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Kerr Solitons and Brillouin Lasers in Optical Microresonators
Get PDFOptical resonators are capable of storing electromagnetic energies in the visible and infrared band. The light intensity is greatly enhanced within the resonator, which makes them suitable as a platform for nonlinear optics studies. Here, using silica microresonators as platforms, we explore the fundamental nonlinear dynamics of light induced by Kerr nonlinearity and Brillouin scattering. The first half of the thesis analyzes optical solitons as a result of Kerr nonlinearity, including its universal scaling, its dynamics in the presence of laser feedback, the analytical properties of its relativistic counterpart, as well as its applications as a wavelength reference. The second half of the thesis focuses on stimulated Brillouin lasers and their linewidth performance, demonstrating new performance levels of the Brillouin laser and two correction factors to its linewidth that have been established for semiconductor lasers.</p
Secondary Organic Aerosol Formation from Volatile Chemical Products: Understanding Aerosol Yields and Dynamics
Get PDFParticulate matter impacts public health and climate. A major component of small particulate matter, called secondary organic aerosol (SOA), is formed from the condensation of the oxidation products of organic compounds emitted into the atmosphere in the gas phase. Recent analysis suggests that volatile chemical products are responsible for a large fraction of the particulate matter formed from petroleum sources: perhaps more than motor vehicles. This is especially the case in urban areas, which have significant air pollution burdens.
Understanding exactly which precursors are responsible for this large SOA formation and under which conditions is difficult: for each compound, different chemical pathways dominate and even similar molecules can form vastly varied amounts of aerosol. Even if one could study every compound, extrapolating data to the atmosphere is non-trivial. SOA formation is principally understood through laboratory chamber studies, but these studies require a rigorous, quantitative grasp of chamber phenomena to meaningfully interpret the results.
In this dissertation, computational simulations of environmental chambers illuminate the physico-chemical processes that occur within a chamber and the manner in which these processes interact, in order to help extrapolate data to real-world conditions. In particular, the contribution of particle charge to the rate of particle-wall deposition within environmental chambers is investigated.
With this understanding, the amount of aerosol formed per precursor emitted, called the secondary organic aerosol yield, is investigated for benzyl alcohol and decamethylcylopentasiloxane (D5). At atmospherically relevant concentrations, benzyl alcohol and D5 have disparate SOA mass yields: as much as 100% for benzyl alcohol and ~1% for D5. Both of these findings differ from what was previously modeled and measured, indicating the importance of performing experiments on the compounds of interest and evaluating the oxidation products under atmospherically relevant conditions.</p
Reduced Order Model Inspired Robotic Bipedal Walking: A Step-to-step Dynamics Approximation based Approach
Get PDFControlling bipedal robotic walking is a challenging task. The dynamics is hybrid, nonlinear, high-dimensional, and typically underactuated. Complex physical constraints have to be satisfied in the walking generation. The stability in terms of not-falling is also hard to be encoded in the walking synthesis. Canonical approaches for enabling robotic walking typically rely on large-scale trajectory optimizations for generating optimal periodic behaviors on the full-dimensional model of the system; then the stabilities of the controlled behaviors are analyzed through the numerically derived Poincaré maps. This full-dimensional periodic behavior based synthesis, despite being theoretically rigorous, suffers from several disadvantages. The trajectory optimization problem is computationally challenging to solve. Non-trivial expert-tuning is required on the cost, constraints, and initial conditions to avoid infeasibilities and local optimality. It is cumbersome for realizing non-periodical behaviors, and the synthesized walking can be sensitive to model uncertainties.
In this thesis, we propose an alternative approach of walking synthesis that is based on reduced order modeling and dynamics approximation. We formulate a discrete step-to-step (S2S) dynamics of walking, where the step size is treated as the control input to stabilize the pre-impact horizontal center of mass (COM) state of the robot. Stepping planning thus is converted into a feedback control problem. To effectively and efficiently solve this feedback stepping planning problem, an underactuated Hybrid Linear Inverted Pendulum (H-LIP) model is proposed to approximate the dynamics of underactuated bipedal walking; the linear S2S dynamics of the H-LIP then approximates the robot S2S dynamics. The H-LIP based stepping controller is hence utilized to plan the desired step sizes on the robot to control its pre-impact horizontal COM state. Stable walking behaviors are consequently generating by realizing the desired step size in the output construction and stabilizing the output via optimization-based controllers. We evaluate this approach successfully on several bipedal walking systems with an increase in the system complexity: a planar five-linkage walker AMBER, an actuated version of the Spring Loaded Inverted Pendulum (aSLIP) in both 2D and 3D, and finally the 3D underactuated robot Cassie. The generated dynamic walking behaviors on these systems are shown to be highly versatile and robust. Furthermore, we show that this approach can be effectively extended to realizing more complex walking tasks such as global trajectory tracking and walking on rough terrain.</p
Identification of New OPA1 Cleavage Site Reveals that Short Isoforms Regulate Mitochondrial Fusion
Get PDFOPA1 is a 120kDa large GTPase belonging to the dynamin superfamily. It is the only known mitochondrial inner membrane fusion protein, mediating fusion of the mitochondrial inner membranes following outer membrane fusion. Additionally, OPA1 also regulates cristae morphology and maintains respiratory chain function.
OPA1 has two forms—inner-membrane-anchored long forms (l-OPA1) and cleaved inter-membrane-space only short forms (s-OPA1). L-OPA1 are proteolytically processed by two mitochondrial proteases—OMA1 and YME1L, acting at cleavage sites S1 and S2 respectively, to produce s-OPA1.
In both mice and human, half of the mRNA splice forms of Opa1 are constitutively processed post translation to yield exclusively s-OPA1. However, the specific function of s-OPA1 in mitochondrial fusion has been debated—under basal conditions, s-OPA1 are needed to maintain optimal fusion activity, but in certain stress conditions, s-OPA1 is dispensable for fusion. By constructing cells in which the Opa1 locus no longer produces transcripts with S2 cleavage sites using CRISPR-Cas9, we generated a simplified system to identify the novel YME1L-dependent site S3 that mediates constitutive and complete cleavage of OPA1. We found that S3 site locates within the C-terminal leucine string of Opa1 exon4b, slightly upstream of the well-established S1. We show that mitochondrial morphology is highly sensitive to the ratio of l-OPA1 to s-OPA1, indicating that s-OPA1 fine tunes mitochondrial fusion.</p
First Observation of the Production of Three Massive Vector Bosons and Search for Long-Lived Particles using Delayed Photons in pp Collisions at √s = 13 TeV
Get PDFThis thesis presents the first observation of the production of three massive vector bosons (VVV with V= W, Z) in proton-proton collisions at the LHC at √s = 13 TeV. The search was performed in final states with two same-sign charged leptons (electrons or muons) plus one or two jets, and three, four, five, or six leptons from WWW, WWZ, WZZ and ZZZ decays, with a data sample corresponding to an integrated luminosity of 137 fb-1 collected by the CMS experiment during 2016-2018. The observed (expected) significance of the combined VVV production is 5.7 (5.9) standard deviations, and the production cross section is measured to be 1010+210-200(stat)+150-120(syst) fb, corresponding to a signal strength of 1.02+0.26-0.23. We also found evidence for WWW and for WWZ production, with observed significances of 3.3 and 3.4 standard deviations, respectively. The measured production cross sections for individual VVV processes are also reported. The establishment of VVV production opens a new program of many standard model studies (such as gauge-gauge and Higgs-gauge couplings), and provides a new tool for many new physics searches (such as anomalous gauge coupling searches, new resonance searches). This thesis also reports a search for long-lived supersymmetry particles decaying to photons in proton-proton collisions at the LHC at √s = 13 TeV, with a data sample corresponding to an integrated luminosity of 77.4fb-1 collected by CMS during 2016-2017. Results are interpreted with a gauge-mediated supersymmetry breaking model, where the long-lived particle is a neutralino and the lightest supersymmetry particle is a gravitino. For neutralino proper decay lengths of 0.1, 1, 10, and 100 m, masses up to 320, 525, 360, and 215 GeV are excluded at 95% confidence level, respectively. This result extends the limits from previous searches by one order of magnitude for the neutralino proper decay length and up to 100 GeV more for the neutralino mass. Motivated by the need for precision timing measurements for long-lived particle searches, as well as for improvements in general object reconstruction performance, the timing performance of two types of sensors are studied in this thesis: one with a Cerium doped Lutetium Yttrium Orthosilicate (LYSO:Ce) crystal as the scintillator and a silicon photomultiplier (SiPM) as the photodetector, another with a Cadmium-Telluride sensor as the active material for a sampling calorimeter. Both setups have been demonstrated with test beams to be able to provide timing measurements of particles with a resolution below 30 ps.</p
Entangling, Controlling, and Detecting Individual Strontium Atoms in Optical Tweezer Arrays
Get PDFWe present a novel experimental platform for quantum and precision science: single strontium atoms trapped in arrays of optical tweezers. We demonstrate development of this platform along three important fronts: single-atom trapping, imaging, and cooling; coherent control of the ultra-narrow clock transition; and inter-atom entanglement via Rydberg interactions.
In the context of single-atom physics, we demonstrate trapping in tweezer arrays of one- and two-dimensions as well as cooling to the motional ground state. We furthermore show high-fidelity single-atom imaging with extremely low loss, allowing us to image the same atoms thousands of times before losing them and in principle allowing for the assembly of defect-free atom arrays of several hundred sites.
Notably, we show these results in tweezers that are at a magic wavelength for strontium's clock transition. This feature allows us to perform high-fidelity state rotations on the clock transition. We also demonstrate operation of a single-site resolved atomic-array optical clock -- a new atomic clock platform that combines several benefits of optical lattice and single-ion clocks.
From the metastable clock state, we drive the atoms to highly-excited Rydberg states to introduce interactions between nearby atoms. Using a Rydberg blockade in an assembled array of atom pairs, we demonstrate generation of two-atom entangled Bell states with a fidelity of >98%, or >99% with correction for state preparation and measurement errors. Furthermore, we demonstrate an auto-ionization state-detection scheme for Rydberg atoms which improves on the infidelity of previous Rydberg state-detection schemes by over an order of magnitude.
We conclude with several outlooks, including preliminary data on light-cone correlation spreading in a system of 17 interacting atoms. We also discuss prospects for implementing quantum gates, operating a spin-squeezed clock, increasing system size, quantifying many-body state fidelity, and reducing sources of infidelity.</p
Resolvent Modeling of Turbulent Jets
Get PDFOptimal control of turbulent flows requires a detailed prediction of the unsteady, three-dimensional turbulent structures that govern quantities of interest like noise, drag, and mixing efficiency. There is a need for physics-based, reduced-order models of turbulent structure for those cases where direct simulation of the flow would be computationally prohibitive. In this thesis, we explore resolvent analysis as a framework for such models. Based on a linearization about the turbulent mean flow field, the resolvent finds optimal (highest gain) forcing functions that give rise, through linear amplification mechanisms, to energetic coherent structures. The forcing functions represent the nonlinear interactions between the coherent structures as well as with background incoherent turbulence. While the high-gain structures capture many characteristics of the observed turbulent coherent structures in both wall-bounded and free-shear flows, closures for the forcing function are required to make these models predictive and thus utilize them for flow control.
In the first part of this thesis, we examine a linear model for the resolvent forcing by adapting the concept of a turbulent (eddy) viscosity from classical Reynolds-Averaged Navier--Stokes (RANS) turbulence modeling. We present a data-driven approach to identify an optimal eddy-viscosity field that best matches the resolvent prediction to the most energetic coherent structure educed via spectral proper orthogonal decomposition (SPOD) of data from high-fidelity simulations. We analyze the specific case of turbulent jets spanning a range of Mach numbers from subsonic to supersonic. We find the optimal eddy-viscosity field to be effective at matching both the shape and energy distribution of structures. More importantly, we find that calibrated eddy-viscosity fields predicted using standard eddy-viscosity models (utilizing only quantities available from RANS) yield results that are close to optimal.
We use the resulting resolvent model together with the high-fidelity data to investigate the full spectrum of amplification mechanisms and coherent structures present in turbulent jets. The addition of a turbulence model provides a clear separation between two established mechanisms in turbulent jets (Kelvin-Helmholtz and Orr) and leads to the identification of a third mechanism known as lift-up. Lift-up becomes the dominant mechanism at low-frequency limits for nonzero azimuthal wavenumbers, generating elongated, streaky structures. We find these streaks to be the most energetic structures in the jet, and that their presence has implications for altering the mean flow and controlling noise.
Finally, we extend resolvent analysis to that of an acoustic analogy that relates the near-field forcing to the far-field acoustics 100 diameters from the nozzle. We again leverage high-fidelity data to produce an ensemble of realizations of the acoustic field and find that only a few resolvent modes are necessary for reconstruction. Ultimately, we find that a resolvent model based solely upon RANS quantities can reconstruct and predict the peak acoustic field at rank-1 to within 2 decibels for both the supersonic and transonic jets.</p
Large-Scale Intelligent Systems: From Network Dynamics to Optimization Algorithms
Get PDFThe expansion of large-scale technological systems such as electrical grids, transportation networks, health care systems, telecommunication networks, the Internet (of things), and other societal networks has created numerous challenges and opportunities at the same time. These systems are often not yet as robust, efficient, sustainable, or smart as we would want them to be. Fueled by the massive amounts of data generated by all these systems, and with the recent advances in making sense out of data, there is a strong desire to make them more intelligent. However, developing large-scale intelligent systems is a multifaceted problem, involving several major challenges. First, large-scale systems typically exhibit complex dynamics due to the large number of entities interacting over a network. Second, because the system is composed of many interacting entities, that make decentralized (and often self-interested) decisions, one has to properly design incentives and markets for such systems. Third, the massive computational needs caused by the scale of the system necessitate performing computations in a distributed fashion, which in turn requires devising new algorithms. Finally, one has to create algorithms that can learn from the copious amounts of data and generalize well. This thesis makes several contributions related to each of these four challenges.
Analyzing and understanding the network dynamics exhibited in societal systems is crucial for developing systems that are robust and efficient. In Part I of this thesis, we study one of the most important families of network dynamics, namely, that of epidemics, or spreading processes. Studying such processes is relevant for understanding and controlling the spread of, e.g., contagious diseases among people, ideas or fake news in online social networks, computer viruses in computer networks, or cascading failures in societal networks. We establish several results on the exact Markov chain model and the nonlinear "mean-field" approximations for various kinds of epidemics (i.e., SIS, SIRS, SEIRS, SIV, SEIV, and their variants).
Designing incentives and markets for large-scale systems is critical for their efficient operation and ensuring an alignment between the agents' decentralized decisions and the global goals of the system. To that end, in Part II of this thesis, we study these issues in markets with non-convex costs as well as networked markets, which are of vital importance for, e.g., the smart grid. We propose novel pricing schemes for such markets, which satisfy all the desired market properties. We also reveal issues in the current incentives for distributed energy resources, such as renewables, and design optimization algorithms for efficient management of aggregators of such resources.
With the growing amounts of data generated by large-scale systems, and the fact that the data may already be dispersed across many units, it is becoming increasingly necessary to run computational tasks in a distributed fashion. Part III concerns developing algorithms for distributed computation. We propose a novel consensus-based algorithm for the task of solving large-scale systems of linear equations, which is one of the most fundamental problems in linear algebra, and a key step at the heart of many algorithms in scientific computing, machine learning, and beyond. In addition, in order to deal with the issue of heterogeneous delays in distributed computation, caused by slow machines, we develop a new coded computation technique. In both cases, the proposed methods offer significant speed-ups relative to the existing approaches.
Over the past decade, deep learning methods have become the most successful learning algorithms in a wide variety of tasks. However, the reasons behind their success (as well as their failures in some respects) are largely unexplained. It is widely believed that the success of deep learning is not just due to the deep architecture of the models, but also due to the behavior of the optimization algorithms, such as stochastic gradient descent (SGD), used for training them. In Part IV of this thesis, we characterize several properties, such as minimax optimality and implicit regularization, of SGD, and more generally, of the family of stochastic mirror descent (SMD). While SGD performs an implicit regularization, this regularization can be effectively controlled using SMD with a proper choice of mirror, which in turn can improve the generalization error.</p
Electron-Phonon Interactions and Charge Transport from First-Principles Calculations: Complex Crystals, Higher Order Coupling, and Steps Toward the Small Polaron Regime
Get PDFElectron-phonon (e-ph) interactions quantify the strength of interplay between charge carriers and lattice vibrations and critically determine the transport properties in materials near room temperature. Depending on the coupling strength, charge carriers can exhibit behaviors ranging from propagating waves extending across crystals to trapped particles localized in space. Therefore, accurately describing e-ph interactions plays a central role in quantitative transport studies on real materials. Over the last few years, first-principles methods combining density functional theory (DFT) and related techniques with the Boltzmann transport equation (BTE) have rapidly risen and reached maturity for investigating transport in various metals, semiconductors, and insulators with weak e-ph coupling. The lowest-order e-ph scattering process can be investigated starting from e-ph interactions from DFT calculations; this first-principles approach provides unambiguous quantitative prediction of transport properties such as the conductivity and mobility in common semiconductors and metals over a wide temperature range without using any empirical parameter. Encouraged by the agreement of the computed transport properties with experiment for many simple materials, this thesis aims to extend the applicability of this first-principles methodology and to further our understanding of microscopic transport mechanisms, especially in the wide temperature window near room temperature where transport is governed by e-ph scattering. We present research that expands the state of the art in three distinct ways, focusing on three research directions we pursue in this work. First, we employ the BTE to calculate the hole carrier mobility of naphthalene, an organic molecular crystal containing 36 atoms in a unit cell, the record largest system for first-principles charge transport calculations to date. The results are in excellent agreement with experiments, demonstrating that transport in some high-mobility organic semiconductors can still be explained within the band theory framework, and show that low-frequency rigid molecular motions control the electrical transport in organic molecular semiconductors in the bandlike regime. The second topic is an attempt to go beyond the lowest-order theory of e-ph interactions and quantify the importance of higher-order e-ph processes. We derive the electron-two-phonon scattering rates using many-body perturbation theory, compute them in GaAs, and quantify their impact on the electron mobility. We show that these next-to-leading order e-ph scattering rates, although smaller than the lowest-order contribution, are not negligible, and can compensate the overestimation of mobility generally made by the lowest-order BTE calculation in weakly-polar semiconductors. In the third part of the thesis, we explore the opposite extreme case in which e-ph interactions are strong and lead to the formation of localized (so-called "polaron") electronic states that become self-trapped by the interactions with the atomic vibrations. We derive a rigorous approach based on canonical transformations to compute the energetics of self-localized (small) polarons in materials with strong e-ph interactions. With the aid of \textit{ab initio} e-ph interactions, we carry out the corresponding numerical calculations to investigate the formation energy of small polaron and determine whether the charge carriers favor localized states over the Bloch waves. Due to the low computational cost of our approach, we are able to apply these calculations to various compounds, focusing on oxides, predicting the presence of small polaron in agreement with experiments in various materials. Our work paves the way to understanding small polaron formation and extending these calculations to predict transport in the polaron hopping mechanism in materials with strong e-ph coupling
Customized Porosity in Ceramic Composites via Freeze Casting
Get PDFFreeze casting is a facile pore-forming technique for ceramics as it affords great tunability in pore structure including size, morphology, wall thickness, tortuosity, and alignment. Nevertheless, similar to any other pore-forming techniques, it has limitations in terms of the range of accessible properties. For example, a porous lamellar structure is highly permeable but easily fractures, while the dendritic structure is the opposite. This research seeks to provide strategies used with freeze casting to achieve a combination of properties that go beyond the current limitations and create optimized pore structures with a specific focus on three properties: strength, permeability, and surface area.
Such strategies utilize two composite material principles. First, particle reinforcement was implemented to optimize the mechanical and transport properties. Second, surface area was increased with hierarchical design for enhanced capture or catalysis applications. To optimize the mechanical and transport properties, we reinforced high-permeability lamellar structures with reinforcement fillers of silicon carbide (SiC) whiskers and carbon nanotubes (CNTs). The two fillers afford two different mechanisms of reinforcement: structural and material reinforcement.
Additions of 30 vol.% SiC whiskers increased the compressive strength by 325% at a small expense in permeability. Shear failure, common in lamellar structures, was prevented by the interwall bridges produced via particle engulfment during freezing. These bridges were demonstrated by the change in microstructure, stress-strain behavior, and fracture surfaces. A 2D in-situ solidification experiment was conducted to observe solidification and particle engulfment directly. We proposed a modified engulfment model to account for the complexity stemming from high-aspect ratio particles and non-planar freezing fronts. Reasonable agreement was found between the model, the simulation based on the model, and the experimental values from the freeze-casting and 2D-solidification experiments.
Freeze-casting with CNTs was explored as an alternative reinforcement strategy, but one which maintains the original pore structure. CNTs were pushed aside by the freezing front to pore walls due to their small diameters for low CNT concentration composites (<4.5 wt.%) such that the original pore structures remained. The compressive strength increased, albeit by smaller percentages (118% for 4.3 wt.%) than those with SiC whiskers. The increase was attributed to the toughening of pore walls with no diminishing effect on permeability. In addition, CNTs changed the electrical conductivity by ten orders of magnitude with the addition of 8.2 wt.% of the reinforcement.
Finally, conformal coatings via self-assembly of block copolymers (BCP) were produced by infiltration into a freeze-cast lamellar structure and significantly increased the surface area of the underlying scaffold. A bimodal pore size distribution with nanometer-size pores from the BCP self-assembly and micron-size pores from freeze casting was observed. An increase in compressive strengths was achieved with the introduction of pore hierarchy while retaining permeability of the macroporous structure due to enlarged lamellar spacings from the infiltration process.</p