A New Approach to Model Confined Suspensions Flows in Complex Networks: Application to Blood Flow

The modeling of blood flows confined in micro-channels or micro-capillary beds depends on the interactions between the cell-phase, plasma and the complex geometry of the network. In the case of capillaries or channels having a high aspect ratio (their longitudinal size is much larger than their transverse one), this modeling is much simplified from the use of a continuous description of fluid viscosity as previously proposed in the literature. Phase separation or plasma skimming effect is a supplementary mechanism responsible for the relative distribution of the red blood cell’s volume density in each branch of a given bifur- cation. Different models have already been proposed to connect this effect to the various hydrodynamics and geometrical parameters at each bifurcation. We discuss the advantages and drawbacks of these models and compare them to an alternative approach for modeling phase distribution in complex channels networks. The main novelty of this new formulation is to show that albeit all the previous approaches seek for a local origin of the phase segre- gation phenomenon, it can arise from a global non-local and nonlinear structuration of the flow inside the network. This new approach describes how elementary conservation laws are sufficient principles (rather than the complex arametric models previously proposed) to provide non local phase separation. Spatial variations of the hematocrit field thus result from the topological complexity of the network as well as nonlinearities arising from solving a new free boundary problem associated with the flux and mass conservation. This network model approach could apply to model blood flow distribution either on artificial micro-models, micro-fluidic networks, or realistic reconstruction of biological micro-vascular networks

Taming active turbulence with patterned soft interfaces

Active matter embraces systems that self-organize at different length and time scales, often exhibiting turbulent flows. Here, we use a quasi-two-dimensional nematically ordered layer of a protein-based active gel to experimentally demonstrate that the geometry of active flows, which we have characterized by means of the statistical distribution of eddy sizes, can be reversibly modified. To this purpose, the active material is prepared in contact with a thermotropic liquid crystal, whose lamellar smectic-A phase tiles the water/oil interface with a lattice of anisotropic domains that feature circular easy-flow directions. The active flow, which arises from the motion of parabolic folds within the filamentous active material, becomes effectively confined into circulating eddies that are in registry with the underlying smectic-A domains. Based on topological arguments applied to the circular confinement, together with well-known properties of the active material, we show that the structure of the active flow is determined by a single intrinsic length scale. The role of this length scale thus reemerges from setting the decay length in the exponential eddy size distribution of the free turbulent regime, to determining the minimum size of circulating eddies featuring a scale-free power law. Our results demonstrate that soft-confinement provides with an invaluable tool to probe the intrinsic length and time scales of active materials, and pave the way for further exploration looking for similarities and differences between active and passive two-dimensional turbulence

Multiple-scale analysis of transport phenomena in porous media with biofilms

This dissertation examines transport phenomena within porous media colonized by biofilms. These sessile communities of microbes can develop within subsurface soils or rocks, or the riverine hyporheic zone and can induce substantial modification to mass and momentum transport dynamics. Biofilms also extensively alter the chemical speciation within freshwater ecosystems, leading to the biodegradation of many pollutants. Consequently, such systems have received a considerable amount of attention from the ecological engineering point of view. Yet, research has been severely limited by our incapacity to (1) directly observe the microorganisms within real opaque porous structures and (2) assess for the complex multiple-scale behavior of the phenomena. This thesis presents theoretical and experimental breakthroughs that can be used to overcome these two difficulties. An innovative strategy, based on X-ray computed microtomography, is devised to obtain three-dimensional images of the spatial distribution of biofilms within porous structures. This topological information can be used to study the response of the biological entity to various physical, chemical and biological parameters at the pore-scale. In addition, these images are obtained from relatively large volumes and, hence, can also be used to determine the influence of biofilms on the solute transport on a larger scale. For this purpose, the boundary-value-problems that describe the pore-scale mass transport are volume averaged to obtain homogenized Darcy-scale equations. Various models, along with their domains of validity, are presented in the cases of passive and reactive transport. This thesis uses the volume averaging technique, in conjunction with spatial moments analyses, to provide a comprehensive macrotransport theory as well as a thorough study of the relationship between the different models, especially between the two-equation and one-equation models. A non-standard average plus perturbation decomposition is also presented to obtain a one-equation model in the case of multiphasic transport with linear reaction rates. Eventually, the connection between the three-dimensional images and the theoretical multiple-scale analysis is established. This thesis also briefly illustrates how the permeability can be calculated numerically by solving the so-called closure problems from the three-dimensional X-ray images

Advanced physics-based and data-driven strategies

Cotutela: Universitat Politècnica de Catalunya i École Centrale de Nantes.Premi Extraordinari de Doctorat, promoció 2018-2019. Àmbit d’Enginyeria Civil i AmbientalSimulation Based Engineering Science (SBES) has brought major improvements in optimization, control and inverse analysis, all leading to a deeper understanding in many processes occuring in the real world. These noticeable breakthroughts are present in a vast variety of sectors such as aeronautic or automotive industries, mobile telecommunications or healthcare among many other fields. Nevertheless, SBES is currently confronting several difficulties to provide accurate results in complex industrial problems. Apart from the high computational costs associated with industrial applications, the errors introduced by constitutive modeling become more and more important when dealing with new materials. Concurrently, an unceasingly growing interest in concepts such as Big-Data, Machine Learning or Data-Analytics has been experienced. Indeed, this interest is intrinsically motivated by an exhaustive development in both data-acquisition and data-storage systems. For instance, an aircraft may produce over 500 GB of data during a single flight. This panorama brings a perfect opportunity to the so-called Dynamic Data Driven Application Systems (DDDAS), whose main objective is to merge classical simulation algorithms with data coming from experimental measures in a dynamic way. Within this scenario, data and simulations would no longer be uncoupled but rather a symbiosis that is to be exploited would achieve milestones which were inconceivable until these days. Indeed, data will no longer be understood as a static calibration of a given constitutive model but rather the model will be corrected dynamicly as soon as experimental data and simulations tend to diverge. Several numerical algorithms will be presented throughout this manuscript whose main objective is to strengthen the link between data and computational mechanics. The first part of the thesis is mainly focused on parameter identification, data-driven and data completion techniques. The second part is focused on Model Order Reduction (MOR) techniques, since they constitute a fundamental ally to achieve real time constraints arising from DDDAS framework.La Ciencia de la Ingeniería Basada en la Simulación (SBES) ha aportado importantes mejoras en la optimización, el control y el análisis inverso, todo lo cual ha llevado a una comprensión más profunda de muchos de los procesos que ocurren en el mundo real. Estos notables avances están presentes en una gran variedad de sectores como la industria aeronáutica o automotriz, las telecomunicaciones móviles o la salud, entre muchos otros campos. Sin embargo, SBES se enfrenta actualmente a varias dificultades para proporcionar resultados precisos en problemas industriales complejos. Aparte de los altos costes computacionales asociados a las aplicaciones industriales, los errores introducidos por el modelado constitutivo son cada vez más importantes a la hora de tratar con nuevos materiales. Al mismo tiempo, se ha experimentado un interés cada vez mayor en conceptos como Big-Data, Machine Learning o Data-Analytics. Ciertamente, este interés está intrínsecamente motivado por un desarrollo exhaustivo de los sistemas de adquisición y almacenamiento de datos. Por ejemplo, una aeronave puede producir más de 500 GB de datos durante un solo vuelo. Este panorama brinda una oportunidad perfecta a los denominados Sistemas de Aplicación Dinámicos Impulsados por Datos (DDDAS), cuyo objetivo principal es fusionar de forma dinámica los algoritmos clásicos de simulación con los datos procedentes de medidas experimentales. En este escenario, los datos y las simulaciones ya no se desacoplarían, sino que aprovechando una simbiosis se alcanzaría hitos que hasta ahora eran inconcebibles. Mas en detalle, los datos ya no se entenderán como una calibración estática de un modelo constitutivo dado, sino que el modelo se corregirá dinámicamente tan pronto como los datos experimentales y las simulaciones tiendan a diverger. A lo largo de este manuscrito se presentarán varios algoritmos numéricos cuyo objetivo principal es fortalecer el vínculo entre los datos y la mecánica computacional. La primera parte de la tesis se centra principalmente en técnicas de identificación de parámetros, basadas en datos y de compleción de datos. La segunda parte se centra en las técnicas de Reducción de Modelo (MOR), ya que constituyen un aliado fundamental para conseguir las restricciones de tiempo real derivadas del marco DDDAS.Les sciences de l'ingénieur basées sur la simulation (Simulation Based Engineering Science, SBES) ont apporté des améliorations majeures dans l'optimisation, le contrôle et l'analyse inverse, menant toutes à une meilleure compréhension de nombreux processus se produisant dans le monde réel. Ces percées notables sont présentes dans une grande variété de secteurs tels que l'aéronautique ou l'automobile, les télécommunications mobiles ou la santé, entre autres. Néanmoins, les SBES sont actuellement confrontées à plusieurs dificultés pour fournir des résultats précis dans des problèmes industriels complexes. Outre les coûts de calcul élevés associés aux applications industrielles, les erreurs introduites par la modélisation constitutive deviennent de plus en plus importantes lorsqu'il s'agit de nouveaux matériaux. Parallèlement, un intérêt sans cesse croissant pour des concepts tels que les données massives (big data), l'apprentissage machine ou l'analyse de données a été constaté. En effet, cet intérêt est intrinsèquement motivé par un développement exhaustif des systèmes d'acquisition et de stockage de données. Par exemple, un avion peut produire plus de 500 Go de données au cours d'un seul vol. Ce panorama apporte une opportunité parfaite aux systèmes d'application dynamiques pilotés par les données (Dynamic Data Driven Application Systems, DDDAS), dont l'objectif principal est de fusionner de manière dynamique des algorithmes de simulation classiques avec des données provenant de mesures expérimentales. Dans ce scénario, les données et les simulations ne seraient plus découplées, mais une symbiose à exploiter permettrait d'envisager des situations jusqu'alors inconcevables. En effet, les données ne seront plus comprises comme un étalonnage statique d'un modèle constitutif donné mais plutôt comme une correction dynamique du modèle dès que les données expérimentales et les simulations auront tendance à diverger. Plusieurs algorithmes numériques seront présentés tout au long de ce manuscrit dont l'objectif principal est de renforcer le lien entre les données et la mécanique computationnelle. La première partie de la thèse est principalement axée sur l'identification des paramètres, les techniques d'analyse des données et les techniques de complétion de données. La deuxième partie est axée sur les techniques de réduction de modèle (MOR), car elles constituent un allié fondamental pour satisfaire les contraintes temps réel découlant du cadre DDDAS.Award-winningPostprint (published version