570 research outputs found

    Mechanical characterization, constitutive modeling and applications of ultra-soft magnetorheological elastomers

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    Mención Internacional en el título de doctorSmart materials are bringing sweeping changes in the way humans interact with engineering devices. A myriad of state-of-the-art applications are based on novel ways to actuate on structures that respond under different types of stimuli. Among them, materials that respond to magnetic fields allow to remotely modify their mechanical properties and macroscopic shape. Ultra-soft magnetorheological elastomers (MREs) are composed of a highly stretchable soft elastomeric matrix in the order of 1 kPa and magnetic particles embedded in it. This combination allows large deformations with small external actuations. The type of the magnetic particles plays a crucial role as it defines the reversibility or remanence of the material magnetization. According to the fillers used, MREs are referred to as soft-magnetic magnetorheological elastomers (sMREs) and hard-magnetic magnetorheological elastomers (hMREs). sMREs exhibit strong changes in their mechanical properties when an external magnetic field is applied, whereas hMREs allow sustained magnetic effects along time and complex shape-morphing capabilities. In this regard, end-of-pipe applications of MREs in the literature are based on two major characteristics: the modification of their mechanical properties and macrostructural shape changes. For instance, smart actuators, sensors and soft robots for bioengineering applications are remotely actuated to perform functional deformations and autonomous locomotion. In addition, hMREs have been used for industrial applications, such as damping systems and electrical machines. From the analysis of the current state of the art, we identified some impediments to advance in certain research fields that may be overcome with new solutions based on ultrasoft MREs. On the mechanobiology area, we found no available experimental methodologies to transmit complex and dynamic heterogeneous strain patterns to biological systems in a reversible manner. To remedy this shortcoming, this doctoral research proposes a new mechanobiology experimental setup based on responsive ultra-soft MRE biological substrates. Such an endeavor requires deeper insights into the magneto-viscoelastic and microstructural mechanisms of ultra-soft MREs. In addition, there is still a lack of guidance for the selection of the magnetic fillers to be used for MREs and the final properties provided to the structure. Eventually, the great advances on both sMREs and hMREs to date pose a timely question on whether the combination of both types of particles in a hybrid MRE may optimize the multifunctional response of these active structures. To overcome these roadblocks, this thesis provides an extensive and comprehensive experimental characterization of ultra-soft sMREs, hMREs and hybrid MREs. The experimental methodology uncovers magneto-mechanical rate dependences under numerous loading and manufacturing conditions. Then, a set of modeling frameworks allows to delve into such mechanisms and develop three ground-breaking applications. Therefore, the thesis has lead to three main contributions. First and motivated on mechanobiology research, a computational framework guides a sMRE substrate to transmit complex strain patterns in vitro to biological systems. Second, we demonstrate the ability of remanent magnetic fields in hMREs to arrest cracks propagations and improve fracture toughness. Finally, the combination of soft- and hard-magnetic particles is proved to enhance the magnetorheological and magnetostrictive effects, providing promising results for soft robotics.Los materiales inteligentes están generando cambios radicales en la forma que los humanos interactúan con dispositivos ingenieriles. Distintas aplicaciones punteras se basan en formas novedosas de actuar sobre materiales que responden a diferentes estímulos. Entre ellos, las estructuras que responden a campos magnéticos permiten la modificación de manera remota tanto de sus propiedades mecánicas como de su forma. Los elastómeros magnetorreológicos (MREs) ultra blandos están compuestos por una matriz elastomérica con gran ductilidad y una rigidez en torno a 1 kPa, reforzada con partículas magnéticas. Esta combinación permite inducir grandes deformaciones en el material mediante la aplicación de campos magnéticos pequeños. La naturaleza de las partículas magnéticas define la reversibilidad o remanencia de la magnetización del material compuesto. De esta manera, según el tipo de partículas que contengan, los MREs pueden presentar magnetización débil (sMRE) o magnetización fuerte (hMRE). Los sMREs experimentan grandes cambios en sus propiedades mecánicas al aplicar un campo magnético externo, mientras que los hMREs permiten efectos magneto-mecánicos sostenidos a lo largo del tiempo, así como programar cambios de forma complejos. En este sentido, las aplicaciones de los MREs se basan en dos características principales: la modificación de sus propiedades mecánicas y los cambios de forma macroestructurales. Por ejemplo, los campos magnéticos pueden emplearse para inducir deformaciones funcionales en actuadores y sensores inteligentes, o en robótica blanda para bioingeniería. Los hMREs también se han aplicado en el ámbito industrial en sistemas de amortiguación y máquinas eléctricas. A partir del análisis del estado del arte, se identifican algunas limitaciones que impiden el avance en ciertos campos de investigación y que podrían resolverse con nuevas soluciones basadas en MREs ultra blandos. En el área de la mecanobiología, no existen metodologías experimentales para transmitir patrones de deformación complejos y dinámicos a sistemas biológicos de manera reversible. En esta investigación doctoral se propone una configuración experimental novedosa basada en sustratos biológicos fabricados con MREs ultra blandos. Dicha solución requiere la identificación de los mecanismos magneto-viscoelásticos y microestructurales de estos materiales, según el tipo de partículas magnéticas, y las consiguientes propiedades macroscópicas del material. Además, investigaciones recientes en sMREs y hMREs plantean la pregunta sobre si la combinación de distintos tipos de partículas magnéticas en un MRE híbrido puede optimizar su respuesta multifuncional. Para superar estos obstáculos, la presente tesis proporciona una caracterización experimental completa de sMREs, hMREs y MREs híbridos ultra blandos. Estos resultados muestran las dependencias del comportamiento multifuncional del material con la velocidad de aplicación de cargas magneto-mecánicas. El desarrollo de un conjunto de modelos teórico-computacionales permite profundizar en dichos mecanismos y desarrollar aplicaciones innovadoras. De este modo, la tesis doctoral ha dado lugar a tres bloques de aportaciones principales. En primer lugar, este trabajo proporciona un marco computacional para guiar el diseño de sustratos basados en sMREs para transmitir patrones de deformación complejos in vitro a sistemas biológicos. En segundo lugar, se demuestra la capacidad de los campos magnéticos remanentes en los hMRE para detener la propagación de grietas y mejorar la tenacidad a la fractura. Finalmente, se establece que la combinación de partículas magnéticas de magnetización débil y fuerte mejora el efecto magnetorreológico y magnetoestrictivo, abriendo nuevas posibilidades para el diseño de robots blandos.I want to acknowledge the support from the Ministerio de Ciencia, Innovación y Universidades, Spain (FPU19/03874), and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 947723, project: 4D-BIOMAP).Programa de Doctorado en Ingeniería Mecánica y de Organización Industrial por la Universidad Carlos III de MadridPresidente: Ramón Eulalio Zaera Polo.- Secretario: Abdón Pena Francesch.- Vocal: Laura de Lorenzi

    Design of decorative 3D models: from geodesic ornaments to tangible assemblies

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    L'obiettivo di questa tesi è sviluppare strumenti utili per creare opere d'arte decorative digitali in 3D. Uno dei processi decorativi più comunemente usati prevede la creazione di pattern decorativi, al fine di abbellire gli oggetti. Questi pattern possono essere dipinti sull'oggetto di base o realizzati con l'applicazione di piccoli elementi decorativi. Tuttavia, la loro realizzazione nei media digitali non è banale. Da un lato, gli utenti esperti possono eseguire manualmente la pittura delle texture o scolpire ogni decorazione, ma questo processo può richiedere ore per produrre un singolo pezzo e deve essere ripetuto da zero per ogni modello da decorare. D'altra parte, gli approcci automatici allo stato dell'arte si basano sull'approssimazione di questi processi con texturing basato su esempi o texturing procedurale, o con sistemi di riproiezione 3D. Tuttavia, questi approcci possono introdurre importanti limiti nei modelli utilizzabili e nella qualità dei risultati. Il nostro lavoro sfrutta invece i recenti progressi e miglioramenti delle prestazioni nel campo dell'elaborazione geometrica per creare modelli decorativi direttamente sulle superfici. Presentiamo una pipeline per i pattern 2D e una per quelli 3D, e dimostriamo come ognuna di esse possa ricreare una vasta gamma di risultati con minime modifiche dei parametri. Inoltre, studiamo la possibilità di creare modelli decorativi tangibili. I pattern 3D generati possono essere stampati in 3D e applicati a oggetti realmente esistenti precedentemente scansionati. Discutiamo anche la creazione di modelli con mattoncini da costruzione, e la possibilità di mescolare mattoncini standard e mattoncini custom stampati in 3D. Ciò consente una rappresentazione precisa indipendentemente da quanto la voxelizzazione sia approssimativa. I principali contributi di questa tesi sono l'implementazione di due diverse pipeline decorative, un approccio euristico alla costruzione con mattoncini e un dataset per testare quest'ultimo.The aim of this thesis is to develop effective tools to create digital decorative 3D artworks. Real-world art often involves the use of decorative patterns to enrich objects. These patterns can be painted on the base or might be realized with the application of small decorative elements. However, their creation in digital media is not trivial. On the one hand, users can manually perform texture paint or sculpt each decoration, in a process that can take hours to produce a single piece and needs to be repeated from the ground up for every model that needs to be decorated. On the other hand, automatic approaches in state of the art rely on approximating these processes with procedural or by-example texturing or with 3D reprojection. However, these approaches can introduce significant limitations in the models that can be used and in the quality of the results. Instead, our work exploits the recent advances and performance improvements in the geometry processing field to create decorative patterns directly on surfaces. We present a pipeline for 2D and one for 3D patterns and demonstrate how each of them can recreate a variety of results with minimal tweaking of the parameters. Furthermore, we investigate the possibility of creating decorative tangible models. The 3D patterns we generate can be 3D printed and applied to previously scanned real-world objects. We also discuss the creation of models with standard building bricks and the possibility of mixing standard and custom 3D-printed bricks. This allows for a precise representation regardless of the coarseness of the voxelization. The main contributions of this thesis are the implementation of two different decorative pipelines, a heuristic approach to brick construction, and a dataset to test the latter

    Interaction of elastomechanics and fluid dynamics in the human heart : Opportunities and challenges of light coupling strategies

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    Das menschliche Herz ist das hochkomplexe Herzstück des kardiovaskulären Systems, das permanent, zuverlässig und autonom den Blutfluss im Körper aufrechterhält. In Computermodellen wird die Funktionalität des Herzens nachgebildet, um Simulationsstudien durchzuführen, die tiefere Einblicke in die zugrundeliegenden Phänomene ermöglichen oder die Möglichkeit bieten, relevante Parameter unter vollständig kontrollierten Bedingungen zu variieren. Angesichts der Tatsache, dass Herz-Kreislauf-Erkrankungen die häufigste Todesursache in den Ländern der westlichen Hemisphäre sind, ist ein Beitrag zur frühzeit- igen Diagnose derselben von großer klinischer Bedeutung. In diesem Zusammenhang können computergestützte Strömungssimulationen wertvolle Einblicke in die Blutflussdynamik liefern und bieten somit die Möglichkeit, einen zentralen Bereich der Physik dieses multiphysikalischen Organs zu untersuchen. Da die Verformung der Endokardoberfläche den Blutfluss antreibt, müssen die Effekte der Elastomechanik als Randbedingungen für solche Strömungssimulationen berücksichtigt werden. Um im klinischen Kontext relevant zu sein, muss jedoch ein Mittelweg zwischen dem Rechenaufwand und der erforderlichen Genauigkeit gefunden werden, und die Modelle müssen sowohl robust als auch zuverlässig sein. Daher werden in dieser Arbeit die Möglichkeiten und Herausforderungen leichter und daher weniger komplexer Kopplungsstrategien mit Schwerpunkt auf drei Schlüsselaspekten bewertet: Erstens wird ein auf dem Immersed Boundary-Ansatz basierender Fluiddynamik-Löser implementiert, da diese Methode mit einer sehr robusten Darstellung von bewegten Netzen besticht. Die grundlegende Funktionalität wurde für verschiedene vereinfachte Geometrien verifiziert und zeigte eine hohe Übereinstimmung mit der jeweiligen analytischen Lösung. Vergleicht man die 3D-Simulation einer realistischen Geometrie des linken Teils des Herzens mit einem körperangepassten Netzbeschreibung, so wurden grundlegende globale Größen korrekt reproduziert. Allerdings zeigten Variationen der Randbedingungen einen großen Einfluss auf die Simulationsergebnisse. Die Anwendung des Lösers zur Simulation des Einflusses von Pathologien auf die Blutströmungsmuster ergab Ergebnisse in guter Übereinstimmung mit Literaturwerten. Bei Simulationen der Mitralklappeninsuffizienz wurde der rückströmende Anteil mit Hilfe einer Partikelverfolgungsmethode visualisiert. Bei hypertropher Kardiomyopathie wurden die Strömungsmuster im linken Ventrikel mit Hilfe eines passiven Skalartransports bewertet, um die lokale Konzentration des ursprünglichen Blutvolumens zu visualisieren. Da in den vorgenannten Studien nur ein unidirektionaler Informationsfluss vom elas- tomechanischen Modell zum Strömungslöser berücksichtigt wurde, wird die Rückwirkung des räumlich aufgelösten Druckfeldes aus den Strömungssimulationen auf die Elastomechanik quantifiziert. Es wird ein sequenzieller Kopplungsansatz eingeführt, um fluiddynamische Einflüsse in einer Schlag-für-Schlag-Kopplungsstruktur zu berücksichtigen. Die geringen Abweichungen im mechanischen Solver von 2 mm verschwanden bereits nach einer Iteration, was darauf schließen lässt, dass die Rückwirkungen der Fluiddynamik im gesunden Herzen begrenzt ist. Zusammenfassend lässt sich sagen, dass insbesondere bei Strömungsdynamiksimula- tionen die Randbedingungen mit Vorsicht gewählt werden müssen, da sie aufgrund ihres großen Einflusses die Anfälligkeit der Modelle erhöhen. Nichtsdestotrotz zeigten verein- fachte Kopplungsstrategien vielversprechende Ergebnisse bei der Reproduktion globaler fluiddynamischer Größen, während die Abhängigkeit zwischen den Lösern reduziert und Rechenaufwand eingespart wird

    High-Order Mesh Morphing for Boundary and Interface Fitting to Implicit Geometries

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    We propose a method that morphs high-orger meshes such that their boundaries and interfaces coincide/align with implicitly defined geometries. Our focus is particularly on the case when the target surface is prescribed as the zero isocontour of a smooth discrete function. Common examples of this scenario include using level set functions to represent material interfaces in multimaterial configurations, and evolving geometries in shape and topology optimization. The proposed method formulates the mesh optimization problem as a variational minimization of the sum of a chosen mesh-quality metric using the Target-Matrix Optimization Paradigm (TMOP) and a penalty term that weakly forces the selected faces of the mesh to align with the target surface. The distinct features of the method are use of a source mesh to represent the level set function with sufficient accuracy, and adaptive strategies for setting the penalization weight and selecting the faces of the mesh to be fit to the target isocontour of the level set field. We demonstrate that the proposed method is robust for generating boundary- and interface-fitted meshes for curvilinear domains using different element types in 2D and 3D.Comment: 30 pages, 16 figure

    Drift-diffusion models for innovative semiconductor devices and their numerical solution

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    We present charge transport models for novel semiconductor devices which may include ionic species as well as their thermodynamically consistent finite volume discretization

    Caractérisation mécanique in vivo des tissus mous : application à la peau humaine et la chéloïde

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    The development of keloids, benign tumors on human skin, is not exclusively due to biological or genetic factors. The presence of anatomical sites favorable to the appearance of these tumors, while others are lacking them, attests to the importance of the mechanical environment of the tissue. The thesis aims to address the problem of keloid growth by developing a patient-specific pipeline, SofTI, based on in vivo experimental measurements and numerical modeling. The objective is to prevent further propagation of keloidic scars via a medical containment solution by identifying optimal material parameters to quantify mechanical stress and map its privileged direction locally. Additionally, the research work introduces MARSAC methodology to characterize the anisotropy in an undamaged skin by estimating Langer's line and stiffness along and across it with an in vivo multi-axial annular suction experiment. The method was used to analyze intra-subject and subject-to-subject variability over a clinical trial.Le développement des chéloïdes, tumeurs bénignes sur la peau humaine, n'est pas exclusivement dû à des facteurs biologiques ou génétiques. La présence de sites anatomiques favorables à l'apparition de ces tumeurs, tandis que d'autres en manquent, atteste de l'importance de l'environnement mécanique du tissu. La thèse vise à résoudre le problème de la croissance des chéloïdes en développant une méthode patient-spécifique, SofTI, basée sur des mesures expérimentales in vivo et une modélisation numérique. L'objectif est de prévenir la propagation des cicatrices chéloïdiennes à l'aide d'une solution médicale de contention en identifiant les paramètres matériau optimaux pour quantifier les contraintes mécaniques et cartographier ses directions privilégiées localement. De plus, le travail de recherche présente la méthodologie MARSAC pour charactériser l'anisotropie dans la peau saine en identifiant la ligne de Langer et la raideur le long et à travers celle-ci partant d'une expérience d'aspiration annulaire multi-axiale in vivo. La méthode a été employée pour analyser la variabilité intra- et inter-sujets sur un essai clinique

    Experimentally supported computational method for the optimal design selection of 3D printed fracture healing implant geometries

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    The development of AM technologies has brought about very promising opportunities in the field of tissue regeneration, especially due to the design freedom they enable. However, the tools and procedures needed to enable medical designers to make use of these revolutionary technologies still need to be developed. In particular, design tools to make implants with optimal geometries for tissue regeneration and procedures to manufacture and test such implants need to be developed to enable the adoption of these technologies by medical designers and biologists designing implants. This thesis aims to address this need. In order to best use the design freedom that AM brings; it is necessary to define the optimal geometries for specific applications. A novel tool that enables the design of optimal scaffold geometries and could be easily adopted by medical designers was developed here by proposing an intuitive design selection framework that graphically allows the user to gain an understanding of how design variables affect the chosen response variables. The novel framework is flexible, enabling the incorporation of any number of necessary computational models. Triply periodic minimal surface (TPMS) equations were used to simplify the design variables needed to generate an optimal porous scaffold geometry. The potential of this framework was demonstrated by using it to find the optimal TPMS type and volume fraction for a fracture fixation scaffold. Experiments were carried out to demonstrate that TCDMDA biocompatible scaffolds of appropriate pore size could be manufactured via projection micro stereolithography. The experiments successfully demonstrate for the first time that TCDMDA scaffolds can be manufactured via PµSLA by using a suitable combination of UV intensity and layer time. It was also demonstrated for the first time that hMSCs adhere to the surface of TCDMDA samples manufactured via PµSLA. To further enhance the cell adhesion, an oxygen plasma treatment was carried out. For the second part of this study it was found that the media could not penetrate the scaffold pores sufficiently, invalidating the results. The presented results highlighting a permeability challenge with TCDMDA scaffolds manufactured via PµSLA are nevertheless expected to contribute to future studies in this area. Experiments were also carried out to demonstrate the biocompatibility of scaffolds manufactured via stereolithography using Dental LT resin (Formlabs, UK). Successful adhesion of hMSCs to the surface of these scaffolds was shown in Chapter 4. Another novel finding of this thesis was that the Dental LT scaffolds manufactured via SLA were able to successfully enable cell growth, cell differentiation and mineralization in the presence of osteogenic media and BMP-2. The final part of the thesis focused on expanding the developed design selection framework to include not only a scaffold for fracture healing, but also a matching fracture fixation plate. Fracture fixation plates have been studied for centuries, but there is little research investigating the combination of a fracture fixation plate and a scaffold. The rise of AM has inspired the development of auxetic geometries, which have been applied to fracture fixation plates before and shown to reduce stress shielding. Moreover, stiffness grading has also proved very promising in improving fracture healing. In this thesis these two promising concepts are combined for the first time demonstrating reduced stress shielding compared to a conventional fixation plate geometry. Moreover, the thesis presents a novel computational design selection framework to find optimal scaffold and fracture plate geometries which lead to an improved healing outcome. The framework may be easily adopted by medical designers

    A Deep Learning Approach to Evaluating Disease Risk in Coronary Bifurcations

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    Cardiovascular disease represents a large burden on modern healthcare systems, requiring significant resources for patient monitoring and clinical interventions. It has been shown that the blood flow through coronary arteries, shaped by the artery geometry unique to each patient, plays a critical role in the development and progression of heart disease. However, the popular and well tested risk models such as Framingham and QRISK3 current cardiovascular disease risk models are not able to take these differences when predicting disease risk. Over the last decade, medical imaging and image processing have advanced to the point that non-invasive high-resolution 3D imaging is routinely performed for any patient suspected of coronary artery disease. This allows for the construction of virtual 3D models of the coronary anatomy, and in-silico analysis of blood flow within the coronaries. However, several challenges still exist which preclude large scale patient-specific simulations, necessary for incorporating haemodynamic risk metrics as part of disease risk prediction. In particular, despite a large amount of available coronary medical imaging, extraction of the structures of interest from medical images remains a manual and laborious task. There is significant variation in how geometric features of the coronary arteries are measured, which makes comparisons between different studies difficult. Modelling blood flow conditions in the coronary arteries likewise requires manual preparation of the simulations and significant computational cost. This thesis aims to solve these challenges. The "Automated Segmentation of Coronary Arteries (ASOCA)" establishes a benchmark dataset of coronary arteries and their associated 3D reconstructions, which is currently the largest openly available dataset of coronary artery models and offers a wide range of applications such as computational modelling, 3D printed for experiments, developing, and testing medical devices such as stents, and Virtual Reality applications for education and training. An automated computational modelling workflow is developed to set up, run and postprocess simulations on the Left Main Bifurcation and calculate relevant shape metrics. A convolutional neural network model is developed to replace the computational fluid dynamics process, which can predict haemodynamic metrics such as wall shear stress in minutes, compared to several hours using traditional computational modelling reducing the computation and labour cost involved in performing such simulations
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