The Engage to Excel (PCAST) report, the National Research Council's Framework for K-12 Science Education, and the Next Generation Science Standards all call for transforming the physics classroom into an environment that teaches students real scientific practices. This work describes the early stages of one such attempt to transform a high school physics classroom. Specifically, a series of model-building and computational modeling exercises were piloted in a ninth grade Physics First classroom. Student use of computation was assessed using a proctored programming assignment, where the students produced and discussed a computational model of a baseball in motion via a high-level programming environment (VPython). Student views on computation and its link to mechanics was assessed with a written essay and a series of think-aloud interviews. This pilot study shows computation's ability for connecting scientific practice to the high school science classroom.

The search for heat regenerators is currently very important due to the amount of wasted heat produced in different human activities. Thermoelectric materials have emerged as a possible solution to the world’s demand and reuse of energy. Recent advances have included the development of materials with tailored phonon properties, including localized "rattling" oscillator modes. In addition a number of interesting physical properties have emerged in rattling systems. This dissertation reports a study of several such systems, experimentally and computationally. Experiments performed include XRD, electron micro-probe, electrical and thermal conductivity, Seebeck coefficient measurements, dc magnetization, dc susceptibility and NMR. In the computational side several ab-initio models have been considered to understand the structural, vibrational and magnetic properties observed in these compounds. Among the studied compounds, the Fe-Al-Zn materials showed interesting magnetic properties combined with anomalous vibrational behavior in a chain geometry. Computational results indicated that the moment is affected by Fe antisites, but also the neighbor configuration contributes to it. Al-V-La is an example of a classical Einstein oscillator material. These properties are related to the existence of loose atoms inside the material. A purely computational study on these materials denoted the existence of two weakly bonded sites. The clathrate structural results from first-principles considerations elucidated the preferred structural configurations in several clathrates. This included Ba-Cu-Ge clathrates, where it was confirmed that the compound follows the Zintl electron counting balance. Also the bonding inside these materials was studied to address the binding of the local-oscillator atoms within the material. For Ba-Ga-Sn clathrates an unusual dimorphism was studied, with both of the two different types of structures investigated. For type-I Ba8Ga16Sn30 the preferred configuration was obtained from NMR lineshape simulations and energy considerations. For the type-VIII Ba8Ga16Sn30 the experimental thermoelectric properties were analyzed in conjunction with computational modeling. Finally in Ba-Al-Ge clathrates the local environments, preferred configuration and vacancy formation were clarified. This included an extensive experimental and computational study on Ba8AlxGe46-x-y2(box)y systems. The different local Al environments were elucidated, with the location of vacancies influencing the surroundings. Also the correlation between the Al substitution and number of vacancies was studied.

By a dominant account, memory is composed of multiple storage systems, each operating according to unique principles. By an alternative account, memory is a single storage system and operates according to a single set of principles. Selective memory impairments in amnesia serve as the primary evidence for the multiple-system perspective. This thesis reports a critical appraisal of the multiple-system perspective using a combination of computational and empirical methods. In the computational analysis, I adopt the Holographic Exemplar Model, a single-system model of memory based on Hintzman’s (1986) classic MINERVA2 model. I simulate amnesia by manipulating the quality with which items are encoded in memory. In the empirical analysis, I simulate amnesia by manipulating peoples’ quality of encoding by limiting the time given to study stimuli. Simulations 1-2 and Experiments 1-2 simulate a dissociation between classification and recognition. All four analyses are consistent with the original results. Simulation 3 and Experiment 3 simulate single and double dissociations between tachistoscopic identification and recognition. The analyses were consistent with the single but not double dissociation. Simulation 4 and Experiment 4 simulate a dissociation among word-stem completion, cued recall, and recognition. Both analyses were only partially consistent with the original results, representing a failure overall. Simulation 5 and Experiment 5 derived a novel prediction from artificial grammar learning, predicting a non-dissociation between string completion and recognition. The mixed results provide some support for a single-system account of memory and opens opportunities for future work. I argue that the analysis is best considered in convergence with previous work moving toward a more integrated account of memory / February 2017

This work is concerned with the prediction of short (SGF) and long glass fiber (LGF) orientation in a center-gated disk and end-gated plaque injection molding test geometry using a simulation method that has not been attempted previously. Previous work has used assumptions to simplify the fiber orientation geometry (assuming a thin cavity) or flow field (neglecting fountain flow and entry regions). LGF orientation is predicted in a center-gated disk injection molding geometry including the advancing front and simulating the sprue and gate region (SGM method) so that no assumption about fiber orientation at the mold entrance has to be made. Using a semi-flexible fiber model and orientation parameters obtained through rheology, increased agreement was found between predicted and experimentally obtained values of orientation using the SGM method and a semi-flexible fiber model than was found using a Hele-Shaw approximation. The SGM method was applied to the end-gated plaque to predict SGF orientation both along and away from the centerline using an objective (reduced strain closure model) and non-objective (strain reduction factor model) orientation model. The predicted values of the strain reduction factor model showed reasonable agreement with experimentally obtained values of orientation throughout the three-dimensional cavity when using orientation parameters fit to experimental orientation data. Furthermore it was found that the objective model predicted results very similar to the non-objective model suggesting that objectivity may not play a role in predicting orientation in more complex geometries such as an end-gated plaque. Finally, the SGM method was applied to the end-gated plaque geometry to predict LGF orientation using a rigid and semi-flexible fiber model. It was found that the SGM method and the semi-flexible fiber model provides orientation predictions that are similar to experimentally obtained values of orientation. / Ph. D.

In this thesis, I have used a combination of computational models such as mean field and spikingnetwork simulations to study various sub-circuits of basal ganglia. I first studied the striatum(chapter 2), which is the input nucleus of basal ganglia. The two types of Medium SpinyNeurons (MSNs), D1 and D2-MSNs, together constitute 98% of the neurons in striatum. Thecomputational models so far have treated striatum as a homogenous unit and D1 and D2 MSNs asinterchangeable subpopulations. This implied that a bias in a Go/No-Go decision is enforced viaexternal agents to the striatum (eg. cortico-striatal weights), thereby assigning it a passive role.New data shows that there is an inherent asymmetry in striatal circuits. In this work, I showedthat striatum due to its asymmetric connectivity acts as a decision transition threshold devicefor the incoming cortical input. This has significant implications on the function of striatum asan active participant in influencing the bias towards a Go/No-Go decision. The striatal decisiontransition threshold also gives mechanistic explanations for phenomena such as L-Dopa InducedDyskinesia (LID), DBS-induced impulsivity, etc. In chapter 3, I extend the mean field model toinclude all the nuclei of basal ganglia to specifically study the role of two new subpopulationsfound in GPe (Globus Pallidus Externa). Recent work shows that GPe, also earlier consideredto be a homogenous nucleus, has at least two subpopulations which are dichotomous in theiractivity with respect to the cortical Slow Wave (SWA) and beta activity. Since the data for thesesubpopulations are missing, a parameter search was performed for effective connectivities usingGenetic Algorithms (GA) to fit the available experimental data. One major result of this studyis that there are various parameter combinations that meet the criteria and hence the presenceof functional homologs of the basal ganglia network for both pathological (PD) and healthynetworks is a possibility. Classifying all these homologous networks into clusters using somehigh level features of PD shows a large variance, hinting at the variance observed among the PDpatients as well as their response to the therapeutic measures. In chapter 4, I collaborated on aproject to model the role of STN and GPe burstiness for pathological beta oscillations as seenduring PD. During PD, the burstiness in the firing patterns of GPe and STN neurons are shownto increase. We found that in the baseline state, without any bursty neurons in GPe and STN,the GPe-STN network can transition to an oscillatory state through modulating the firing ratesof STN and GPe neurons. Whereas when GPe neurons are systematically replaced by burstyneurons, we found that increase in GPe burstiness enforces oscillations. An optimal % of burstyneurons in STN destroys oscillations in the GPe-STN network. Hence burstiness in STN mayserve as a compensatory mechanism to destroy oscillations. We also propose that bursting inGPe-STN could serve as a mechanism to initiate and kill oscillations on short time scales, asseen in the healthy state. The GPe-STN network however loses the ability to kill oscillations inthe pathological state. / <p>QC 20160509</p>

Insects use an estimate of the angular speed of the visual image across the eye (termed optic flow) for a wide variety of behaviors including flight speed control, visual navigation, depth estimation, grazing landings, and visual odometry. Despite the behavioral importance of visual speed estimation, the neuronal mechanisms by which the brain extracts optic flow information from the retinal image remain unknown. This dissertation investigates the underlying neuronal mechanisms of visual speed estimation via three complementary strategies: the development of neuronally-based computational models, testing of the models in a behavioral simulation framework, and behavioral experiments using bumblebees. Using these methods I demonstrate the sufficiency of two non-directional models of motion detection for reproducing real-world, speed dependent behaviors, propose potential neuronal circuits by which these models may be physiologically implemented, and predict the expected responses of these neurons to a range of visual stimuli.

Cold Spray is a solid-state additive manufacturing process that uses metallic feedstock powders to create layers on a substrate through plastic deformation. This process can be used for the repair of mechanical parts in the aerospace industry as well as for structural applications. Aluminum alloy powders, including Al 6061, 7075, 2024, and 5056, are typically used in this process as feedstock material. Since this process takes place all in the solid state, the properties and microstructure of the initial feedstock powder directly influence the properties of the final consolidated Cold Spray part. Given this, it is important to fully understand the internal powder microstructure, specifically the secondary phases as a function of thermal treatment. This work focuses on the understanding of the internal microstructure of Al 6061, 7075, 2024, and 5056 through the use of light microscopy, scanning electron microscopy, transmission electron microscopy, energy dispersive x-ray spectroscopy, electron backscatter diffraction, and differential scanning calorimetry. Thermodynamic models were used to predict the phase stability in these powders and were calibrated using the experimental results to give a more complete understanding of the phase transformations during thermal processing.

A microwave heat exchanger (MHE) is a device which converts microwave (MW) energy into usable form of heat energy. The working principle of the MHE is based on a collective effect of electromagnetic wave propagation, heat transfer and fluid flow, so the development of an efficient device requires complicated experimentation with processes of different physical nature. A peculiar phenomenon making the design of MHE even more challenging is extit{thermal runaway}, a nonlinear phenomenon in which a small increase in the input power gives rise to a large increase in temperature. Such high temperature may result in material damage through excessive thermal expansion, cracking, or melting. In this Thesis, we report on an initial phase in the development of a computational model which may help clarify complicated interaction between nonlinear phenomena that might be difficult to comprehend and control experimentally. We present a 2D multiphysics model mimicking operation of a layered MHE that simulates the nonlinear interaction between MW, thermal, and fluid flow phenomena involved in the operation of the MHE. The model is built for a triple layered (fluid-ceramic-fluid) MHE and is capable of capturing the S- and SS-profiles of power response curve which determines steady-state temperature solution as a function of incident power. The model is implemented on the platform of the COMSOL Multiphysics modeling software. We show that a MHE with particular thickness and dielectric properties of the layers can operate efficiently by keeping temperatures during thermal runaway under control. Overall temperatures increase rapidly as soon as the local maximum temperature reaches a critical value. This condition is held true both in absence and in presence of fluid flow. It is demonstrated that the efficiency of the MHE dramatically increases when thermal runaway is achieved. As the amount of heat energy, which is being transferred to the fluid from the heated dielectric, increases, incident power required to achieve thermal runaway also increases. It is also shown that, with appropriate length of the layered MHE, thermal runaway can be achieved at a lower power level. While the model developed in this Thesis studies the basic operation of a three layered MHE, it can further be developed to investigate optimum design parameters of the MHE of other structures so that maximum thermal efficiency is achieved.

Various types of mammalian cells exhibit the remarkable ability to adapt to external applied mechanical stresses and strains. This ability allows cells to maintain a stable endogenous mechanical tension at a preferred (homeostatic) level, which is of great importance for normal physiological function of cells and tissues, and for a protection from various diseases, including atherosclerosis and cancer. Previous studies have shown that the cell ability to maintain tensional homeostasis is cell type-dependent. For example, isolated endothelial cell cannot maintain tensional homeostasis, whereas clusters of endothelial cells can, more so the greater the size of the cluster is. On the other hand, cell clustering does not affect tensional homeostasis of fibroblasts and vascular smooth muscle cells. Underlying mechanisms for these behaviors of different cell types are largely unknown. In this study, we combined theoretical analysis and mathematical modeling to investigate several biophysical factors, including cluster shape and size, magnitude and dynamics of cellular traction forces, and applied shear forces that may influence tensional homeostasis in cells and clusters. We developed two-dimensional models of cells clusters of different shapes and sizes. To simulate temporal fluctuations of cell-extracellular matrix traction forces, we used a Monte Carlo approach. We also applied physical forces obtained from previous experimental measurements to the models. Results of the analysis and modeling revealed that cluster size, magnitude and dynamics of focal adhesion traction forces have a major influence on traction field variability, whereas the influence of cluster shape appears to be minor. The dynamics of traction forces seems to be related to cell types and it can explain why in certain cell types, such as endothelial cells, cell clustering promotes tensional homeostasis, whereas in other cell types, such as fibroblasts, clustering has virtually no effect on homeostasis. To further investigate mechanisms that may affect tensional homeostasis, we investigated the effect of applied steady shear stress on the traction field dynamics of endothelial cells and clusters. We applied steady shear stress to our two-dimensional model of cell clusters and then computed ensuing changes in the traction force variability. These simulations mimicked the effect of flow-induced shear stress on tensional homeostasis of endothelial cells and clusters. We found that under steady shear stress, temporal fluctuations of the traction field of endothelial cells became attenuated. This result agrees with the viewpoint that steady shear flow promotes tensional homeostasis in the endothelium. Together, results of this study advance our understanding of biophysical mechanisms that contribute to the cell ability to maintain tensional homeostasis. Furthermore, these results will help us to modify our current experimental procedures, as well as to design new experiments for our investigation of tensional homeostasis. / 2020-10-22T00:00:00Z

Lithium ion battery systems are promising solutions to current energy storage needs due to their high operating voltage and capacity. Numerous efforts have been conducted to model these systems in order to aid the design process and avoid expensive and time consuming prototypical experiments. Of the numerous processes occurring in these systems, solid state transport in particular has drawn a large amount of attention from the research community, as it tends to be one of the rate limiting steps in lithium ion battery performance. Recent studies have additionally indicated that purposeful design of battery electrodes using 3D microstructures offers new freedoms in design, better use of available cell area, and increased battery performance. The following study is meant to serve as a first principles investigation into the behaviors of 3D electrode architectures by monitoring concentration and cycle behaviors under realistic operating conditions. This was accomplished using computational tools to model the solid state diffusion behavior in several generated electrode morphologies. Developed computational codes were used to generate targeted structures under prescribed conditions of particle shape, size, and overall morphology. The diffusion processes in these morphologies were simulated under conditions prescribed from literature. Primary results indicate that parameters usually employed to describe electrode geometry, such as volume to surface area ratio, cannot be solely relied upon to predict or characterize performance. Additionally, the interaction between particle shapes implies some design aspects that may be exploited to improve morphology behavior. Of major importance is the degree of particle isolation and overlap in 3D architectures, as these govern gradient development and lithium depletion within the electrode structures. The results of this study indicate that there are optimum levels of these parameters, and so purposeful design must make use of these behaviors.