Laminar Free Convection from a Vertically-Oriented Concentrated Photovoltaic Cell with Uniform Surface Heat Flux

This research project uses field measurements to investigate the cooling of a triple-junction, photovoltaic cell under natural convection when subjected to various amounts of insolation. The team built an experimental apparatus consisting of a mirror and Fresnel lens to concentrate light onto a triple-junction photovoltaic cell, mounted vertically on a copper heat sink. Measurements were taken year-round to provide a wide range of ambient conditions. A surface was then generated, in MATLAB, using Sparrow’s model for natural convection on a vertical plate under constant heat flux. This surface can be used to find the expected operating temperature of a cell at any location, given the ambient temperature and insolation. This research is an important contribution to the industry because it utilizes field data that represents how a cell would react under normal operation. It also extends the use of a well-known model from a one-sun environment to a multi-sun one.

Spectral Frame Analysis and Learning through Graph Structure

This dissertation investigates the connection between spectral analysis and frame theory. When considering the spectral properties of a frame, we present a few novel results relating to the spectral decomposition. We first show that scalable frames have the property that the inner product of the scaling coefficients and the eigenvectors must equal the inverse eigenvalues. From this, we prove a similar result when an approximate scaling is obtained. We then focus on the optimization problems inherent to the scalable frames by first showing that there is an equivalence between scaling a frame and optimization problems with a non-restrictive objective function. Various objective functions are considered, and an analysis of the solution type is presented. For linear objectives, we can encourage sparse scalings, and with barrier objective functions, we force dense solutions. We further consider frames in high dimensions, and derive various solution techniques. From here, we restrict ourselves to various frame classes, to add more specificity to the results. Using frames generated from distributions allows for the placement of probabilistic bounds on scalability. For discrete distributions (Bernoulli and Rademacher), we bound the probability of encountering an ONB, and for continuous symmetric distributions (Uniform and Gaussian), we show that symmetry is retained in the transformed domain. We also prove several hyperplane-separation results. With the theory developed, we discuss graph applications of the scalability framework. We make a connection with graph conditioning, and show the in-feasibility of the problem in the general case. After a modification, we show that any complete graph can be conditioned. We then present a modification of standard PCA (robust PCA) developed by Cand\`es, and give some background into Electron Energy-Loss Spectroscopy (EELS). We design a novel scheme for the processing of EELS through robust PCA and least-squares regression, and test this scheme on biological samples. Finally, we take the idea of robust PCA and apply the technique of kernel PCA to perform robust manifold learning. We derive the problem and present an algorithm for its solution. There is also discussion of the differences with RPCA that make theoretical guarantees difficult.

STRUCTURE AND FUNCTION OF THE GROUP III CHAPERONINS, A UNIQUE CLADE OF PROTEIN FOLDING NANOMACHINES

The survival and descent of cells is universally dependent on maintaining their proteins in a properly folded condition. It is widely accepted that the information for the folding of the nascent polypeptide chain into a native protein is encrypted in the amino acid sequence, and the Nobel Laureate Christian Anfinsen was the first to demonstrate that a protein could spontaneously refold after complete unfolding. However, it became clear that the observed folding rates for many proteins were much slower than rates estimated in vivo. This led to the recognition of required protein-protein interactions that promote proper folding. A unique group of proteins, the molecular chaperones, are responsible for maintaining protein homeostasis during normal growth as well as stress conditions. Chaperonins (CPNs) are ubiquitous and essential chaperones. They form ATP-dependent, hollow complexes that encapsulate polypeptides in two back-to-back stacked multisubunit rings, facilitating protein folding through highly cooperative allosteric articulation. CPNs are usually classified into Group I and Group II. Here, I report the characterization of a novel CPN belonging to a third Group, recently discovered in bacteria. Group III CPNs have close phylogenetic association to the Group II CPNs found in Archaea and Eukarya, and may be a relic of the Last Common Ancestor of the CPN family. The gene encoding the Group III CPN from Carboxydothermus hydrogenoformans and Candidatus Desulforudis audaxviator was cloned in E. coli and overexpressed in order to both characterize the protein and to demonstrate its ability to function as an ATPase chaperone. The opening and closing cycle of the Chy chaperonin was examined via site-directed mutations affecting the ATP binding site at R155. To relate the mutational analysis to the structure of the CPN, the crystal structure of both the AMP-PNP (an ATP analogue) and ADP bound forms were obtained in collaboration with Sun-Shin Cha in Seoul, South Korea. The ADP and ATP binding site substitutions resulted in frozen forms of the structures in open and closed conformations. From this, mutants were designed to validate hypotheses regarding key ATP interacting sites as well as important stabilizing interactions, and to observe the physical properties of the resulting complexes by calorimetry.

Inverse Spectral Methods in Acoustic Normal Mode Velocimetry of High Reynolds Number Spherical Couette Flows

Experimental geophysical fluid dynamics often examines regimes of fluid flow infeasible for computer simulations. Velocimetry of zonal flows present in these regimes brings many challenges when the fluid is opaque and vigorously rotating; spherical Couette flows with molten metals are one such example. The fine structure of the acoustic spectrum can be related to the fluid’s velocity field, and inverse spectral methods can be used to predict and, with sufficient acoustic data, mathematically reconstruct the velocity field. The methods are to some extent inherited from helioseismology. This work develops a Finite Element Method suitable to matching the geometries of experimental setups, as well as modelling the acoustics based on that geometry and zonal flows therein. As an application, this work uses the 60-cm setup Dynamo 3.5 at the University of Maryland Nonlinear Dynamics Laboratory. Additionally, results obtained using a small acoustic data set from recent experiments in air are provided.