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Thermoelastic Deflections of Thin-Shell Composite Space Structures
Get PDFAs space structures become larger, lighter, and deployable, thermal deflections induced by sunlight become a significant source of structural inaccuracy and even spacecraft vibration. Studying these deflections is notoriously difficult: analytical solutions rapidly become intractable, experiments under vacuum and cooling are low-visibility and expensive, and multiphysics finite-element simulations are computationally demanding and usually don’t account for coupled thermo-structural analyses and/or changing radiation view factors.
This work demonstrates key improvements in experimental methods and thermo-structural simulation of these thermal deflections. First, simultaneous full-field measurements of structural temperatures and deflections are achieved by constructing and using a custom vacuum chamber and heating setup; significant thermal gradients and repeatable thermal deformations are measured and analyzed, forming a ground truth for succeeding simulations. Second, multiphysics models of the experimental chamber are created in COMSOL Multiphysics and characterized, even accounting for residual convection, and used to inform prototype improvements and more advanced simulations. Third, based off such predictions, the unit structure prototype composite is improved by adding a layer of graphitized polymer film, with further experimentation showing a dramatic reduction in deflections.
Finally, the accumulated knowledge is used to simulate a satellite slew maneuver with realistic orbital heating; a custom technique to couple thermal (Thermal Desktop) and structural (Abaqus) finite-element software via a MATLAB script allows for the recalculation of radiation view factors during simulations, a feat necessary for accurate heating calculations on deployable structures. These results have immediate applicability in predicting structural temperatures and deflections during the satellite maneuvers proposed for the Caltech Space Solar Power Project, as well as suggesting critical improvements to ensure reliability and mission success.</p
Microwave Spectroscopy for Probing Electronuclear Modes in Quantum Magnets
Get PDFCrystals with rare earth ions present an opportunity to explore a range of model magnetic systems, allowing for an experimental realization of several important physical concepts. For example, the compound LiHoF₄ is a transparent, insulating crystal which implements the transverse field Ising model (TFIM) with the Ho³⁺ spins. The TFIM is a well-known model which is one of the simplest systems to display quantum behavior, such as quantum phase transitions (QPTs). This makes LiHoF₄ very useful for investigating these and other quantum effects. LiHoF₄ ~also has strong hyperfine coupling to the nuclear spins, which means the excitations must be considered as composite of electronic and nuclear states (i.e., 'electronuclear'). This introduces a nuclear spin bath which modifies behavior near the QPT. In this work, we investigate the behavior of this QPT by probing the electronuclear states in LiHoF₄ at microwave frequencies. To accomplish this, we develop the use of loop-gap resonators which enable sensitive microwave measurements in LiHoF₄. We also extend the techniques to related systems, such as the 2-dimensional XY antiferromagnet LiErF₄. We then investigate ways to observe new phenomena in the LiHoF₄ system, namely improving superconducting resonators as one possible way to observe the dynamics of quantum quenching through the QPT
Probing the Biological Interactions of a Therapeutic Small Peptide at the Tissue, Cellular, and Molecular Levels for Treating Retinal Diseases
Get PDFAge-related macular degeneration (AMD) and diabetic retinopathy (DR) are the leading causes of blindness in developed countries, affecting the lives of millions and lowering their quality of life due to limited eyesight. Although these retinal diseases afflict a large portion of the senior population, the current standard of care, primarily antibody injection treatments, merely treats symptoms and fails to address the root causes of diseases.
A therapeutic hexapeptide, risuteganib (RSG), designed by Allegro Ophthalmics, LLC., has shown efficacy in treating both angiogenic and inflammatory retinopathies such as wet- and dry-AMD and diabetic macular edema (DME), a progressive form of DR. In the past, many studies sought to identify integrins that demonstrated specific binding affinities to RSG, based on the structural similarity of RSG to the well-known RGD motif. In contrast, Caltech decided to pursue unbiased research directions to unveil the mechanism of action (MOA) of RSG. Following studies from Caltech, UC Irvine, Johns Hopkins University, and Duke University revealed unanticipated features of RSG, suggesting that it has both anti-angiogenic and anti-inflammatory effects and that it can rescue compromised mitochondrial functions in cells under chemically induced oxidative stress. In this thesis, we conducted a series of investigations, progressing from the tissue level to the cellular level and then to the molecular level, to test a hypothesis that may unify these observations.
In Chapter 2, we developed a peptide-directed fluorescent staining method to identify the binding location of RSG-dye conjugate using aged BALB/c to recapitulate features of age-related retinal diseases. We identified crucial parameters that visualized replicable, RSG-specific labeling at the aged RPE layer. Findings from Chapter 2 and past studies laid the groundwork for the multi-layered investigations on the MOA of RSG in subsequent chapters, hinting at the cell type of interest, the need for appropriate stress to tissue or cell, and the importance of limiting RSG probes below clinical dosage. In collaboration with the Kenney lab at UC Irvine, we tested the protective effects of RSG in differentiated ARPE-19 cell model in response to chemically activated hypoxia-inducible factor 1 (HIF-1) signaling pathway (Chapter 3). Sub-clinical dosage of RSG (30 µM) demonstrated protective effects against chemically elevated HIF-1α leading to cell death and compromised mitochondrial membrane integrity. Further, RSG-dye conjugate localized in the mitochondria of differentiated ARPE-19 cells, where partial co-localization with mitochondrial protein, pyruvate dehydrogenase E1 α subunit (PDHA1), was observed. In Chapter 4, we developed a luminescence-based in vitro assay to quantify the effect of RSG on the activity of a mitochondrial kinase, pyruvate dehydrogenase kinase 1 (PDHK1). PDHK1 interacts with pyruvate dehydrogenase complex (PDC) which contains PDHA1. PDHK/PDC interaction governs the pyruvate decarboxylation process, thereby serving as a dynamic metabolism switch. Upregulation of PDHK1 decreases PDC activity, which subsequently increases glycolytic flux as an adaptive response against hypoxia, temporarily alleviating oxygen deficiency at the cost of total ATP production per mol glucose. When such response is prolonged in the RPE cells, energy deficiency and compromised homeostasis lead to cell death of not only RPE cells but other crucial retinal cells including the photoreceptor cells. Here, tested RSG concentrations (3.125, 6.25, and 12.5 µM) did not demonstrate significant inhibition of PDHK1 activity; nevertheless, the devised in vitro assay laid the foundation for a reliable, quantitative assessment of RSG or other inhibitors’ effects on the activity of PDHK1 and its isoforms. Chapter 5 summarized the findings in the previous chapters and suggested future studies that might aid in the continued search for the mechanism of action of RSG.</p
Methods for Learning Mechanics: Inverse Problems, Constitutive Modeling, and Design
Get PDFPredicting the behavior of materials and structures under complex loading is a fundamental challenge in solid mechanics. Traditional techniques rely on idealized experiments, a priori information and are computationally intensive. This work introduces a unified framework for constitutive identification, multiscale modeling, and design optimization, grounded in physical laws and enhanced by machine learning.
We first focus on the inverse problem of identifying constitutive behavior from experimental data. We formulate this task as an optimization problem with the governing equations of the experiment as a constraint. This allows us to infer material parameters from full-field or contact-based measurements. This approach enforces physical laws and accommodates complex loading, noise, and limited data. We apply it to recover properties for a history-dependent material using both synthetic and experimental datasets. We further extend the framework using recurrent neural operators to learn constitutive responses directly from data, bypassing the need for an explicit model form.
In the second part of the thesis, we extend our focus to multiscale modeling and structural design. Neural operators are used to learn homogenized solutions of linear elliptic PDEs with discontinuous coefficients, eliminating the need to resolve fine-scale features during inference. For topology optimization, we develop reduced-order neural surrogates embedded within the design loop, achieving efficient yet accurate updates. Together, these contributions offer a cohesive, data-driven strategy for advancing modeling and design in solid mechanics.</p
Operator Learning for Scientific Computing
No full textThis thesis develops operator learning theory and methods for use in scientific computing. Operator learning uses data to approximate maps between infinite dimensional function spaces. As such, operator learning provides a natural framework for using machine learning in applications with partial differential equations (PDEs). While operator learning architectures have successfully modeled a variety of physical phenomena in practice, the theoretical foundations underpinning these successes remain in early stages of development.
The present work takes a step towards a complete understanding of operator learning and its potential use in scientific applications. The thesis begins by studying multiscale constitutive modeling, where operator learning models can serve as surrogates to accelerate simulation and aid in model discovery of physical laws. The work proposes, and theoretically and numerically analyzes, an operator learning architecture for modeling history dependence in homogenized constitutive equations. The thesis then addresses learning solutions to an elliptic PDE in the presence of discontinuities and corner interfaces in two-dimensional materials. By proving a key continuity result for the underlying PDE, a universal approximation result is obtained. In its second half, the thesis moves on from the setting of homogenized constitutive laws and gives insight to operator learning from a broader perspective. First, error analysis bounds a form of discretization error that arises in implementations of the Fourier Neural Operator (FNO). Next, a modified form of the FNO, the Fourier Neural Mapping, accommodates finite-dimensional data while retaining the underlying function space structure. This modification allows applications where the map of interest is governed by an infinite-dimensional operator with data, such as parameters or summary statistics, in the form of finite vectors. Finally, the thesis extends a theory-to-practice gap result in finite dimensions to the infinite-dimensional operator learning setting, asserting that even for classes of architectures whose model expressivity scales well with model size, their error convergence with respect to data size scales poorly. In summary, this thesis builds understanding of operator learning from several perspectives and contributes both theoretical advancements and practical methodologies that improve the applicability of operator learning models to scientific problems.</p
An Electrodynamic Perspective of Black Holes
No full textNumerical relativity (NR) is a powerful modeling tool for the dynamics of general-relativistic systems that are difficult to analyze analytically. Prior work has led to a tetrad formulation of the 3+1 decomposition of the Einstein field equations (EFE) that bears a striking resemblance to electrodynamics, done by recasting Einstein’s equations into a set of coupled nonlinear Maxwell equations. We use gravitational electric and magnetic fields developed from this theory to analytically probe Schwarzschild and Kerr solutions to the EFE. We compare the resulting Kerr dynamics with a numerical simulation of the Kerr spacetime. We then extend this analysis to visualize the inspiral, merger, and ring-down of a binary black hole collision simulated in moving puncture gauge
Full-Field Quantitative Visualization of Shock-Driven Pore Collapse in Solids: Mechanics of Deformation, Failure, and Interaction
No full textPorosity in solids is ubiquitous throughout engineering applications: inherent in energetic materials and exaggerated upon degradation, incorporated into shock-absorbing structures via materials such as metallic foams and metamaterials, and arising through manufacturing defects---especially in metal additive manufacturing methods. In these applications, many phenomena at both the macro- and meso-scale are critical to the operation under dynamic compression. Macroscopic shock wave structure, including shock attenuation and disruption are important for engineered structures like metallic foams, while mesoscopic localized shear deformation near porous defects can be a cause of failure in structures and is thought to be a mechanism for mechanically-induced hot spots in energetic materials which can dictate their ignition behavior. While the macroscopic shock response of porous materials has been well studied, the mesoscopic response has received less attention. Recent studies have improved the understanding through sophisticated numerical simulations and pore collapse experiments leveraging innovative high-speed imaging technologies, but many details of the mesoscopic response remain unclear.
This thesis is focused on the mesoscopic domain, with an overarching goal of characterizing local details of pore collapse, such as the rate of collapse, pore geometry (asymmetry) evolution, deformation induced in the material surrounding the pore, localization/failure mechanisms, and interactions between pores. Fundamental understanding of these mesoscopic phenomena is a critical step toward unraveling the physics which couple the mesoscale and macroscale responses, enabling predictive modeling for the dynamic response of porous materials/structures, and developing innovative engineering designs with porous materials.
The first part of this thesis develops a novel internal digital image correlation (DIC) technique for use in full-scale dynamic laboratory experiments, which enables investigation of phenomena which occur under confinement or are sensitive to boundary effects. The technique consists of manufacturing transparent specimens with an internally embedded speckle pattern, which is then dynamically deformed via the experiment of choice. During dynamic loading, the internal speckle pattern is visualized with a high-speed camera, after which DIC software is used to process the images and compute the displacement, velocity, and strain fields. The technique is implemented and validated using polymethyl methacrylate (PMMA) specimens under compression with split-Hopkinson (Kolsky) pressure bar and plate impact experiments---providing validation under both uniaxial stress and uniaxial strain conditions, at strain rates of 10³-10⁶ s⁻¹ and impact stresses up to 0.65 GPa.
The second part of the thesis implements the internal DIC technique to investigate the mechanics of a single spherical pore during collapse induced by weak shock loading up to 1 GPa impact stress in PMMA. The first of its kind internal strain measurements reveal concentrations around the collapsing pore, which are approximately consistent with elastostatic theory. Equivalent shear strain measurements uncover a transition from classical strain concentrations to the development of shear bands at 0.6 GPa, and raw deformation images show the development of fracture at 0.8 GPa---representing two distinct failure mechanisms arising within a small range of impact stresses. The shear bands arise due to large stress concentrations near the pore, which leads to plastic deformation and heating. Thermal softening generates local material instabilities, which can grow into regions of large, localized deformation. These bands are captured via explicit finite element analysis through a thermo-viscoplastic material model. The numerical simulations further indicate the crack to be a shear crack propagating through the weakened material of an adiabatic shear band. Finally, theoretical approaches elucidate the mechanics which govern the initiation of, spacing between, and preferred paths for these failure modes.
The third part of the thesis follows a natural extension toward real porous media, investigating the collapse of pore arrays in PMMA with a focus on the role of interactions between pores on the localization and failure response. Experiments are conducted on pairs of pores in vertical and horizontal configurations. By utilizing internal DIC and shadowgraphy, the evolution of shear bands and cracks is visualized and measured. Further, apparent interactions between pores are identified through shifts in impact stress thresholds for failure initiation and through delayed crack growth. Baroclinicity, and accompanying baroclinic torque, is identified as the driving mechanism for crack propagation in these experiments. Finally, shear diffraction waves initiate upon plane wave interaction with pores and propagate toward neighboring pores. This is considered as a possible interaction mechanism between pores which alters the failure response.
The work presented in this thesis enabled the first in-situ observation of adiabatic shear banding during pore collapse in addition to a much-improved spatiotemporal characterization of crack propagation compared to previous works. Analysis of the experimental results revealed the ability of theoretical and numerical (FEA) models to capture many details of shear localization in pore collapse. Further analysis unraveled mechanisms governing pore collapse and associated failure modes, including the importance of pore asymmetry during collapse as well as planar shock interaction with the pore and the resultant baroclinicity and diffracted shear waves.</p
Measuring Neutrino Oscillations with NOvA and T2K
No full textThe discoveries of the twentieth century proved that neutrinos have mass and can change flavor. For the past few decades, a major focus of research has been the measurement of the physical parameters which govern this flavor oscillation. These measurements remain inconclusive on a few key questions, including the ordering of the neutrino masses and whether neutrinos violate CP symmetry. NOvA and T2K are two long-baseline accelerator neutrino experiments working in this space. By placing detectors in a beam of muon (anti-)neutrinos, these experiments interrogate neutrino oscillations by measuring muon (anti-)neutrino disappearance and electron (anti-)neutrino appearance. The complementarity of NOvA's and T2K's oscillation measurements motivated the experiments to pursue a joint oscillation analysis. After bracketing the potential impacts of correlations between the two experiments' systematic uncertainties and constructing a joint likelihood function, we share in this thesis the first results from the NOvA-T2K joint oscillation analysis. We report the world's most precise measurement of Δm232 to date: +2.429+0.039-0.035 (-2.477±0.035) × 10-3 eV2 assuming the normal (inverted) mass ordering, showing a slight preference for the inverted mass ordering. The maximally CP-violating value of δCP=+π/2 is excluded by 3σ credible intervals, and if we assume neutrinos are in the inverted mass ordering, we see evidence of CP violation at 3σ.
Additionally, we present an effort to encapsulate neutrino cross-section models in a parametrization-agnostic way. We have created a suite of systematic parameters that are capable of mimicking the action of NOvA's cross-section model. This method could be used in a future joint data analysis between long-baseline neutrino experiments. We also introduce Voronoi histograms, an ancillary technique developed as part of this program. Voronoi histograms are a new way to efficiently bin high-dimensional data. This method preserves bin density in regions of interest, while tightly controlling the total number of bins used. The performance gains from using Voronoi binnings over standard rectangular binnings scale dramatically with the dimensionality of the data
Cuticular Hydrocarbons in Myrmecophiles are a Mechanism of Symbiotic Entrenchment
No full textThe velvety tree ant, Liometopum occidentale, hosts three myrmecophilous rove beetles, Sceptobius lativentris, Platyusa sonomae, and Liometoxenus newtonarum. The three beetles independently evolved to mimic the nestmate recognition pheromones of L. occidentale with varying degrees of accuracy. The accuracy of the mimicry determines the degree of integration of the beetles into nests of their host; P. sonomae achieves the least accurate mimicry and is located at the nest periphery, whereas S. lativentris employs the most accurate mimicry and has access to the entirety of the ant nest and its resources. The accuracy of the mimicry was found to be dependent on the mechanism by which it is achieved. P. sonomae synthesizes the pheromone blend de novo and S. lativentris acquires the pheromones from the host ant. The approach taken by S. lativentris is significant, because the class of chemicals used as nestmate recognition pheromones in ants play a more primary role, forming a desiccation barrier that coats the surface of all insects. In the transition into the nests of its hosts, which occurs after the pupal developmental stage, S. lativentris permanently shuts off its production of these anti-desiccation compounds, opting instead to steal them from its host. This high-fidelity mimicry comes at a cost. S. lativentris is locked into an obligate and irreversible dependence on L. occidentale, dying within a day away from its host ant
Time-Resolved Proteomic Analysis in Zebrafish and Cultured Neurons Using Bioorthogonal Noncanonical Amino Acid Tagging
No full textTemporally and spatially controlled protein synthesis plays a critical role in orchestrating the molecular events underlying behaviors, stress adaptations, and therapeutic responses to drugs. However, traditional proteomic techniques often fail to capture the dynamic changes in protein expression essential for understanding transient biological phenomena. To overcome this limitation, the work presented in this thesis leverages bioorthogonal noncanonical amino acid tagging (BONCAT) coupled with mass spectrometry to perform time-resolved proteomic analyses in zebrafish larvae and cultured neurons.
Chapter II details the development and validation of BONCAT proteomics in zebrafish, demonstrating that newly synthesized proteins from zebrafish larvae could be reliably labeled, enriched, and identified even over short labeling periods. Proof-of-concept experiments using heat shock revealed that BONCAT proteomics was able to detect changes in expression of proteins known to be induced by heat shock with greater sensitivity than conventional approaches using global proteomics. These results establish BONCAT as a powerful tool for investigating dynamic changes in protein synthesis in zebrafish. In Chapter III, we applied BONCAT to neuronal cultures to profile the proteomic changes induced by sub-anesthetic, antidepressant-relevant doses of ketamine. These studies uncovered rapid alterations in protein synthesis, identifying significantly differentially regulated proteins and pathways involved in synaptic plasticity, cytoskeletal remodeling, cellular signaling, metabolism, and RNA processing. This work provides novel molecular insights into ketamine’s rapid-acting antidepressant effects and further illustrates the utility of BONCAT for capturing early, transient proteomic responses to drug treatment. Finally, in Chapter IV, we explore changes in protein expression in zebrafish larvae underlying circadian rhythms and in response to low-dose ketamine treatment. We observed interesting protein synthesis patterns in both biological contexts, but our findings lacked the statistical significance and reproducibility across experiments required to draw strong biological conclusions from our data. Although methodological refinements are required, our work underscores BONCAT’s potential to elucidate transient proteomic shifts underlying behavioral phenomena and pharmacological interventions in zebrafish.</p