichtige Klasse der Leichtbaumaterialien sind die faserverstärkten Kunststoffe. In der vorliegenden Arbeit liegt der Fokus auf der Entwicklung und Bereitstellung von Materialmodellen zur Vorhersage des Ermüdungsverhaltens kurzglasfaserverstärkter Thermoplaste. Diese Materialien unterscheiden sich dabei durch ihre Aufschmelzbarkeit und ihrer damit einhergehenden besseren Recyclebarkeit von thermosetbasierten Materialien. Außerdem erlauben die Kurzglasfasern im Gegensatz zu Langfasern eine einfache und zeiteffiziente Herstellung komplexer Komponenten.
Ermüdung ist ein wichtiger Versagensmechanismus in solchen Komponenten, insbesondere für Bauteile z.B. in Fahrzeugen, die vibrationsartigen Belastungen ausgesetzt sind. Durch die inherente Anisotropie des Materials sind die experimentelle Charakterisierung und Vorhersage dieses Versagensmechanismus jedoch äußerst zeitintensiv und stellen somit eine wesentliche Herausforderung im Entwicklungsprozess und für die breitere Anwendung solcher Bauteile dar. Daher ist die Entwicklung komplementärer simulativer Methoden von großem Interesse.
Im Rahmen dieser Arbeit werden Methoden zur Vorhersage der Ermüdungsschädigung kurzglasfaserverstärkter Werkstoffe im Rahmen einer Multiskalenmethode entwickelt. Die in der Arbeit betrachteten Multiskalenmodelle bieten die Möglichkeit, allein anhand der experimentellen Charakterisierungen der Materialparameter der Konstituenten, d.h. Faser und Matrix, komplexe anisotrope Effekte des Verbundmaterials vorherzusagen. Der experimentelle Aufwand kann dadurch enorm reduziert werden. Dazu werden zunächst Materialmodelle für die Konstituenten des Komposits entwickelt. Mithilfe FFT-basierter rechnergestützter Homogenisierung wird daraus das Materialverhalten des Komposits für verschiedene Mikrostrukturen und Lastfälle vorhergesagt. Die vorberechneten Lastfälle auf Mikrostrukturebene werden mit datengetriebenen Methoden auf die Makroskala übertragen. Das ermöglicht eine effiziente Berechnung von Bauteilen in wenigen Stunden, wohingegen eine entsprechende Berechnung mit geometrischer Auflösung aller einzelnen Fasern der Mikrostruktur auf heutigen Computern viele Jahre dauern würden.
Für die Matrix werden unterschiedliche Schädigungsmodelle untersucht. Ihre Vor- und Nachteile werden analysiert. Die Mikrostruktursimulationen geben einen Einblick in den Einfluss verschiedener statistischer Parameter wie Faserlängen und Faservolumengehalt auf das Kompositverhalten. Ein neues Modellordnungsreduktionsverfahren wird entwickelt und zur Simulation des Ermüdungsschädigungsverhaltens auf Bauteilebene angewandt. Weiter werden Modellerweiterungen zur Berücksichtigung des R-Wert-Verhältnisses und viskoelastischer Effekte in der Evolution der Ermüdungsschädigung entwickelt und mit experimentellen Ergebnissen validiert.
Das entstandene Simulationsframework erlaubt nach Vorrechnungen auf einer geringen Menge von Mikrostrukturen und Lastfällen eine effiziente Makrosimulation eines Bauteils vorzunehmen. Dabei können Effekte wie Viskoelastizität und R-Wert-Abhängigkeit je nach gewünschter Modellierungstiefe berücksichtigt oder vernachlässigt werden, um immer das effizientste Modell, das alle relevanten Effekte abbildet, nutzen zu können
Acoustic Propagation Variation with Temperature Profile in Water Filled Steel Pipes at Pressure
Conventional pressure leak testing of buried pipelines compares measurements of pressure with pipe wall temperature. An alternative proposed method uses acoustic velocity measurements to replace pipe wall temperature measurements. Early experiments using this method identified anomalous results of rising acoustic velocities thought to be caused by air solution.
This research investigated the anomalous acoustic velocity measurements by evaluation of acoustic velocity variation with pressure, temperature and air solution. Quiescent air solution rate experiments were carried out in water filled pipes. Computer modelling of the air bubble shape variation with pipe diameter was found to agree with bubble and drop experiments over the pipe diameter range from 100 mm to 1000 mm. Bubbles were found to maintain constant width over a large volume range confirmed by experiments and modelling
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