Non-Euclidean Shells: A Study of Growth-Induced Fabrication and Mechanical Multi-Stability

Non-Euclidean shells are ubiquitous in both natural and man-made systems, yet fabrication at smaller length-scales (nanometer to micrometer) and the underlying mechanical behavior of these geometries is not well understood. In this dissertation, we develop a framework for improvements in fabrication and control over deformation pathways of non-Euclidean elastic shells. For programming a target non-Euclidean geometry, we study the non-uniform growth induced buckling in flat sheets. To deepen our understanding of this powerful mechanism, we present an experimental verification of its mathematical equivalence with a mechanism involving topological defects. We establish a framework for correlating topological defect-induced buckling, realized through a simple cut-and-glue construction, with growth induced buckling realized through non-uniform growth of patterned 2D hydrogel sheets. Validating the obtained mathematical results, we demonstrate fabrication of a cylindrical and conical dipole geometry through both mechanisms under consideration. In addition, upon applying a similar treatment on tetrahedron geometry we realize the limitations of growth-induced buckling mechanism for programming 3D geometry, and find that optimizing for pattern resolution and swelling range can lead to a higher fidelity in target geometries. Next, we turn towards the interplay between geometry and mechanics in non-Euclidean shells, to harness multi-stability between different geometric configurations. Under this theme, we study deformation of arbitrarily curved surfaces along pre-defined creases (curves with local weakening) finding that the continuity of this deformation can be predicted through a simple consideration of the curvature of the crease. We establish that simple geometric control over the crease curvature can be used to program on-demand snap-through instabilities between bi-stable states of developable, elliptic and hyperbolic surfaces. Using experiments, FEA and theoretical analysis of bending and stretching energies involved while indenting a hemispherical shell, we establish the geometric phase space in which the isometric state of a creased sphere is stable. Finally, we extend this notion by considering the ability to program multiple stable states in a tiled conical geometry, commonly found in bendable drinking straws (bendy straws) and other similar structures. These corrugated structures exhibit stability in axial (resulting in change in length), non-axial (change in ‘bent’ angle) and azimuthal direction (change in azimuthal angle) imparting a desirable high-degree of freedom in possible configurations. By analyzing the stability behavior of elastic double conical frusta shells, we study the necessary conditions for programming multi-stability, and find that axial stability depends on geometrical parameters. Interestingly, we conclude that a stable non-axial state requires a combination of appropriate geometry and presence of a pre-stress in azimuthal direction. The controlled multi-stability opens pathways toward a truly re-configurable shape programmable system, useful for manipulators and actuators in soft-robotic

The Apache Point Observatory Galactic Evolution Experiment (APOGEE) Spectrographs

We describe the design and performance of the near-infrared (1.51--1.70 micron), fiber-fed, multi-object (300 fibers), high resolution (R = lambda/delta lambda ~ 22,500) spectrograph built for the Apache Point Observatory Galactic Evolution Experiment (APOGEE). APOGEE is a survey of ~ 10^5 red giant stars that systematically sampled all Milky Way populations (bulge, disk, and halo) to study the Galaxy's chemical and kinematical history. It was part of the Sloan Digital Sky Survey III (SDSS-III) from 2011 -- 2014 using the 2.5 m Sloan Foundation Telescope at Apache Point Observatory, New Mexico. The APOGEE-2 survey is now using the spectrograph as part of SDSS-IV, as well as a second spectrograph, a close copy of the first, operating at the 2.5 m du Pont Telescope at Las Campanas Observatory in Chile. Although several fiber-fed, multi-object, high resolution spectrographs have been built for visual wavelength spectroscopy, the APOGEE spectrograph is one of the first such instruments built for observations in the near-infrared. The instrument's successful development was enabled by several key innovations, including a "gang connector" to allow simultaneous connections of 300 fibers; hermetically sealed feedthroughs to allow fibers to pass through the cryostat wall continuously; the first cryogenically deployed mosaic volume phase holographic grating; and a large refractive camera that includes mono-crystalline silicon and fused silica elements with diameters as large as ~ 400 mm. This paper contains a comprehensive description of all aspects of the instrument including the fiber system, optics and opto-mechanics, detector arrays, mechanics and cryogenics, instrument control, calibration system, optical performance and stability, lessons learned, and design changes for the second instrument.Comment: 81 pages, 67 figures, PASP, accepte

Final Report of the Muon E821 Anomalous Magnetic Moment Measurement at BNL

We present the final report from a series of precision measurements of the muon anomalous magnetic moment, a_mu = (g-2)/2. The details of the experimental method, apparatus, data taking, and analysis are summarized. Data obtained at Brookhaven National Laboratory, using nearly equal samples of positive and negative muons, were used to deduce a_mu(Expt) = 11 659 208.0(5.4)(3.3) x 10^-10, where the statistical and systematic uncertainties are given, respectively. The combined uncertainty of 0.54 ppm represents a 14-fold improvement compared to previous measurements at CERN. The standard model value for a_mu includes contributions from virtual QED, weak, and hadronic processes. While the QED processes account for most of the anomaly, the largest theoretical uncertainty, ~0.55 ppm, is associated with first-order hadronic vacuum polarization. Present standard model evaluations, based on e+e- hadronic cross sections, lie 2.2 - 2.7 standard deviations below the experimental result.Comment: Summary paper of E821 Collaboration measurements of the muon anomalous magnetic moment, each reported earlier in Letters or Brief Reports; 96 pages, 41 figures, 16 tables. Revised version submitted to PR

Energy harvesting for marine based sensors

This work examines powering marine based sensors (MBSs) by harvesting energy from their local environment. MBSs intrinsically operate in remote locations, traditionally requiring expensive maintenance expeditions for battery replacement and data download. Nowadays, modern wireless communication allows real-time data access, but adds a significant energy drain, necessitating frequent battery replacement. Harvesting renewable energy to recharge the MBSs battery, introduces the possibility of autonomous MBS operation, reducing maintenance costs and increasing their applicability. The thesis seeks to answer if an unobtrusive energy harvesting device can be incorporated into the MBS deployment to generate 1 Watt of average power. Two candidate renewable energy resources are identified for investigation, ocean waves and the thermal gradient across the air/water interface. Wave energy conversion has drawn considerable research in recent years, due to the large consistent energy flux of ocean waves compared to other conventional energy sources such as solar or wind, but focussing on large scale systems permanently deployed at sites targeted for their favourable wave climates. Although a small amount of research exists on using wave energy for distributed power generation, the device sizes and power outputs of these systems are still one to two orders of magnitude larger than that targeted in this thesis. The present work aims for an unobtrusive device that is easily deployable/retrievable with a mass less than 50kg and which can function at any deployment location regardless of the local wave climate. Additionally, this research differs from previous work, by also seeking to minimise the wave induced pitch motion of the MBS buoy, which negatively affects the data transmission of the MBS due to tilting and misalignment of the RF antenna. Thermal energy harvesting has previously been investigated for terrestrial based sensors, utilising the temperature difference between the soil and ambient air. In this thesis, the temperature difference between the water and ambient air is utilised, to present the first investigation of this thermal energy harvesting concept in the marine environment. A prototype wave energy converter (WEC) was proposed, consisting of a heaving cylindrical buoy with an internal permanent magnet linear generator. A mathematical model of the prototype WEC is derived by coupling a hydrodynamic model for the motion of the buoy with a vibration energy harvester model for the generator. The wave energy resource is assessed, using established mathematical descriptions of ocean wave spectra and by analysing measured wave data from the coast of Queensland, resulting in characteristic wave spectra that are input to the mathematical model of the WEC. The parameters of the WEC system are optimised, to maximise the power output while minimising the pitch motion. A prototype thermal energy harvesting device is proposed, consisting of a thermoelectric device sandwiched between airside and waterside heat exchangers. A mathematical model is derived to assess the power output of the thermal energy harvester using different environmental datasets as input. A physical prototype is built and a number of experiments performed to assess its performance. The results indicate that the prototype WEC should target the high frequency tail of ocean wave spectra, diverging from traditional philosophy of larger scale WECs which target the peak frequency of the input wave spectrum. The analysis showed that the prototype WEC was unable to provide the required power output whilst remaining below 100kg and obeying a 40 degrees pitch angle constraint to ensure robust data transmission. However, a proposed modification to the WECs cylindrical geometry, to improve its hydrodynamic coupling to the input waves, was shown to enable the WEC to provide the required 1W output power whilst obeying the pitch constraints and having a mass below 50kg. The thermal energy harvester results reveal that the thermal gradient across the air/water interface alone is not a suitable energy resource, requiring a device with a cross-sectional area in excess of 100m² to power a MBS. However, including a solar thermal energy collector to increase the airside temperature, greatly improves the performance and enables a thermal energy harvester with a cross-sectional area on the order of 1m² to provide 1W of output power. The findings in this thesis suggest that a well hydrodynamically designed buoy can provide two major benefits for a MBS deployment: enabling efficient wave energy absorption by the MBS buoy, and minimising the wave induced pitch motion which negatively affects the data transmission

Digital Filters

The new technology advances provide that a great number of system signals can be easily measured with a low cost. The main problem is that usually only a fraction of the signal is useful for different purposes, for example maintenance, DVD-recorders, computers, electric/electronic circuits, econometric, optimization, etc. Digital filters are the most versatile, practical and effective methods for extracting the information necessary from the signal. They can be dynamic, so they can be automatically or manually adjusted to the external and internal conditions. Presented in this book are the most advanced digital filters including different case studies and the most relevant literature

Statistical analysis and design of subthreshold operation memories

This thesis presents novel methods based on a combination of well-known statistical techniques for faster estimation of memory yield and their application in the design of energy-efficient subthreshold memories. The emergence of size-constrained Internet-of-Things (IoT) devices and proliferation of the wearable market has brought forward the challenge of achieving the maximum energy efficiency per operation in these battery operated devices. Achieving this sought-after minimum energy operation is possible under sub-threshold operation of the circuit. However, reliable memory operation is currently unattainable at these ultra-low operating voltages because of the memory circuit's vanishing noise margins which shrink further in the presence of random process variations. The statistical methods, presented in this thesis, make the yield optimization of the sub-threshold memories computationally feasible by reducing the SPICE simulation overhead. We present novel modifications to statistical sampling techniques that reduce the SPICE simulation overhead in estimating memory failure probability. These sampling scheme provides 40x reduction in finding most probable failure point and 10x reduction in estimating failure probability using the SPICE simulations compared to the existing proposals. We then provide a novel method to create surrogate models of the memory margins with better extrapolation capability than the traditional regression methods. These models, based on Gaussian process regression, encode the sensitivity of the memory margins with respect to each individual threshold variation source in a one-dimensional kernel. We find that our proposed additive kernel based models have 32% smaller out-of-sample error (that is, better extrapolation capability outside training set) than using the six-dimensional universal kernel like Radial Basis Function (RBF). The thesis also explores the topological modifications to the SRAM bitcell to achieve faster read operation at the sub-threshold operating voltages. We present a ten-transistor SRAM bitcell that achieves 2x faster read operation than the existing ten-transistor sub-threshold SRAM bitcells, while ensuring similar noise margins. The SRAM bitcell provides 70% reduction in dynamic energy at the cost of 42% increase in the leakage energy per read operation. Finally, we investigate the energy efficiency of the eDRAM gain-cells as an alternative to the SRAM bitcells in the size-constrained IoT devices. We find that reducing their write path leakage current is the only way to reduce the read energy at Minimum Energy operation Point (MEP). Further, we study the effect of transistor up-sizing under the presence of threshold voltage variations on the mean MEP read energy by performing statistical analysis based on the ANOVA test of the full-factorial experimental design.Esta tesis presenta nuevos métodos basados en una combinación de técnicas estadísticas conocidas para la estimación rápida del rendimiento de la memoria y su aplicación en el diseño de memorias de energia eficiente de sub-umbral. La aparición de los dispositivos para el Internet de las cosas (IOT) y la proliferación del mercado portátil ha presentado el reto de lograr la máxima eficiencia energética por operación de estos dispositivos operados con baterias. La eficiencia de energía es posible si se considera la operacion por debajo del umbral de los circuitos. Sin embargo, la operación confiable de memoria es actualmente inalcanzable en estos bajos niveles de voltaje debido a márgenes de ruido de fuga del circuito de memoria, los cuales se pueden reducir aún más en presencia de variaciones randomicas de procesos. Los métodos estadísticos, que se presentan en esta tesis, hacen que la optimización del rendimiento de las memorias por debajo del umbral computacionalmente factible mediante la simulación SPICE. Presentamos nuevas modificaciones a las técnicas de muestreo estadístico que reducen la sobrecarga de simulación SPICE en la estimación de la probabilidad de fallo de memoria. Estos esquemas de muestreo proporciona una reducción de 40 veces en la búsqueda de puntos de fallo más probable, y 10 veces la reducción en la estimación de la probabilidad de fallo mediante las simulaciones SPICE en comparación con otras propuestas existentes. A continuación, se proporciona un método novedoso para crear modelos sustitutos de los márgenes de memoria con una mejor capacidad de extrapolación que los métodos tradicionales de regresión. Estos modelos, basados en el proceso de regresión Gaussiano, codifican la sensibilidad de los márgenes de memoria con respecto a cada fuente de variación de umbral individual en un núcleo de una sola dimensión. Los modelos propuestos, basados en kernel aditivos, tienen un error 32% menor que el error out-of-sample (es decir, mejor capacidad de extrapolación fuera del conjunto de entrenamiento) en comparacion con el núcleo universal de seis dimensiones como la función de base radial (RBF). La tesis también explora las modificaciones topológicas a la celda binaria SRAM para alcanzar velocidades de lectura mas rapidas dentro en el contexto de operaciones en el umbral de tensiones de funcionamiento. Presentamos una celda binaria SRAM de diez transistores que consigue aumentar en 2 veces la operación de lectura en comparacion con las celdas sub-umbral de SRAM de diez transistores existentes, garantizando al mismo tiempo los márgenes de ruido similares. La celda binaria SRAM proporciona una reducción del 70% en energía dinámica a costa del aumento del 42% en la energía de fuga por las operaciones de lectura. Por último, se investiga la eficiencia energética de las células de ganancia eDRAM como una alternativa a los bitcells SRAM en los dispositivos de tamaño limitado IOT. Encontramos que la reducción de la corriente de fuga en el path de escritura es la única manera de reducir la energía de lectura en el Punto Mínimo de Energía (MEP). Además, se estudia el efecto del transistor de dimensionamiento en virtud de la presencia de variaciones de voltaje de umbral en la media de energia de lecture MEP mediante el análisis estadístico basado en la prueba de ANOVA del diseño experimental factorial completo.Postprint (published version

Crafting chaos: computational design of contraptions with complex behaviour

The 2010s saw the democratisation of digital fabrication technologies. Although this phenomenon made fabrication more accessible, physical assemblies displaying a complex behaviour are still difficult to design. While many methods support the creation of complex shapes and assemblies, managing a complex behaviour is often assumed to be a tedious aspect of the design process. As a result, the complex parts of the behaviour are either deemed negligible (when possible) or managed directly by the software, without offering much fine-grained user control. This thesis argues that efficient methods can support designers seeking complex behaviours by increasing their level of control over these behaviours. To demonstrate this, I study two types of artistic devices that are particularly challenging to design: drawing machines, and chain reaction contraptions. These artefacts’ complex behaviour can change dramatically even as their components are moved by a small amount. The first case study aims to facilitate the exploration and progressive refinement of complex patterns generated by drawing machines under drawing-level user-defined constraints. The approach was evaluated with a user study, and several machines drawing the expected pattern were fabricated. In the second case study, I propose an algorithm to optimise the layout of complex chain reaction contraptions described by a causal graph of events in order to make them robust to uncertainty. Several machines optimised with this method were successfully assembled and run. This thesis makes the following contributions: (1) support complex behaviour specifications; (2) enable users to easily explore design variations that respect these specifications; and (3) optimise the layout of a physical assembly to maximise the probability of real-life success