Collective robotic behavior poses significant advantages over classical control methods such as system response and robustness. Biological cooperative communities have provided great insights for development of many control algorithms. Localized chemical signaling within bacterial communities is used for directed movement and dynamic density measurements. Both individual and population scale models have been created to adequately model community dynamics. These dynamics, including directed motion due to chemotaxis and density controlled functionality from quorum sensing, are modeled through an individual scale in a community scale environment. This modeling provides both a platform for analyzing the BacteriaBot engineered system as well as inspires decentralized stochastic control techniques for solving bacteria-like collaborative control problems. / Master of Science

Phenomenological material models such as Johnson-Cook plasticity are often used in finite element simulations of large deformation processes at different strain rates and temperatures. Since the material constants that appear in such models depend on the material, experimental data, fitting method, as well as the mathematical representation of strain rate and temperature effects, the predicted material behavior is subject to uncertainty. In this dissertation, evidence theory is used for modeling uncertainty in the material constants, which is represented by separate belief structures that are combined into a joint belief structure and propagated using impact loading simulation of structures. Yager’s rule is used for combining evidence obtained from more than one source. Uncertainty is quantified using belief, plausibility, and plausibility-decision functions. An evidence-based design optimization (EBDO) approach is presented where the nondeterministic response functions are expressed using evidential reasoning. The EBDO approach accommodates field material uncertainty in addition to the embedded uncertainty in the material constants. This approach is applied to EBDO of an externally stiffened circular tube under axial impact load with and without consideration of material field uncertainty caused by spatial variation of material uncertainties due to manufacturing effects. Surrogate models are developed for approximation of structural response functions and uncertainty propagation. The EBDO example problem is solved using genetic algorithms. The uncertainty modeling and EBDO results are presented and discussed.

No description available.

Titanium-7%wt-Aluminum (Ti-7Al) has been of interest to the aerospace industry owing to its good structural and thermal properties. However, extensive research is still needed to study the structural behavior and determine the material properties of Ti-7Al. The homogenized macro-scale material properties are directly related to the crystallographic structure at the micro-scale. Furthermore, microstructural uncertainties arising from experiments and computational methods propagate on the material properties used for designing aircraft components. Therefore, multi-scale modeling is employed to characterize the microstructural features of Ti-7Al and computationally predict the macro-scale material properties such as Young's modulus and yield strength using machine learning techniques. Investigation of microstructural features across large domains through experiments requires rigorous and tedious sample preparation procedures that often lead to material waste. Therefore, computational microstructure reconstruction methods that predict the large-scale evolution of microstructural topology given the small-scale experimental information are developed to minimize experimental cost and time. However, it is important to verify the synthetic microstructures with respect to the experimental data by characterizing microstructural features such as grain size and grain shape. While the relationship between homogenized material properties and grain sizes of microstructures is well-studied through the Hall-Petch effect, the influences of grain shapes, especially in complex additively manufactured microstructure topologies, are yet to be explored. Therefore, this work addresses the gap in the mathematical quantification of microstructural topology by developing measures for the computational characterization of microstructures. Moreover, the synthesized microstructures are modeled through crystal plasticity simulations to determine the material properties. However, such crystal plasticity simulations require significant computing times. In addition, the inherent uncertainty of experimental data is propagated on the material properties through the synthetic microstructure representations. Therefore, the aforementioned problems are addressed in this work by explicitly quantifying the microstructural topology and predicting the material properties and their variations through the development of surrogate models. Next, this work extends the proposed multi-scale models of microstructure-property relationships to magnetic materials to investigate the ferromagnetic-paramagnetic phase transition. Here, the same Ising model-based multi-scale approach used for microstructure reconstruction is implemented for investigating the ferromagnetic-paramagnetic phase transition of magnetic materials. The previous research on the magnetic phase transition problem neglects the effects of the long-range interactions between magnetic spins and external magnetic fields. Therefore, this study aims to build a multi-scale modeling environment that can quantify the large-scale interactions between magnetic spins and external fields. / Doctor of Philosophy / Titanium-Aluminum (Ti-Al) alloys are lightweight and temperature-resistant materials with a wide range of applications in aerospace systems. However, there is still a lack of thorough understanding of the microstructural behavior and mechanical performance of Titanium-7wt%-Aluminum (Ti-7Al), a candidate material for jet engine components. This work investigates the multi-scale mechanical behavior of Ti-7Al by computationally characterizing the micro-scale material features, such as crystallographic texture and grain topology. The small-scale experimental data of Ti-7Al is used to predict the large-scale spatial evolution of the microstructures, while the texture and grain topology is modeled using shape moment invariants. Moreover, the effects of the uncertainties, which may arise from measurement errors and algorithmic randomness, on the microstructural features are quantified through statistical parameters developed based on the shape moment invariants. A data-driven surrogate model is built to predict the homogenized mechanical properties and the associated uncertainty as a function of the microstructural texture and topology. Furthermore, the presented multi-scale modeling technique is applied to explore the ferromagnetic-paramagnetic phase transition of magnetic materials, which causes permanent failure of magneto-mechanical components used in aerospace systems. Accordingly, a computational solution is developed based on an Ising model that considers the long-range spin interactions in the presence of external magnetic fields.

Genome-scale modeling and new strategies for constraining these models were applied in this research to find new insights into cellular metabolism and identify potential metabolic engineering strategies. A newly updated genome-scale model for Clostridium acetobutylicum, iMM864, was constructed, largely based on the previously published iRS552 model. The new model was built using a newly developed genome-scale model database, and updates were derived from new insights into clostridial metabolism. Novel methods of proton-balancing and setting flux (defined as reaction rate (mmol/g biomass/hr)) ratio constraints were applied to create simulations made with the iMM864 model approximate observed experimental results. It was determined that the following constraints must be applied to properly model C. acetobutylicum metabolism: (1) proton-balancing, (2) constraining the specific proton flux (SPF), and (3) installing proper flux ratio constraints. Simulations indicate that the metabolic shift into solventogenesis is not due to optimizing growth at different pH conditions. However, they provide evidence that C. acetobutylicum has developed strictly genetically regulated solventogenic metabolic pathways for the purpose of increasing its surrounding pH to decrease the toxic effects of high proton concentrations. Applying a ratio constraint for the P/O ratio (a measure of aerobic respiratory efficiency) to the iAF1260 genome-scale model of E. coli K12 MG1655 was explored. Relationships were found between: (1) the P/O ratio, (2) the SPF, (3) the growth rate, and (4) the production of acetate. As was expected, higher acetate production correlates with lower P/O ratios, while higher growth correlates with higher P/O ratios. For the first time, a genome-scale model was able to quantify this relationship and targeting both the P/O ratio and the SFP is required to produce an E. coli K12 strain with either (i) maximized growth rate (and minimized acetate production) or (ii) maximized acetate production (at the expense of cell growth). A gene knockout mutant, Î ndh, was created with E. coli BL-21 to study the effects of forcibly higher P/O ratios on growth. The results suggest that a metabolic bottleneck lies with the NADH-1 complex, the NADH dehydrogenase that contributes to the generation of a proton motive force. / Master of Science

The task of thermal management in electrical systems has never been simple and has only become more difficult in recent years as the power electronics industry pushes towards devices with higher power densities. At the Center for Power Electronic Systems (CPES), a new approach to power electronic design is being implemented with the Integrated Power Electronic Module (IPEM). It is believed that an IPEM-based design approach will significantly enhance the competitiveness of the U.S. electronics industry, revolutionize the power electronics industry, and overcome many of the technology limits in today's industry by driving down the cost of manufacturing and design turnaround time. But with increased component integration comes the increased risk of component failure due to overheating. This thesis addresses the issues associated with the thermal management of integrated power electronic devices. Two studies are presented in this thesis. The focus of these studies is on the thermal design of a DC-DC front-end power converter developed at CPES with an IPEM-based approach. The first study investigates how the system would respond when the fan location and heat sink fin arrangement are varied in order to optimize the effects of conduction and forced-convection heat transfer to cool the system. The set-up of an experimental test is presented, and the results are compared to the thermal model. The second study presents an improved methodology for the thermal modeling of large-scale electrical systems and their many subsystems. A zoom-in/zoom-out approach is used to overcome the computational limitations associated with modeling large systems. The analysis performed in this paper was completed using I-DEAS©,, a three-dimensional finite element analysis (FEA) program which allows the thermal designer to simulate the affects of conduction and convection heat transfer in a forced-air cooling environment. / Master of Science

Microstructure reconstruction is a necessary tool for use in multi-scale modeling, as it allows for the analysis of the microstructure of a material without the cost of measuring all of the required data for the analysis. For microstructure reconstruction to be effective, the synthetic microstructure needs to predict what a small sample of measured data would look like on a larger domain. The Markov Random Field (MRF) algorithm is a method of generating statistically similar microstructures for this process. In this work, two key factors of the MRF algorithm are analyzed. The first factor explored is how the base features of the microstructure related to orientation and grain/phase topology information influence the selection of the MRF parameters to perform the reconstruction. The second focus is on the analysis of the numerical uncertainty (epistemic uncertainty) that arises from the use of the MRF algorithm. This is done by first removing the material uncertainty (aleatoric uncertainty), which is the noise that is inherent in the original image representing the experimental data. The epistemic uncertainty that arises from the MRF algorithm is analyzed through the study of the percentage of isolated pixels and the difference in average grain sizes between the initial image and the reconstructed image. This research mainly focuses on two different microstructures, B4C-TiB2 and Ti-7Al, which are a ceramic composite and a metallic alloy, respectively. Both of them are candidate materials for many aerospace systems owing to their desirable mechanical performance under large thermo-mechanical stresses. / Master of Science / Microstructure reconstruction is a necessary tool for use in multi-scale modeling, as it allows for the analysis of the microstructure of a material without the cost of measuring all of the required data for the analysis. For microstructure reconstruction to be effective, the synthetic microstructure needs to predict what a small sample of measured data would look like on a larger domain. The Markov Random Field (MRF) algorithm is a method of generating statistically similar microstructures for this process. In this work, two key factors of the MRF algorithm are analyzed. The first factor explored is how the base features of the microstructures related to orientation and grain/phase topology information influence the selection of the MRF parameters to perform the reconstruction. The second focus is on the analysis of the numerical uncertainty that arises from the use of the MRF algorithm. This is done by first removing the material uncertainty, which is the noise that is inherent in the original image representing the experimental data. This research mainly focuses on two different microstructures, B4C-TiB2 and Ti-7Al, which are a ceramic composite and a metallic alloy, respectively. Both of them are candidate materials for many aerospace systems owing to their desirable mechanical performance under large thermo-mechanical stresses.

An alternative formulation of the governing equations of a dynamical system, called the multi-scale localized perturbation method, is introduced and derived for the purpose of solving complex geophysical flow problems. Simulation variables are decomposed into background and perturbation components, then assumptions are made about the evolution of these components within the context of an environmental flow in order to close the system. Once closed, the original governing equations become a set of one-way coupled governing equations called the "delta form" of the governing equations for short, with one equation describing the evolution of the background component and the other describing the evolution of the perturbation component. One-way interaction which arises due to non-linearity in the original differential equations appears in this second equation, allowing the background fields to influence the evolution of a perturbation. Several solution methods for this system of equations are then proposed. Advantages of the delta form include the ability to specify a complex, temporally- and spatially-varying background field separate from a perturbation introduced into the system, including those created by natural or man-made sources, which enhances visualization of the perturbation as it evolves in time and space. The delta form is also shown to be a tool which can be used to simplify simulation setup. Implementation of the delta form of the incompressible URANS equations with turbulence model and scalar transport within OpenFOAM is then documented, followed by verification cases. A stratified wake collapse case in a domain containing a background shear layer is then presented, showing how complex internal gravity wave-shear layer interactions are retained and easily observed in spite of the variable decomposition. The multi-scale localized perturbation method shows promise for geophysical flow problems, particularly multi-scale simulation involving the interaction of large-scale natural flows with small-scale flows generated by man-made structures. / Master of Science / Natural flows, such as those in our oceans and atmosphere, are seen everywhere and affect human life and structures to an amazing degree. Study of these complex flows requires special care be taken to ensure that mathematical equations correctly approximate them and that computers are programmed to correctly solve these equations. This is no different for researchers and engineers interested in studying how man-made flows, such as one generated by the wake of a plane, wind turbine, cruise ship, or sewage outflow pipe, interact with natural flows found around the world. These interactions may yield complex phenomena that may not otherwise be observed in the natural flows alone. The natural and artificial flows may also mix together, rendering it difficult to study just one of them. The multi-scale localized perturbation method is devised to aid in the simulation and study of the interactions between these natural and man-made flows. Well-known equations of fluid dynamics are modified so that the natural and man-made flows are separated and tracked independently, which gives researchers a clear view of the current state of a region of air or water all while retaining most, if not all, of the complex physics which may be of interest. Once the multi-scale localized perturbation method is derived, its mathematical equations are then translated into code for OpenFOAM, an open-source software toolkit designed to simulate fluid flows. This code is then tested by running simulations to provide a sanity check and verify that the new form of the equations of fluid dynamics have been programmed correctly, then another, more complicated simulation is run to showcase the benefits of the multi-scale localized perturbation method. This simulation shows some of the complex fluid phenomena that may be seen in nature, yet through the multi-scale localized perturbation method, it is easy to view where the man-made flows end and where the natural flows begin. The complex interactions between the natural flow and the artificial flow are retained in spite of separating the flow into two parts, and setting up the simulation is simplified by this separation. Potential uses of the multi-scale localized perturbation method include multi-scale simulations, where researchers simulate natural flow over a large area of land or ocean, then use this simulation data for a second, small-scale simulation which covers an area within the large-scale simulation. An example of this would be simulating wind currents across a continent to find a potential location for a wind turbine farm, then zooming in on that location and finding the optimal spacing for wind turbines at this location while using the large-scale simulation data to provide realistic wind conditions at many different heights above the ground. Overall, the multi-scale localized perturbation method has the potential to be a powerful tool for researchers whose interest is flows in the ocean and atmosphere, and how these natural flows interact with flows created by artificial means.

Ce travail de thèse a porté sur la simulation numérique du comportement thermique et mécanique d'un combustible nucléaire à particules. Il s'agit d'un composite réfractaire constitué d'une matrice de graphite comportant 45 % en fraction volumique de particules sphériquesd'UO2 revêtues de deux couches de pyrocarbone. L'objectif était de développer une modélisationmulti-échelles de ce composite afin d'estimer son comportement moyen, ainsi que les hétérogé-néités des champs mécaniques au sein des constituants. Nous avons modélisé la microstructuredu combustible et généré des échantillons numériques en 3D. Pour cela, des outils de générationde distributions aléatoires de sphères, de maillage et de caractérisation microstructurale, tellela covariance, ont été développés dans le code de calcul Cast3M. Une centaine d'échantillonsnumériques de différentes tailles ont été réalisés. Le comportement thermo-élastique du combustiblea été caractérisé à partir de ces échantillons, à l'aide de calculs de microstructures paréléments finis. Nous avons étudié l'influence de divers paramètres de la modélisation, dont lesconditions aux limites. Nous proposons une méthode pour s'affranchir des effets des conditionsaux limites sur les résultats, appelée méthode d'érosion. Elle s'appuie sur l'analyse des résultatssur un érodé du volume élémentaire. Nous avons alors déterminé les propriétés effectives ducomposite (modules d'élasticité, conductivité thermique, dilatation thermique), ainsi que lesdistributions des champs mécaniques locaux au sein de la matrice. Enfin, nous avons proposéun modèle de changement d'échelles permettant d'obtenir, non seulement les valeurs moyennesdes variables mécaniques dans chaque phase, mais également leurs variances et covariances pourtout chargement macroscopique imposé. Cette approche statistique de changement d'échellespermet ainsi d'estimer la distribution des grandeurs mécaniques au sein de chaque phase ducomposite. / The aim of this work was to perform numerical simulations of the thermal and mechanical behavior of a particle-based nuclear fuel. This is a refractory composite material made of UO2spherical particles which are coated with two layers of pyrocarbon and embedded in a graphitematrix at a high volume fraction (45 %). The objective was to develop a multi-scale modelingof this composite material which can estimate its mean behavior as well as the heterogeneity ofthe local mechanical variables. The first part of this work was dedicated to the modeling of themicrostructure in 3D. To do this, we developed tools to generate random distributions of spheres,meshes and to characterize the morphology of the microstructure towards the finite elementcode Cast3M. A hundred of numerical samples of the composite were created. The secondpart was devoted to the characterization of the thermo-elastic behavior by the finite elementmodeling of the samples. We studied the influence of different modeling parameters, one of themis the boundary conditions. We proposed a method to vanish the boundary conditions effectsfrom the computed solution by analyzing it on an internal sub-volume of the sample obtained byerosion. Then, we determined the effective properties (elastic moduli, thermal conductivity andthermal expansion) and the stress distribution within the matrix. Finally, in the third part weproposed a multi-scale modeling to determine the mean values and the variance and covarianceof the local mechanical variables for any macroscopic load. This statistical approach have beenused to estimate the intra-phase distribution of these variables in the composite material.

Scale modeling has been commonly used for architectural acoustics but use in other noise control areas is nominal. Acoustic scale modeling theory is first reviewed and then feasibility for small-scale applications, such as is common in the electronics industry, is investigated. Three application cases are used to examine the viability. In the first example, a scale model is used to determine the insertion loss of a rectangular barrier. In the second example, the transmission loss through parallel tubes drilled through a cylinder is measured and results are compared to a 2.85 times scale model with good agreement. The third example is a rectangular cuboid with a smaller cylindrical well bored into it. A point source is placed above the cuboid. The transfer function was measured between positions on the top of the cylinder and inside of the cylindrical well. Treatments were then applied sequentially including a cylindrical barrier around the well, a membrane cover over the opening, and a layer of sound absorption over the well. Results are compared between the full scale and a 5.7 times scale model and correlation between the two is satisfactory.