Modeling and simulation of arterial walls with focus on damage and residual stresses

Die vorliegende Arbeit behandelt die kontinuumsmechanische Modellierung von Arterienwänden. Ein Schwerpunkt liegt in der Konstruktion von anisotropen Schädigungsmodellen zur Beschreibung von Schädigungseffekten in Arterienwänden, wie sie bei therapeutischen Maßnahmen auftreten. Solche Schädigungseffekte gelten als einer der wesentlichen Faktoren für eine erfolgreiche Behandlung von atherosklerotisch degenerierten Arterien mittels Ballonangioplastie. Ein weiterer Schwerpunkt liegt in der Erarbeitung eines numerischen Modells zur Berücksichtigung von Eigenspannungen in Arterienwänden. Eigenspannungen beeinflussen die Spannungsverteilung in Umfangsrichtung derart, dass sie zu einer Verringerung der Spannungsgradienten in der Arterienwand beitragen. Hierauf aufbauend wird ein neuer Ansatz zur Implementierung von Eigenspannungen vorgeschlagen. Alle Modelle werden an experimentelle Daten angepasst und auf die numerische Simulation von patientenspezifischen Arterienwänden angewendet. Die Quasi-Inkompressibilität des Materials wird zum einen durch die Verwendung einer Penalty-Methode und zum anderen über einen Augmented-Lagrange Ansatz erfüllt. Beide Methoden werden hinsichtlich ihres Einflusses auf die Robustheit numerischer Simulationen untersucht.The present work deals with the continuum-mechanical modeling and analysis of arterial walls. One focus is on the construction of anisotropic damage models that are able to reflect damage effects in arterial tissues under therapeutic loading. Damage effects are assumed to be a main contributor to the success of a balloon angioplasty, which is a method of treatment of atherosclerotic arteries. Another main focus is on the elaboration of a numerical model for the incorporation of residual stresses in arterial walls. Residual stresses influence the circumferential stress distribution in such a way that they prevent large stress gradients in the arterial wall. Thus, a novel approach for the implementation of residual stresses is proposed. All models are adjusted to experimental data and applied to numerical simulations of patient-specific arterial walls. The quasi-incompressibility constraint is ensured by using the Penalty-Method and the Augmented-Lagrange-Method, which are analyzed with respect to their computational robustness

Simulation of hyperelastic materials in real-time using Deep Learning

The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel computing, adaptive meshing, and model order reduction. In this paper we present U-Mesh: a data-driven method based on a U-Net architecture that approximates the non-linear relation between a contact force and the displacement field computed by a FEM algorithm. We show that deep learning, one of the latest machine learning methods based on artificial neural networks, can enhance computational mechanics through its ability to encode highly non-linear models in a compact form. Our method is applied to two benchmark examples: a cantilever beam and an L-shape subject to moving punctual loads. A comparison between our method and proper orthogonal decomposition (POD) is done through the paper. The results show that U-Mesh can perform very fast simulations on various geometries, mesh resolutions and number of input forces with very small errors

Doctor of Philosophy

dissertationDespite the progress that has been made since the inception of the finite element method, the field of biomechanics has generally relied on software tools that were not specifically designed to target this particular area of application. Software designed specifically for the field of computational biomechanics does not appear to exist. To overcome this limitation, FEBio was developed, an acronym for “Finite Elements for Biomechanics”, which provided an open-source framework for developing finite element software that is tailored to the specific needs of the biomechanics and biophysics communities. The proposed work added an extendible framework to FEBio that greatly facilitates the implementation of novel features and provides an ideal platform for exploring novel computational approaches. This framework supports plugins, which simplify the process of adding new features even more since plugins can be developed independently from the main source code. Using this new framework, this work extended FEBio in two important areas of interest in biomechanics. First, as tetrahedral elements continue to be the preferred modeling primitive for representing complex geometries, several tetrahedral formulations were investigated in terms of their robustness and accuracy for solving problems in computational biomechanics. The focus was on the performance of quadratic tetrahedral formulations in large deformation contact analyses, as this is an important area of application in biomechanics. Second, the application of prestrain to computational models has been recognized as an important component in simulations of biological tissues in order to accurately predict the mechanical response. As this remains challenging to do in existing software packages, a general computational framework for applying prestrain was incorporated in the FEBio software. The work demonstrated via several examples how plugins greatly simplify the development of novel features. In addition, it showed that the quadratic tetrahedral formulations studied in this work are viable alternatives for contact analyses. Finally, it demonstrated the newly developed prestrain plugin and showed how it can be used in various applications of prestrain

Slip and hall current effects on Jeffrey fluid suspension flow in a peristaltic hydromagnetic blood micropump

The magnetic properties of blood allow it to be manipulated with an electromagnetic field. Electromagnetic blood flow pumps are a robust technology which provide more elegant and sustainable performance compared with conventional medical pumps. Blood is a complex multi-phase suspension with non-Newtonian characteristics which are significant in micro-scale transport. Motivated by such applications, in the present article a mathematical model is developed for magnetohydrodynamic (MHD) pumping of blood in a deformable channel with peristaltic waves. A Jeffery’s viscoelastic formulation is employed for the rheology of blood. A twophase fluid-particle (“dusty”) model is utilized to better simulate suspension characteristics (plasma and erythrocytes). Hall current and wall slip effects are incorporated to achieve more realistic representation of actual systems. A two-dimensional asymmetric channel with dissimilar peristaltic wave trains propagating along the walls is considered. The governing conservation equations for mass, fluid and particle momentum are formulated with appropriate boundary conditions. The model is simplified using of long wavelength and creeping flow approximations. The model is also transformed from the fixed frame to the wave frame and rendered non-dimensional. Analytical solutions are derived. The resulting boundary value problem is solved analytically and exact expressions are derived for the fluid velocity, particulate velocity, fluid/particle fluid and particulate volumetric flow rates, axial pressure gradient, pressure rise and skin friction distributions are evaluated in detail. Increasing Hall current parameter reduces bolus growth in the channel, particle phase velocity and pressure difference in the augmented pumping region whereas it increases fluid phase velocity, axial pressure gradient and pressure difference in the pumping region. Increasing the hydrodynamic slip parameter accelerates both particulate and fluid phase flow at and close to the channel walls, enhances wall skin friction, boosts pressure difference in the augmented pumping region and increases bolus magnitudes. Increasing viscoelastic parameter (stress relaxation time to retardation time ratio) decelerates the fluid phase flow, accelerates the particle phase flow, decreases axial pressure gradient, elevates pressure difference in the augmented pumping region and reduces pressure difference in the pumping region. Increasing drag particulate suspension parameter decelerates the particle phase velocity, accelerates the fluid phase velocity, strongly elevates axial pressure gradient and reduces pressure difference (across one wavelength) in the augmented pumping region. Increasing particulate volume fraction density enhances bolus magnitudes in both the upper and lower zones of the channel and elevates pressure rise in the augmented pumping region

Single-shot compressed ultrafast photography: a review

Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields

Data-driven simulation for augmented surgery

International audienceTo build an augmented view of an organ during surgery, it is essential to have a biomechanical model with appropriate material parameters and boundary conditions , able to match patient specific properties. Adaptation to the patient's anatomy is obtained by exploiting the image-rich context specific to our application domain. While information about the organ shape, for instance, can be obtained preoper-atively, other patient-specific parameters can only be determined intraoperatively. To this end, we are developing data-driven simulations, which exploit information extracted from a stream of medical images. Such simulations need to run in real-time. To this end we have developed dedicated numerical methods, which allow for real-time computation of finite element simulations. The general principle consists in combining finite element approaches with Bayesian methods or deep learning techniques, that allow to keep control over the underlying computational model while allowing for inputs from the real world. Based on a priori knowledge of the mechanical behavior of the considered organ, we select a constitutive law to model its deformations. The predictive power of such constitutive law highly depends on the knowledge of the material parameters and A. Mendizaba

Models and Analysis of Vocal Emissions for Biomedical Applications

The Models and Analysis of Vocal Emissions with Biomedical Applications (MAVEBA) workshop came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy