In recent years, the prevalence and application of composite materials has exploded. Due to the demands of commercial transportation, the aviation industry has taken a leading role in the integration of composite structures. Among the leading concepts to develop lighter, more fuel-efficient commercial transport is the Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) concept. The highly integrated structure of PRSEUS allows pressurized, non-circular fuselage designs to be implemented, enabling the feasibility of Hybrid Wing Body (HWB) aircraft. In addition to its unique fabrication process, the through-thickness stitching utilized by PRSEUS overcomes the low post-damage strength present in typical composites. Although many proof-of-concept tests have been performed that demonstrate the potential for PRSEUS, efficient computational tools must be developed before the concept can be commercially certified and implemented. In an attempt to address this need, a comprehensive modeling approach is developed that investigates PRSEUS at multiple scales. The majority of available experiments for comparison have been conducted at the coupon level. Therefore, a computational methodology is progressively developed based on physically realistic concepts without the use of tuning parameters. A thorough verification study is performed to identify the most effective approach to model PRSEUS, including the effect of element type, boundary conditions, bonding properties, and model fidelity. Using the results of this baseline study, a high fidelity stringer model is created at the component scale and validated against the existing experiments. Finally, the validated model is extended to larger scales to compare PRSEUS to the current state-of-the-art. Throughout the current work, the developed methodology is demonstrated to make accurate predictions that are well beyond the capability of existing predictive models. While using commercially available predictive tools, the methodology developed herein can accurately predict local behavior up to and beyond failure for stitched structures such as PRSEUS for the first time. Additionally, by extending the methodology to a large scale fuselage section drop scenario, the dynamic behavior of PRSEUS was investigated for the first time. With the predictive capabilities and unique insight provided, the work herein may serve to benefit future iteration of PRSEUS as well as certification by analysis efforts for future airframe development. / PHD

Dust is a detrimental, but unavoidable, consequence of any mining process. It is particularly problematic in underground coal mining, where respirable coal dust poses the potential health risk of coal workers’ pneumoconiosis (CWP). Float dust, if not adequately diluted with rock dust, also creates the potential for a dust explosion initiated by a methane ignition. Furthermore, recently promulgated dust regulations for lowering a miner’s exposure to respirable coal dust will soon call for dramatic improvements in dust suppression and capture. Computational fluid dynamics (CFD) results are presented for a research project with the primary goal of applying a flooded-bed dust scrubber, with high capture and cleaning efficiencies, to a Joy 7LS longwall shearer operating in a 7-ft (2.1 m) coal seam. CFD software, Cradle is used to analyze and evaluate airflow patterns and dust concentrations, under various arrangements and conditions, around the active mining zone of the shearer for maximizing the capture efficiency of the scrubber.

Inflatable Aerodynamic Decelerators (IADs) are a candidate technology NASA began investigating in the late 1960’s. Compared to supersonic parachutes, IADs represent a decelerator option capable of operating at higher Mach numbers and dynamic pressures. IADs have seen a resurgence in interest from the Entry, Descent, and Landing (EDL) community in recent years. The NASA Space Technology Roadmap (STR) highlights EDL systems, as well as, Materials, Structures, Mechanical Systems, and Manufacturing (MSMM) as key Technology Areas for development in the future; recognizing deployable decelerators, flexible material systems, and computational design of materials as essential disciplines for development. This investigation develops a multi-scale flexible material modeling approach that enables efficient high-fidelity IAD design and a critical understanding of the new materials required for robust and cost effective qualification methods. The approach combines understanding of the fabric architecture, analytical modeling, numerical simulations, and experimental data. This work identifies an efficient method that is as simple and as fast as possible for determining IAD material characteristics while not utilizing complicated or expensive research equipment. This investigation also recontextualizes an existing mesomechanical model through validation for structures pertaining to the analysis of IADs. In addition, corroboration and elaboration of this model is carried out by evaluating the effects of varying input parameters. Finally, the present investigation presents a novel method for numerically determining mechanical properties. A sub-scale section that captures the periodic pattern in the material (unit cell) is built. With the unit cell, various numerical tests are performed. The effective nonlinear mechanical stiffness matrix is obtained as a function of elemental strains through correlating the unit cell force-displacement results with a four node membrane element of the same size. Numerically determined properties are validated for relevant structures. Optical microscopy is used to capture the undeformed geometry of the individual yarns.

There is a lack of fundamental studies by means of state of the art numerical and scale modeling techniques scrutinizing the theoretical and technical aspect of air table separators as well as means to comprehend and improve the efficiency of the process. The dissertation details the development of a workable empirical model, a numerical model and a scale model to demonstrate the use of a laboratory air table unit. The modern air-based density separator achieves effective density-based separation for particle sizes greater than 6 mm. Parametric studies with the laboratory scale unit using low rank coal have demonstrated the applicability with regards to finer size fractions of the range 6 mm to 1 mm. The statistically significant empirical models showed that all the four parameters, i.e, blower and table frequency, longitudinal and transverse angle were significant in determining the separation performance. Furthermore, the tests show that an increase in the transverse angle increased the flow rate of solids to the product end and the introduction of feed results in the dampening of airflow at the feed end. The higher table frequency and feed rate had a detrimental effect on the product yield due to low residence time of particle settlement. The research further evaluated fine particle upgrading using various modeling techniques. The numerical model was evaluated using K-Epsilon and RSM turbulence formulations and validated using experimental dataset. The results prove that the effect of fine coal vortices forming around the riffles act as a transport mechanism for higher density particle movement across the table deck resulting in 43% displacement of the midlings and 29% displacement of the heavies to the product side. The velocity and vector plots show high local variance of air speeds and pressure near the feed end and an increase in feed rate results in a drop in deshaling capability of the table. The table was further evaluated using modern scale-modeling concepts and the scaling laws indicated that the vibration velocity has an integral effect on the separation performance. The difference between the full-scale model and the scaled prototype was 3.83% thus validating the scaling laws.

Embryonic development depends on the precise coordination of cell fate specification, patterning and morphogenesis. Although great strides have been made in the molecular understanding of each of these processes, how their interplay governs the formation of complex tissues remains poorly understood. New techniques for experimental manipulation and image quantification enable the study of development in unprecedented detail, resulting in new hypotheses on the interactions between known components. By expressing these hypotheses in terms of rules and equations, computational modeling and simulation allows one to test their consistency against experimental data. However, new computational methods are required to represent and integrate the network of interactions between gene regulation, signaling and biomechanics that extend over the molecular, cellular and tissue scales. In this thesis, I present a framework that facilitates computational modeling of multiscale multicellular systems and apply it to investigate pancreatic development and the formation of vascular networks. This framework is based on the integration of discrete cell-based models with continuous models for intracellular regulation and intercellular signaling. Specifically, gene regulatory networks are represented by differential equations to analyze cell fate regulation; interactions and distributions of signaling molecules are modeled by reaction-diffusion systems to study pattern formation; and cell-cell interactions are represented in cell-based models to investigate morphogenetic processes. A cell-centered approach is adopted that facilitates the integration of processes across the scales and simultaneously constrains model complexity. The computational methods that are required for this modeling framework have been implemented in the software platform Morpheus. This modeling and simulation environment enables the development, execution and analysis of multi-scale models of multicellular systems. These models are represented in a new domain-specific markup language that separates the biological model from the computational methods and facilitates model storage and exchange. Together with a user-friendly graphical interface, Morpheus enables computational modeling of complex developmental processes without programming and thereby widens its accessibility for biologists. To demonstrate the applicability of the framework to problems in developmental biology, two case studies are presented that address different aspects of the interplay between cell fate specification, patterning and morphogenesis. In the first, I focus on the interplay between cell fate stability and intercellular signaling. Specifically, two studies are presented that investigate how mechanisms of cell-cell communication affect cell fate regulation and spatial patterning in the pancreatic epithelium. Using bifurcation analysis and simulations of spatially coupled differential equations, it is shown that intercellular communication results in a multistability of gene expression states that can explain the scattered spatial distribution and low cell type ratio of nascent islet cells. Moreover, model analysis shows that disruption of intercellular communication induces a transition between gene expression states that can explain observations of in vitro transdifferentiation from adult acinar cells into new islet cells. These results emphasize the role of the multicellular context in cell fate regulation during development and may be used to optimize protocols for cellular reprogramming. The second case study focuses on the feedback between patterning and morphogenesis in the context of the formation of vascular networks. Integrating a cell-based model of endothelial chemotaxis with a reaction-diffusion model representing signaling molecules and extracellular matrix, it is shown that vascular network patterns with realistic morphometry can arise when signaling factors are retained by cell-modified matrix molecules. Through the validation of this model using in vitro assays, quantitative estimates are obtained for kinetic parameters that, when used in quantitative model simulations, confirm the formation of vascular networks under measured biophysical conditions. These results demonstrate the key role of the extracellular matrix in providing spatial guidance cues, a fact that may be exploited to enhance vascularization of engineered tissues. Together, the modeling framework, software platform and case studies presented in this thesis demonstrate how cell-centered computational modeling of multi-scale and multicellular systems provide powerful tools to help disentangle the complex interplay between cell fate specification, patterning and morphogenesis during embryonic development.

"In warehouse storage applications, it is important to classify the burning behavior of commodities and rank them according to material flammability for early fire detection and suppression operations. In this study, the large-scale effects of warehouse fires are decoupled into separate processes of heat and mass transfer. As a first step, two nondimensional parameters are shown to govern the physical phenomena at the large-scale, a mass transfer number, and the soot yield of the fuel which controls the radiation observed in the large-scale. In this study, a methodology is developed to obtain a mass-transfer parameter using mass-loss (burning rate) measurements from bench-scale tests. Two fuels are considered, corrugated cardboard and polystyrene. Corrugated cardboard provides a source of flaming combustion in a warehouse and is usually the first item to ignite and sustain flame spread. Polystyrene is typically used as the most hazardous product in large-scale fire testing. A mixed fuel sample (corrugated cardboard backed by polystyrene) was also tested to assess the feasibility of ranking mixed commodities using the bench-scale test method. The nondimensional mass transfer number was then used to model upward flame propagation on 20-30 foot stacks of Class III commodity consisting of paper cups packed in corrugated cardboard boxes on rack-storage. Good agreement was observed between the model and large-scale experiments during the initial stages of fire growth."

Les propriétés effectives mécaniques et physiques des matériaux hétérogènes dépendent d'une part de leurs constituants, mais peuvent également être fortement modifiées par leur répartition géométrique à l'échelle de la microstructure. L'optimisation topologique a pour but de définir la répartition optimale de matière dans une structure en vue de maximiser un ou plusieurs objectifs tels que les propriétés mécaniques sous des contraintes telles que la masse de matière. Récemment, les développements rapides de l'impression 3D ou d'autres techniques de fabrication additive ont rendu possible la fabrication de matériaux avec des microstructures "à la demande", ouvrant de nouvelles perspectives inédites pour la conception de matériaux. Dans ce contexte, les objectifs de cette thèse sont de développer des outils de modélisation et de simulation numériques pour concevoir des matériaux et des structures hétérogènes ayant des propriétés optimisées basés sur l'optimisation topologique. Plus précisément, nous nous intéressons aux points suivants. Premièrement, nous proposons des contributions à l'optimisation topologique à une seule échelle. Nous présentons tout d'abord une nouvelle méthode d'optimisation topologique avec évolution pour la conception de structures continues par description lisse de bords. Nous introduisons également deux techniques d'homogénéisation topologique pour la conception de microstructures possédant des propriétés effectives extrêmes et des « méta propriétés » (coefficient de Poisson négatif).Dans une seconde partie, des techniques multi échelle basées sur l'optimisation topologique sont développées. Nous proposons d'une part une approche concourante de structures hétérogènes dont les microstructures peuvent posséder plus de deux matériaux. Nous développons ensuite une approche d'optimisation topologique dans un cadre d'homogénéisation pour des échelles faiblement séparées, induisant des effets de gradient. Enfin dans une troisième partie, nous développons l'optimisation topologique pour maximiser la résistance à la fracture de structures ou de matériaux hétérogènes. La méthode de champs de phase pour la fracture est combinée à la méthode BESO pour concevoir des microstructures permettant d'augmenter fortement la résistance à la rupture. La technique prend en compte l'initiation, la propagation et la rupture complète de la structure / Mechanical and physical properties of complex heterogeneous materials are determined on one hand by the composition of their constituents, but can on the other hand be drastically modified by their microstructural geometrical shape. Topology optimization aims at defining the optimal structural or material geometry with regards to specific objectives under mechanical constraints like equilibrium and boundary conditions. Recently, the development of 3D printing techniques and other additive manufacturing processes have made possible to manufacture directly the designed materials from a numerical file, opening routes for totally new designs. The main objectives of this thesis are to develop modeling and numerical tools to design new materials using topology optimization. More specifically, the following aspects are investigated. First, topology optimization in mono-scale structures is developed. We primarily present a new evolutionary topology optimization method for design of continuum structures with smoothed boundary representation and high robustness. In addition, we propose two topology optimization frameworks in design of material microstructures for extreme effective elastic modulus or negative Poisson's ratio. Next, multiscale topology optimization of heterogeneous materials is investigated. We firstly present a concurrent topological design framework of 2D and 3D macroscopic structures and the underlying three or more phases material microstructures. Then, multiscale topology optimization procedures are conducted not only for heterogeneous materials but also for mesoscopic structures in the context of non-separated scales. A filter-based nonlocal homogenization framework is adopted to take into account strain gradient. Finally, we investigate the use of topology optimization in the context of fracture resistance of heterogeneous structures and materials. We propose a first attempt for the extension of the phase field method to viscoelastic materials. In addition, Phase field methods for fracture able to take into account initiation, propagation and interactions of complex both matrix and interfacial micro cracks networks are adopted to optimally design the microstructures to improve the fracture resistance

La corrosion des aciers est l’une des principales causes de dégradation des ouvrages en béton armé, notamment en façade maritime. Cette dégradation est due à la diffusion d’ions chlorures qui peut se produire dans les ouvrages immergés dans l’eau de mer, ceux subissant des cycles de marnage et également ceux soumis aux embruns marins. La corrosion peut être également due au processus de carbonatation du béton, responsable de la baisse du pH et par conséquent de la dépassivation des aciers. Cette thèse propose un modèle physico-chimique pour décrire la penetration des agents agressifs dans la première phase de la corrosion des aciers. Cette phase dite « incubation » correspond à la phase pendant laquelle le transfert des espèces agressives se produit dans le béton. Afin d’identifier clairement les paramètres de transport de ce modèle macroscopique une démarche multi-échelle est mise en oeuvre pour rendre compte de la diffusion dans les bétons. Cette démarche repose sur une description détaillée de la microstructure du béton (constituants et morphologie) par des méthodes de changement d’échelle. Cette approche se veut la plus exhaustive possible avec la prise en compte explicite de l’influence du rapport eau sur liant de la pâte de ciment, des propriétés des granulats et de la zone de transition entre la pâte et les granulats. Après identification de tous les paramètres d’entrée, le modèle physico-chimique macroscopique est utilisé pour évaluer la durée de vie d’un béton soumis au couplage carbonatation -chlorures en milieu insaturé. Suite à une validation du modèle par comparaison à des résultats expérimentaux, plusieurs applications de ce modèle sont présentées. / The corrosion of steels is one of the principal causes of degradation of reinforced concrete structures, especially in front of the sea. This degradation is due to the diffusion of chloride ions that can occur in the structures immersed in the seawater, those undergoing tidal cycles and also those subjected to the sea spray. The corrosion can also be due to the carbonation process of the concrete, which is responsible for decreasing of the pH and consequently the depassivation of the steels. This thesis proposes a physicochemical model to describe the penetration of aggressive agents in the first period of steel corrosion. This so-called « incubation » period corresponds to the period during which the transfer of aggressive species occurs in the concrete. In order to clearly identify the transport parameters of thismacroscopic model, a multi-scale approach is implemented to consider the diffusion in concretes. This approach is based on taking into account the detailed description of the microstructure of the concrete (constituents and morphology) by upscaling methods. This multi-scale approach is intended to be the most comprehensive with the consideration of the influence of the water-to-binder ratio of the cement paste, the properties of the aggregates and the transition zone between the cement paste and the aggregates. After identifying all the input parameters, the macroscopic physico-chemical model is used to evaluate the service life of a concrete subjected to coupling carbonation -chlorides in unsaturated medium. Following a validation of the model compared to experimental results, several applications of this model are presented.

Remote sensors and unmanned aerial vehicles (UAVs) have the potential to dramatically improve infrastructure health monitoring in terms of accuracy of the information and frequency of data collection. UAV automation has made significant progress but that automation is also creating vast amounts of data that needs to be processed into actionable information. A key aspect of this work is the optimization (not just automation) of data collection from UAVs for targeted planning of mission objectives. This work investigates the use of camera planning for Structure from Motion for 3D modeling of infrastructure. Included in this thesis is a novel multi-scale view-planning algorithm for autonomous targeted inspection. The method presented reduced the number of photos needed and therefore reduced the processing time while maintaining desired accuracies across the test site. A second focus in this work investigates various set covering problem algorithms to use for selecting the optimal camera set. The trade-offs between solve time and quality of results are explored. The Carousel Greedy algorithm is found to be the best method for solving the problem due to its relatively fast solve speeds and the high quality of the solutions found. Finally, physical flight tests are used to demonstrate the quality of the method for determining coverage. Each of the set covering problem algorithms are used to create a camera set that achieves 95% coverage. The models from the different camera sets are comparable despite having a large amount of variability in the camera sets chosen. While this study focuses on multi-scale view planning for optical sensors, the methods could be extended to other remote sensors, such as aerial LiDAR.

Remote sensors and unmanned aerial vehicles (UAVs) have the potential to dramatically improve infrastructure health monitoring in terms of accuracy of the information and frequency of data collection. UAV automation has made significant progress but that automation is also creating vast amounts of data that needs to be processed into actionable information. A key aspect of this work is the optimization (not just automation) of data collection from UAVs for targeted planning of mission objectives. This work investigates the use of camera planning for Structure from Motion for 3D modeling of infrastructure. Included in this thesis is a novel multi-scale view-planning algorithm for autonomous targeted inspection. The method presented reduced the number of photos needed and therefore reduced the processing time while maintaining desired accuracies across the test site. A second focus in this work investigates various set covering problem algorithms to use for selecting the optimal camera set. The trade-offs between solve time and quality of results are explored. The Carousel Greedy algorithm is found to be the best method for solving the problem due to its relatively fast solve speeds and the high quality of the solutions found. Finally, physical flight tests are used to demonstrate the quality of the method for determining coverage. Each of the set covering problem algorithms are used to create a camera set that achieves 95% coverage. The models from the different camera sets are comparable despite having a large amount of variability in the camera sets chosen. While this study focuses on multi-scale view planning for optical sensors, the methods could be extended to other remote sensors, such as aerial LiDAR.