It is currently a well-established fact that the dynamics of interacting fluid compartments of the central nervous system (CNS) may play a role in the CNS fluid physiology and pathology of a number of neurological disorders, including neurodegenerative diseases associated with accumulation of waste products in the brain. However, the mechanisms and routes of waste clearance from the brain are still unclear. One of the main components of this interacting cerebral fluids dynamics is blood flow. In the last decades, mathematical modeling and fluid dynamics simulations have become a valuable complementary tool to experimental approaches, contributing to a deeper understanding of the circulatory physiology and pathology. However, modeling blood flow in the brain remains a challenging and demanding task, due to the high complexity of cerebral vascular networks and the difficulties that consequently arise to describe and reproduce the blood flow dynamics in these vascular districts. The first part of this work is devoted to the development of efficient numerical strategies for blood flow simulations in complex vascular networks. In cardiovascular modeling, one-dimensional (1D) and lumped-parameter (0D) models of blood flow are nowadays well-established tools to predict flow patterns, pressure wave propagation and average velocities in vascular networks, with a good balance between accuracy and computational cost. Still, the purely 1D modeling of blood flow in complex and large networks can result in computationally expensive simulations, posing the need for extremely efficient numerical methods and solvers. To address these issues, we develop a novel modeling and computational framework to construct hybrid networks of coupled 1D and 0D vessels and to perform computationally efficient and accurate blood flow simulations in such networks. Starting from a 1D model and a family of nonlinear 0D models for blood flow, with either elastic or viscoelastic tube laws, this methodology is based on (i) suitable coupling equations ensuring conservation principles; (ii) efficient numerical methods and numerical coupling strategies to solve 1D, 0D and hybrid junctions of vessels; (iii) model selection criteria to construct hybrid networks, which provide a good trade-off between accuracy in the predicted results and computational cost of the simulations. By applying the proposed hybrid network solver to very complex and large vascular networks, we show how this methodology becomes crucial to gain computational efficiency when solving networks and models where the heterogeneity of spatial and/or temporal scales is relevant, still ensuring a good level of accuracy in the predicted results. Hence, the proposed hybrid network methodology represents a first step towards a high-performance modeling and computational framework to solve highly complex networks of 1D-0D vessels, where the complexity does not only depend on the anatomical detail by which a network is described, but also on the level at which physiological mechanisms and mechanical characteristics of the cardiovascular system are modeled. Then, in the second part of the thesis, we focus on the modeling and simulation of cerebral blood flow, with emphasis on the venous side. We develop a methodology that, departing from the high-resolution MRI data obtained from a novel in-vivo microvascular imaging technique of the human brain, allows to reconstruct detailed subject-specific cerebral networks of specific vascular districts which are suitable to perform blood flow simulations. First, we extract segmentations of cerebral districts of interest in a way that the arterio-venous separation is addressed and the continuity and connectivity of the vascular structures is ensured. Equipped with these segmentations, we propose an algorithm to extract a network of vessels suitable and good enough, i.e. with the necessary properties, to perform blood flow simulations. Here, we focus on the reconstruction of detailed venous vascular networks, given that the anatomy and patho-physiology of the venous circulation is of great interest from both clinical and modeling points of view. Then, after calibration and parametrization of the MRI-reconstructed venous networks, blood flow simulations are performed to validate the proposed methodology and assess the ability of such networks to predict physiologically reasonable results in the corresponding vascular territories. From the results obtained we conclude that this work represents a proof-of-concept study that demonstrates that it is possible to extract subject-specific cerebral networks from the novel high-resolution MRI data employed, setting the basis towards the definition of an effective processing pipeline for detailed blood flow simulations from subject-specific data, to explore and quantify cerebral blood flow dynamics, with focus on venous blood drainage.

<p>Injection to the subcutaneous (SC) tissue is one of the preferred methods for drug delivery of pharmaceuticals, from small molecules to monoclonal antibodies. Delivery to SC has become widely popular in part thanks to the low cost, ease of use, and effectiveness of drug delivery through the use of auto-injector devices. However, injection physiology, from initial plume formation to the eventual uptake of the drug in the lymphatics, is highly dependent on SC mechanics, poroelastic properties in particular. Yet, the poroelastic properties of SC have been understudied. In this thesis, I present a two-pronged approach to understanding the poroelastic properties of SC. Experimentally, mechanical and fluid transport properties of SC were measured with confined compression experiments and compared against gelatin hydrogels used as SC-phantoms. It was found that SC tissue is a highly non-linear material that has viscoelastic and porohyperelastic dissipation mechanisms. Gelatin hydrogels showed a similar, albeit more linear response, suggesting a micromechanical mechanism may underline the nonlinear behavior. The second part of the thesis focuses on the multiscale modeling of SC to gain a fundamental understanding of how geometry and material properties of the microstructure drive the macroscale response. SC is composed of adipocytes (fat cells) embedded in a collagen network. The geometry can be characterized with Voroni-like tessellations. Adipocytes are fluid-packed, highly deformable and capable of volume change through fluid transport. Collagen is highly nonlinear and nearly incompressible. Representative volume element (RVE) simulations with different Voroni tesselations shows that the different materials, coupled with the geometry of the packing, can contribute to different material response under the different kinds of loading. Further investigation of the effect of geometry showed that cell packing density nonlinearly contributes to the macroscale response. The RVE models can be homogenized to obtain macroscale models useful in large scale finite element simulations of injection physiology. Two types of homogenization were explored: fitting to analytical constitutive models, namely the Blatz-Ko material model, or use of Gaussian process surrogates, a data-driven non-parametric approach to interpolate the macroscale response.</p>

<p dir="ltr">This dissertation develops a comprehensive multiscale framework to predict microstructural evolution and associated mechanical response of materials by employing mechanistic finite element models or data-driven neural networks techniques. First, a novel approach is presented for simulating microstructural evolution during severe plastic deformation (SPD) in multiphase alloys, integrating dislocation density-based models with Crystal Plasticity Finite Element Modeling (CPFEM) to efficiently capture grain refinement across different phases in multiphase material. Second, a data-driven predictive model leveraging Artificial Neural Networks (ANN) is developed to link morphological attributes of microstructure—such as grain and cell structure—with material properties in additively manufactured AISI 316L, enhancing the ability to accurately predict material performance from microstructural details. Finally, dynamic recrystallization (DRX) is modeled through a finite element approach high-temperature deformation with the cell switching strategy of cellular automata, capturing key phenomena such as grain growth and nucleation events within a scalable multiscale approach. Together, these studies advance predictive capabilities for material deformation, promoting more efficient design and manufacturing processes.</p>

<p>This study is concerned with the predictive modeling of the machining and the mechanical behaviors of additive manufactured (AMed) Ti6AlV/TiC composites and Ti6Al4V, respectively, using microstructure-based hierarchical multiscale modeling. The predicted results could constitute as a basis for optimizing the parameters of machining and AM of the current materials.</p> <p>Through hierarchical flow of material behaviors from the atomistic, to the microscopic and the macroscopic scales, multiscale heterogeneous models (MHMs) coupled to the finite element method (FEM) are employed to simulate the conventional and the laser assisted machining (LAM) of Ti6AlV/TiC composites. In the atomistic level, molecular dynamics (MD) simulations are used to determine the traction-separation relationship for the cohesive zone model (CZM) describing the Ti6AlV/TiC interface. Bridging the microstructures across the scales in MHMs is achieved by representing the workpiece by macroscopic model with the microscopic heterogeneous structure including the Ti6Al4V matrix, the TiC particles, and their interfaces represented by the parameterized CZM. As a result, MHMs are capable of revealing the possible reasons of the peculiar high thrust forces behavior during conventional machining of Ti6Al4V/TiC composites, and how laser assisted machining can improve this behavior, which has not been conducted before.</p> <p>Extending MHMs to predict the mechanical behaviors of AMed Ti6Al4V would require including the heterogeneous microstructure at the grain level, which could be computational expensive. To solve this issue, the extended mechanics of structure genome (XMSG) is introduced as a novel multiscale homogenization approach to predict the mechanical behavior of AMed Ti6Al4V in a computationally efficient manner. This is realized by embedding the effects of microstructure heterogeneity, porosity growth, and crack propagation in the multiscale calculations of the mechanical behavior of the AMed Ti6Al4V using FEM. In addition, the XMSG can predict the asymmetry in the Young’s modulus of the AMed Ti6Al4V under tensile and compression loading as well as the anisotropy in the mechanical behaviors. The applicability of XMSG to fatigue life prediction with valid results is conducted by including the energy dissipations associated with cyclic loading/unloading in the calculations of the cyclic response of the material.</p>

The size and conformational complexity of proteins and other large systems represent major challenges for today's methods of quantum chemistry.This thesis is centered around the development of new computational tools to gain molecular-level insight into electronic transitions in such systems. To meet this challenge, we focus on the polarizable embedding (PE) model, which takes advantage of the fact that many electronic transitions are localized to a smaller part of the entire system.This motivates a partitioning of the large system into two regions that are treated at different levels of theory:The smaller part directly involved in the electronic process is described using accurate quantum-chemical methods, while the effects of the rest of the system, the environment, are incorporated into the Hamiltonian of the quantum region in an effective manner. This thesis presents extensions of the PE model with theaim of expanding its range of applicability to describe electronic transitions in large molecular systemsin the optical and X-ray regions. The developments cover both improvements with regardto the quantum region as well as the embedding potential representing the environment.Regarding the former, a damped linear response formulation has been implemented to allow for calculations of absorption spectra of large molecular systems acrossthe entire frequency range. A special feature of this development is its abilityto address core excitations that are otherwise not easily accessible.Another important development presented in this thesis is the coupling of the PE model to a multi-configuration self-consistent-field description of the quantum region and its further combination with response theory. In essence, this extends the PE model to the study of electronic transitions in large systems that are prone to static correlation --- a situation that is frequently encountered in biological systems. In addition to the direct environmental effects on the electronic structure of the quantum region, another important component of the description of electronic transitions in large molecular systems is an accurate account of the indirect effects of the environment, i.e., the geometrical distortions in the quantum region imposed by the environment. In thisthesis we have taken the first step toward the inclusion of geometry distortions in the PE frameworkby formulating and implementing molecular gradients for the quantum region. To identify critical points related to the environment description, we perform a theoretical analysis of the PE model starting from a full quantum-mechanicaltreatment of a composite system. Based on this, we present strategies for an accurate yet efficient construction of the embedding potentialcovering both the calculation of ground state and transition properties. The accurate representation of the environment makes it possible to reduce the size of the quantum region without compromising the overall accuracy of the final results. This further enables use of highly accurate quantum-chemical methods despite their unfavorable scaling with the size of the system. Finally, some examples of applications will be presented to demonstrate how the PE model may be applied as a tool to gain insight into and rationalize the factors influencing electronic transitions in large molecular systems of increasing complexity. / <p>The dissertation was awarded the best PhD thesis prize 2016 by the Danish Academy of Natural Sciences.</p><p></p><p>QC 20170209</p>

Variation et sélection sont au coeur de l'évolution Darwinienne. Cependant, ces deux mécanismes dépendent de processus eux-mêmes façonnés par l'évolution. Chez les micro-organismes, qui font face à des environnements souvent variables, ces propriétés adaptatives sont particulièrement bien exploitées, comme le démontrent de nombreuses expériences en laboratoire. Chez ses organismes, l'évolution semble donc avoir optimisé sa propre capacité à évoluer, un processus que nous nommons évolution de l'évolution (EvoEvo). La notion d'évolution de l'évolution englobe de nombreux concepts théoriques, tels que la variabilité, l'évolvabilité, la robustesse ou encore la capacité de l'évolution à innover (open-endedness). Ces propriétés évolutives des micro-organismes, et plus généralement de tous les organismes vivants, sont soupçonnées d'agir à tous les niveaux d'organisation biologique, en interaction ou en conflit, avec des conséquences souvent complexes et contre-intuitives. Ainsi, comprendre l'évolution de l'évolution implique l'étude de la trajectoire évolutive de micro-organismes — réels ou virtuels —, et ce à différents niveaux d'organisation (génome, interactome, population, …). L'objectif de ce travail de thèse a été de développer et d'étudier des modèles mathématiques et numériques afin de lever le voile sur certains aspects de l'évolution de l'évolution. Ce travail multidisciplinaire, car impliquant des collaborations avec des biologistes expérimentateur•rice•s, des bio-informaticien•ne•s et des mathématicien•ne•s, s'est divisé en deux parties distinctes, mais complémentaires par leurs approches : (i) l'extension d'un modèle historique en génétique des populations — le modèle géométrique de Fisher — afin d'étudier l'évolution du bruit phénotypique en sélection directionnelle, et (ii) le développement d'un modèle d'évolution in silico multi-échelles permettant une étude plus approfondie de l'évolution de l'évolution. Cette thèse a été financée par le projet européen EvoEvo (FP7-ICT-610427), grâce à la commission européenne. / Variation and selection are the two core processes of Darwinian Evolution. Yet, both are directly regulated by many processes that are themselves products of evolution. Microorganisms efficiently exploit this ability to dynamically adapt to new conditions. Thus, evolution seems to have optimized its own ability to evolve, as a primary means to react to environmental changes. We call this process evolution of evolution (EvoEvo). EvoEvo covers several aspects of evolution, encompassing major concepts such variability, evolvability, robustness, and open-endedness. Those phenomena are known to affect all levels of organization in bacterial populations. Indeed, understanding EvoEvo requires to study organisms experiencing evolution, and to decipher the evolutive interactions between all the components of the biological system of interest (genomes, biochemical networks, populations, ...). The objective of this thesis was to develop and exploit mathematical and numerical models to tackle different aspects of EvoEvo, in order to produce new knowledge on this topic, in collaboration with partners from diverse fields, including experimental biology, bioinformatics, mathematics and also theoretical and applied informatics. To this aim, we followed two complementary approaches: (i) a population genetics approach to study the evolution of phenotypic noise in directional selection, by extending Fisher's geometric model of adaptation, and (ii) a digital genetics approach to study multi-level evolution. This work was funded by the EvoEvo project, under the European Commission (FP7-ICT-610427).

Made available in DSpace on 2015-03-04T18:51:21Z (GMT). No. of bitstreams: 1 Iury.pdf: 2129454 bytes, checksum: 6a7aff5ca085814119b9518b0aab9bef (MD5) Previous issue date: 2010-08-05 / Conselho Nacional de Desenvolvimento Cientifico e Tecnologico / This work aims at developing computational models capable of furnishing more realistic and less costly computationally for the problem of electrokinetic remediation of polluted clayey soils. Innovative results are obtained by improving the multiscale models previously developed by Lima and co-workers through the construction of perturbations of the local microscopic problems in conjuction with more realistic boundary conditions at the electrodes and with the development of precise estimates for the assymptotic behavior of the macroscopic solution. Considering the aliance of such techniques within the framework of the homogenization method of periodic structures we discretize the macroscopic model by the finite element method numerical simulations of an electroosmose experiment capable of predicting more realistic scenarios of electrokinetic remediation. / Este trabalho objetiva o desenvolvimento de modelos computacionais capazes de construir simulações numéricas mais realistas e menos custosas computacionalmente para o problema de descontaminação de solos argilosos por técnicas de eletrocinética. Resultados inovadores são obtidos aprimorando-se os modelos multi-escala desenvolvidos anteriormente por Lima e colaboradores via construção de soluções perturbativas dos problemas locais microscópicos aliada à condições de contorno mais realistas nos eletrodos e ao desenvolvimento de estimativas precisas para o comportamento assintótico da solução macroscópica. Por intermédio da conjunção destas técnicas imersas no contexto da teoria de homogeneização de estruturas periódicas discretizamos o modelo macroscópico pelo método dos elementos finitos e construimos simulações numéricas de um experimento de eletroosmose capazes de predizer cenários mais realistas em eletrorremediação de solos.

L'infection d'un organisme par un agent pathogène déclenche l'activation des lymphocytes T-CD8 et l'initiation de la réponse immunitaire. Il s'ensuit un programme complexe de prolifération et de différenciation des lymphocytes T-CD8, contrôlé par l'évolution de leur contenu moléculaire. Dans ce manuscrit, nous présentons deux modèles mathématiques de la réponse T-CD8. Le premier se présente comme une équation différentielle à impulsions grâce à laquelle nous étudions l'effet du partage inégal des protéines lors des divisions cellulaires sur la régulation de l'hétérogénéité moléculaire. Le second est un modèle à base d'agents couplant la description d'une population discrète de lymphocytes T-CD8 à celle du contenu moléculaire de ces derniers. Ce modèle s'avère capable de reproduire les différentes phases caractéristiques de la réponse T-CD8 aux échelle cellulaire et moléculaire. Ces deux travaux supportent l'hypothèse que la dynamique cellulaire observée in vivo est le reflet de l'hétérogénéité moléculaire qui structure la population de lymphocytes T-CD8 / Infection of an organism by a pathogen triggers the activation of the CD8 T-cells and the initiation of the immune response. The result is a complex program of proliferation and differentiation of the CD8 T-cells, controlled by the evolution of their molecular content. In this manuscript, we present two mathematical models of the CD8 T-cell response. The first one is presented as an impulsive differential equation by which we study the effect of unequal molecular partitioning at cell division on the regulation of molecular heterogeneity. The second one is an agent-based-model that couples the description of a discrete population of CD8 T-cells and that of their molecular content. This model can reproduce the different typical phases of the CD8 T-cell response at both the cellular and the molecular scales. These two studies support the hypothesis that the cell dynamics observed in vivo is a consequence of the molecular heterogeneity structuring the CD8 T-cell population

Chemo-mechanics studies the material behavior and phenomena at the interface of mechanics and chemistry. Material failures due to coupled chemo-mechanical effects are serious roadblocks in the development of renewable energy technologies. Among the sources of renewable energies for the mass market, hydrogen and lithium-ion battery are promising candidates due to their high efficiency and easiness of conversion into other types of energy. However, hydrogen will degrade material mechanical properties and lithium insertion can cause electrode failures in battery owing to their high mobilities and strong chemo-mechanical coupling effects. These problems seriously prevent the large-scale applications of these renewable energy sources. In this thesis, the atomistic and continuum modeling are performed to study the chemical-mechanical failures. The objective is to understand the hydrogen embrittlement of grain boundary engineered metals and the lithium insertion-induced fracture in alloy electrodes for lithium-ion batteries. Hydrogen in metallic containment systems such as high-pressure vessels and pipelines causes the degradation of their mechanical properties that can result in sudden catastrophic fracture. A wide range of hydrogen embrittlement phenomena was attributed to the loss of cohesion of interfaces (between grains, inclusion and matrix, or phases) due to interstitially dissolved hydrogen. Our modeling and simulation of hydrogen embrittlement will address the question of why susceptibility to hydrogen embrittlement in metallic materials can be markedly reduced by grain boundary engineering. Implications of our results for efficient hydrogen storage and transport at high pressures are discussed. Silicon is one of the most promising anode materials for Li-ion batteries (LIB) because of the highest known theoretical charge capacity. However, Si anodes often suffer from pulverization and capacity fading. This is caused by the large volume changes of Si (~300%) upon Li insertion/extraction close to the theoretical charging/discharging limit. In particular, large incompatible deformation between areas of different Li contents tends to initiate fracture, leading to electro-chemical-mechanical failures of Si electrodes. In order to understand the chemo-mechanical mechanisms, we begin with the study of basic fracture modes in pure silicon, and then study the diffusion induced deformation and fracture in lithiated Si. Results have implications for increasing battery capacity and reliability. To improve mechanical stability of LIB anode, failure mechanisms of silicon and coated tin-oxide nanowires have been studied at continuum level. It's shown that anisotropic diffusivity and anisotropic deformation play vital roles in lithiation process. Our predictions of fracture initiation and evolution are verified by in situ experiment observations. Due to the mechanical confinement of the coating layers, our study demonstrates that it is possible to simultaneously control the electrochemical reaction rate and the mechanical strain of the electrode materials through carbon or aluminum coating, which opens new avenues of designing better lithium ion batteries. This thesis addresses the nano-chemo-mechanical failure problems in two green energy-carrier systems toward improving the performance of Li-ion battery anode and hydrogen storage system. It provides an atomistic and continuum modeling framework for the study of chemo-mechanics of advanced materials such as nano-structured metals and alloys. The results help understand the chemical effects of impurities on the mechanical properties of host materials with different metallic and covalent bonding characteristics.

L’objectif général de la thèse est de contribuer à l’amélioration de nos connaissances sur les métamorphoses de la neige sèche et sur sa description physique, à l’échelle microscopique (grains de glace et pores) et macroscopique (couche de neige). Dans un premier temps,la méthode d’homogénéisation basée sur les développements asymptotiques à échelles multiples est appliquée à la physique des métamorphoses de la neige sèche. On présente ainsi les descriptions macroscopiques équivalentes du transport de vapeur et de chaleur dérivées à partir de la description de la physique à micro-échelle. On considère à l’échelle des grains la diffusion, la conduction, et la convection forcée, couplées aux changements de phase (sublimation et déposition). Dans un second temps, les propriétés effectives de transport impliquées dans les descriptions macroscopiques (conductivité thermique effective, coefficient effectif de diffusion de vapeur et perméabilité intrinsèque) sont estimées à l’aide d’images 3D de neige couvrant toute la gamme de masse volumique et de types de neige. Enfin, on s’intéresse au suivi temporel des métamorphoses. Les liens entre la microstructure et les propriétés effectives d’une couche de neige sont mis en évidence au cours d’une métamorphose de gradient de température en utilisant des images 3D.On présente ensuite une cellule cryogénique que nous avons mise au point pour le suivi grains à grains par tomographie des évolutions d’un échantillon de neige au cours des métamorphoses, et qui s’utilise à température ambiante. / The main objective of the thesis is to improve our knowledge about dry snow metamorphismand its physical description, at the microscopic (ice grains and pores) andmacroscopic (snow layer) scales. First, the homogenization method of multiple scaleexpansions is applied for the first time to the physics involved in dry snow metamorphism.This way, we present the equivalent macroscopic descriptions of heat and vaportransfers derived from the physical description at micro-scale. We consider at the grainscale diffusion, conduction, and forced convection, coupled to phase changes (sublimationand deposition). Second, the effective properties of transport arising in the macroscopicdescriptions (effective thermal conductivity, effective coefficient of vapor diffusion, andintrinsic permeability) are estimated from 3D images of snow spanning the whole range ofdensity and snow types. Finally, the monitoring of metamorphism with time is considered.The relationship between the microstructure and the effective properties of a snow layerare investigated during temperature gradient metamorphism using 3D images. We presentthen a new cryogenic cell that we developed to monitor the grain to grain evolution of asnow sample by time-lapse tomography during the metamorphism, and which operates atroom temperature.