Interrogating Seyferts with NebulaBayes: Spatially probing the narrow-line region radiation fields and chemical abundances

NebulaBayes is a new Bayesian code that implements a general method of comparing observed emission-line fluxes to photoionization model grids. The code enables us to extract robust, spatially resolved measurements of abundances in the extended narrow line regions (ENLRs) produced by Active Galactic Nuclei (AGN). We observe near-constant ionization parameters but steeply radially-declining pressures, which together imply that radiation pressure regulates the ENLR density structure on large scales. Our sample includes four `pure Seyfert' galaxies from the S7 survey that have extensive ENLRs. NGC2992 shows steep metallicity gradients from the nucleus into the ionization cones. An {\it inverse} metallicity gradient is observed in ESO138-G01, which we attribute to a recent gas inflow or minor merger. A uniformly high metallicity and hard ionizing continuum are inferred across the ENLR of Mrk573. Our analysis of IC5063 is likely affected by contamination from shock excitation, which appears to soften the inferred ionizing spectrum. The peak of the ionizing continuum E_peak is determined by the nuclear spectrum and the absorbing column between the nucleus and the ionized nebula. We cannot separate variation in this intrinsic E_peak from the effects of shock or HII region contamination, but E_peak measurements nevertheless give insights into ENLR excitation. We demonstrate the general applicability of NebulaBayes by analyzing a nuclear spectrum from the non-active galaxy NGC4691 using a HII region grid. The NLR and HII region model grids are provided with NebulaBayes for use by the astronomical community.Comment: Accepted for publication in ApJ; 29 pages with 10 figures and 3 table

A proton source in the ALPHA apparatus for precision measurements of antihydrogen and hydrogen

The apparent lack of antimatter within our local solar system, the Milky Way, and at Galactic boundaries is inconsistent with the Big Bang hypothesis. This disagreement has motivated many experiments to compare the properties and behaviour of antimatter and matter. The ALPHA (Antihydrogen Laser PHysics Apparatus) experiment produce, trap and study antihydrogen. This synthesis involves antiprotons sourced from the limited schedule of the Antiproton Decelerator facility. The restricted availability hinders the number of novel antiproton experiments, whichcould potentially increase the number of trapped antihydrogen atoms per Antiproton Decelerator cycle. Some of these studies can be performed using a substitute for antiprotons, such as protons, allowing the limited antiprotons to be used during the implementation of their results. This research demonstrates a method that adapts an existing Penning trap to produce protons on demand within reasonable operating time scales of minutes. The availability of protons enables the consideration of new physics studies within ALPHA, including hydrogen formation, trapping, and possibly in situ hydrogen-antihydrogen comparisons. The study produced protons from radiofrequency-drivenelectrons through electron impact ionisation of the cryogenic Penning trap residual gas. The resulting positive ions were sympathetically cooled and compressed by positrons. All positive ions, except protons, were ejected from the trapping potentials using the autoresonance method. The remaining trapped population is approximately (0.9 − 1.2) × 106 protons. This research proves the feasibility of generating protons within the ALPHA apparatus, paving the way for future prospects of hydrogen generation

Hi-Fidelity Simulation of the Self-Assembly and Dynamics of Colloids and Polymeric Solutions with Long Range Interactions

Modeling the equilibrium properties and dynamic response of the colloidal and polymeric solutions provides valuable insight into numerous biological and industrial processes and facilitates development of novel technologies. To this end, the centerpiece of this research is to incorporate the long range electrostatic or hydrodynamic interactions via computationally efficient algorithms and to investigate the effect of these interactions on the self-assembly of colloidal particles and dynamic properties of polymeric solutions. Specifically, self-assembly of a new class of materials, namely bipolar Janus nano-particles, is investigated via molecular dynamic simulation in order to establish the relationship between individual particle characteristics, such as surface charge density, particle size, etc., and the final structure formation. Furthermore, the importance of incorporating the long range electrostatic interaction in achieving the corresponding final morphology is discussed. The dynamic properties of polymeric solutions are investigated via two parallel pathways. In the first approach, force-extension behavior of the flexible polyelectrolytes is probed via fine-grained Brownian dynamics simulation of the bead-rod model. The presented model accurately incorporates the excluded volume interaction in order to capture the effect of salt concentration on the force-extension response of polyelectrolyte chain as observed in the single chain experiments. It is shown that accurate incorporation of the excluded volume effect on a long chain of more than 500 Kuhn segments is necessary to reach the universal scaling both for equilibrium properties and force-extension response. Next, a new force law is extracted using a novel discrete Pade approximant from the constant-force ensemble result of the bead-rod model. The new force law is implemented in the coarse-grained meso-scale bead-spring model with hydrodynamic interactions in order to investigate the dynamics of flexible macromolecules in the athermal solvent. In the second approach the computational cost of the long range hydrodynamic interaction in dilute solution of polymeric chains with constrains is reduced via development of a new computational technique based on the conjugate gradient and Krylov subspace methods. Moreover, an algorithm for estimating the contribution of various forces to the transient polymeric stress tensor is introduced and employed in order to investigate transient dynamics of the solution of the flexible polymeric chains

Tunable Compressive Mechanical Behavior of Ice-Templated Materials

The inherent hierarchical microstructural organization in natural materials is responsible for their excellent mechanical properties beyond that predicted by the simple rule-of-mixtures. Further exhibit synergy between strength and toughness, otherwise mutually exclusive in brittle materials. Conventional processing methods are unable to replicate hierarchical microstructures in engineering ceramics akin to that observed in natural materials. Ice-templating has emerged as a potential technique to fabricate bioinspired hierarchical materials. This process involves simultaneous unidirectional solidification and phase segregation of aqueous suspensions. Ice-templated porous ceramic materials have received significant attention for overcoming several limitations of conventional ceramic foams currently used in numerous engineering applications. The purpose of this dissertation is many-folds with a central theme of tunable compressive response, which has been revealed by investigating microstructure-mechanical property relationships in both ice-templated ceramics and infiltrated composites. First, this dissertation developed a novel extrinsic methodology. The alternating current (AC) electric field was uniquely employed to tailor ice-templated microstructure and mechanical properties. This research revealed that local suspension concentration could be effectively manipulated by applying AC electric fields to aqueous ceramic suspensions, providing a novel approach to tune ice-templated microstructure and remarkably enhance compressive mechanical properties without compromising porosity. Next, this dissertation investigated the loading orientation dependence of the compressive response of freeze cast ceramic-polymer composites, motivated by the structural gradient observed in natural materials. The results revealed a strong orientation dependence of compressive response and failure behavior (catastrophic vs. progressive). Furthermore, this dissertation investigated inelastic deformation mechanisms that evolve in these composites under compressive loading conditions and cause macroscopic failure. It was revealed that the deformation mechanisms were also strongly influenced by the loading orientation. Finally, this dissertation investigated compressive response and damage evolution in ice-templated ceramics and composites under high-strain rate (dynamic) loading conditions. Ice-templated materials with higher porosity exhibited progressive crushing type damage evolution, irrespective of the strain rate regime. The results suggested greater structural stability in ice-templated ceramics at high-strain rates. This dissertation also investigated the influence of microstructure on the impact behavior of ice-templated sintered alumina materials and the relationship between dynamic compressive strength and impact response

Programming by Demonstration on Riemannian Manifolds

This thesis presents a Riemannian approach to Programming by Demonstration (PbD). It generalizes an existing PbD method from Euclidean manifolds to Riemannian manifolds. In this abstract, we review the objectives, methods and contributions of the presented approach. OBJECTIVES PbD aims at providing a user-friendly method for skill transfer between human and robot. It enables a user to teach a robot new tasks using few demonstrations. In order to surpass simple record-and-replay, methods for PbD need to \u2018understand\u2019 what to imitate; they need to extract the functional goals of a task from the demonstration data. This is typically achieved through the application of statisticalmethods. The variety of data encountered in robotics is large. Typical manipulation tasks involve position, orientation, stiffness, force and torque data. These data are not solely Euclidean. Instead, they originate from a variety of manifolds, curved spaces that are only locally Euclidean. Elementary operations, such as summation, are not defined on manifolds. Consequently, standard statistical methods are not well suited to analyze demonstration data that originate fromnon-Euclidean manifolds. In order to effectively extract what-to-imitate, methods for PbD should take into account the underlying geometry of the demonstration manifold; they should be geometry-aware. Successful task execution does not solely depend on the control of individual task variables. By controlling variables individually, a task might fail when one is perturbed and the others do not respond. Task execution also relies on couplings among task variables. These couplings describe functional relations which are often called synergies. In order to understand what-to-imitate, PbDmethods should be able to extract and encode synergies; they should be synergetic. In unstructured environments, it is unlikely that tasks are found in the same scenario twice. The circumstances under which a task is executed\u2014the task context\u2014are more likely to differ each time it is executed. Task context does not only vary during task execution, it also varies while learning and recognizing tasks. To be effective, a robot should be able to learn, recognize and synthesize skills in a variety of familiar and unfamiliar contexts; this can be achieved when its skill representation is context-adaptive. THE RIEMANNIAN APPROACH In this thesis, we present a skill representation that is geometry-aware, synergetic and context-adaptive. The presented method is probabilistic; it assumes that demonstrations are samples from an unknown probability distribution. This distribution is approximated using a Riemannian GaussianMixtureModel (GMM). Instead of using the \u2018standard\u2019 Euclidean Gaussian, we rely on the Riemannian Gaussian\u2014 a distribution akin the Gaussian, but defined on a Riemannian manifold. A Riev mannian manifold is a manifold\u2014a curved space which is locally Euclidean\u2014that provides a notion of distance. This notion is essential for statistical methods as such methods rely on a distance measure. Examples of Riemannian manifolds in robotics are: the Euclidean spacewhich is used for spatial data, forces or torques; the spherical manifolds, which can be used for orientation data defined as unit quaternions; and Symmetric Positive Definite (SPD) manifolds, which can be used to represent stiffness and manipulability. The Riemannian Gaussian is intrinsically geometry-aware. Its definition is based on the geometry of the manifold, and therefore takes into account the manifold curvature. In robotics, the manifold structure is often known beforehand. In the case of PbD, it follows from the structure of the demonstration data. Like the Gaussian distribution, the Riemannian Gaussian is defined by a mean and covariance. The covariance describes the variance and correlation among the state variables. These can be interpreted as local functional couplings among state variables: synergies. This makes the Riemannian Gaussian synergetic. Furthermore, information encoded in multiple Riemannian Gaussians can be fused using the Riemannian product of Gaussians. This feature allows us to construct a probabilistic context-adaptive task representation. CONTRIBUTIONS In particular, this thesis presents a generalization of existing methods of PbD, namely GMM-GMR and TP-GMM. This generalization involves the definition ofMaximum Likelihood Estimate (MLE), Gaussian conditioning and Gaussian product for the Riemannian Gaussian, and the definition of ExpectationMaximization (EM) and GaussianMixture Regression (GMR) for the Riemannian GMM. In this generalization, we contributed by proposing to use parallel transport for Gaussian conditioning. Furthermore, we presented a unified approach to solve the aforementioned operations using aGauss-Newton algorithm. We demonstrated how synergies, encoded in a Riemannian Gaussian, can be transformed into synergetic control policies using standard methods for LinearQuadratic Regulator (LQR). This is achieved by formulating the LQR problem in a (Euclidean) tangent space of the Riemannian manifold. Finally, we demonstrated how the contextadaptive Task-Parameterized Gaussian Mixture Model (TP-GMM) can be used for context inference\u2014the ability to extract context from demonstration data of known tasks. Our approach is the first attempt of context inference in the light of TP-GMM. Although effective, we showed that it requires further improvements in terms of speed and reliability. The efficacy of the Riemannian approach is demonstrated in a variety of scenarios. In shared control, the Riemannian Gaussian is used to represent control intentions of a human operator and an assistive system. Doing so, the properties of the Gaussian can be employed to mix their control intentions. This yields shared-control systems that continuously re-evaluate and assign control authority based on input confidence. The context-adaptive TP-GMMis demonstrated in a Pick & Place task with changing pick and place locations, a box-taping task with changing box sizes, and a trajectory tracking task typically found in industr

A Framework for Collaborative Multi-task, Multi-robot Missions

Robotics is a transformative technology that will empower our civilization for a new scale of human endeavors. Massive scale is only possible through the collaboration of individual or groups of robots. Collaboration allows specialization, meaning a multirobot system may accommodate heterogeneous platforms including human partners. This work develops a unified control architecture for collaborative missions comprised of multiple, multi-robot tasks. Using kinematic equations and Jacobian matrices, the system states are transformed into alternative control spaces which are more useful for the designer or more convenient for the operator. The architecture allows multiple tasks to be combined, composing tightly coordinated missions. Using this approach, the designer is able to compensate for non-ideal behavior in the appropriate space using whatever control scheme they choose. This work presents a general design methodology, including analysis techniques for relevant control metrics like stability, responsiveness, and disturbance rejection, which were missing in prior work. Multiple tasks may be combined into a collaborative mission. The unified motion control architecture merges the control space components for each task into a concise federated system to facilitate analysis and implementation. The task coordination function defines task commands as functions of mission commands and state values to create explicit closed-loop collaboration. This work presents analysis techniques to understand the effects of cross-coupling tasks. This work analyzes system stability for the particular control architecture and identifies an explicit condition to ensure stable switching when reallocating robots. We are unaware of any other automated control architectures that address large-scale collaborative systems composed of task-oriented multi-robot coalitions where relative spatial control is critical to mission performance. This architecture and methodology have been validated in experiments and in simulations, repeating earlier work and exploring new scenarios and. It can perform large-scale, complex missions via a rigorous design methodology

Simulation of fluid suspended particle behaviour subject to transverse standing acoustic fields

The computational study addressed the effectiveness with which a standing wave acoustic field could be used to deflect quartz particles carried in water at 20°C through a simple parallelepiped control volume representative of a vertically orientated duct geometry dimensioned 50 × 50 × 70 cm 3 , with square base. An acoustically driven planar standing wave field produces quasi-static oscillatory pressure gradients within resonant cavities, which are responsible for acoustic forces, which act on particles, located within the acoustic field. These forces drive particles to nodal (no fluctuation) or anti-nodal (continuous fluctuation) planes of pressure. Standing wave fields are generally produced by a transducer driving into a fluid through an adhesively bonded matching layer. The wave is reflected at the opposite boundary layer terminating in an air backing. The chamber is dimensioned so as to produce constructive wave interference between the two waves travelling in opposite directions. The acoustic force has been used in small scale filtration systems to deflect particles and on larger scales as a pre filtration agglomerator clumping very small particles which are otherwise poorly filtered in isolation by conventional methods. The study was twofold, in that a major component of the study comprised developing the architecture of the computational model, the other part comprising qualitative model validation through parameter variation. The study involved coupling between Computational Fluid Dynamics (CFD) Software (OpenFOAM) and Discrete Element Modelling (DEM) Software (LIGGGHTS), through a coupling code (CFDEM) built as an extension to OpenFOAM and tailored for LIGGGHTS. The acoustic field was assumed ideal i.e. in a lossless medium with perfect reflection at the opposite wall. Particle-particle and particle-wall collisions were circumvented by using larger time increments, inadequate to resolve col- lisions, and inserting particles in the bulk of the flow away from any wall boundary. Twenty particles with uniform radial size distribution in the range 5-30 micron were seeded in the flow field about 10 cm from the bottom inlet, and carried in the z direction at various flow speeds, 0.1 ms − 1 , 0.5 ms − 1 and 1 ms − 1 , whilst being subject to acoustic forces in the x direction, to investigate deflection response and transducer lengths required to achieve adequate lateral deflection. The model accounted for drag, buoyancy, gravity and primary acoustic forces. Flow velocities distinguished by those maxi mum velocities recorded at duct centrelines were obtained by adjusting pressure gradients across the domain. The fluid continuum was modelled through Reynolds Averaged Navier Stokes (RANS) equa- tions, supplemented by an eddy viscosity k − two equation turbulence model. The flow profile was validated against the analytic Darcy-Weisbach pressure to mean velocity relation. Two acoustic driv- ing frequencies, 14794 Hz and 26629 Hz , were investigated for each flow rate to determine the effect frequency had on acoustic force magnitude, nodal distribution and particle residence time. Acoustic deflection efficiency was measured as that time or particle vertical travel length required, coinciding with a lateral deflection to within 1.5 mm of an adjacent nodal plane. From a computational point of interest acoustic force dependencies and trends were qualitatively evaluated for consistency with theoretic equations and published literatur

Polarizable Particles and their Two-Dimensional Arrays: Advances in Small Antenna and Metasurface Technologies.

Metamaterials are subwavelength-structured materials designed to exhibit tailored electromagnetic properties. Metamaterials have allowed extreme control over constituent material parameters (i.e. permittivity, permeability, and chirality), which has enabled a myriad of counterintuitive physical phenomena. However, metamaterials typically suffer from high losses, difficulties in fabrication, and are bulky. This has led to the development of metasurfaces, which are the two dimensional equivalent of metamaterials. Metasurfaces can impart abrupt discontinuities on electromagnetic wavefronts, allowing electromagnetic fields to be tailored across subwavelength length scales. The building blocks of metasurfaces are subwavelength textured, polarizable particles. Near resonance, these particles support strong currents, which makes them excellent small antennas. In this thesis, a circuit model is developed that can model an arbitrary small antenna based on its frequency dependent polarizability. In addition, a direct transfer patterning process is developed that allows metallic patterns to be printed onto arbitrarily contoured substrates. This work will find immediate applications in a number of emerging technologies resulting from the rapid expansion of the mobile electronics industry. Next, extreme control of the polarization and profile of a wavefront is demonstrated using two-dimensional arrays of polarizable particles (i.e. metasurfaces). A new class of metasurfaces, referred to as metamaterial Huygens' surfaces, is shown to have a significantly improved efficiency over the state of the art. Metamaterial Huygens' surfaces utilize polarizable particles that exhibit both an electric and magnetic response, which allows for reflectionless wavefront control. Next, it is shown that simply cascading patterned metallic sheets can also provide high transmission and complete phase control. To demonstrate the design methodology, several different metasurfaces are developed that deflect incident Gaussian beams to a stipulated angle or convert an incident Gaussian beam into a vector Bessel beam. Further, utilizing sheets with anisotropic patterns provides additional magneto-electric coupling, which enables complete control of a wavefront (i.e. amplitude, phase, and polarization control). The experimental verification at frequencies ranging from microwaves to optics highlights the versatility of this work.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111395/1/carlpfei_1.pd

Kinematics and Robot Design I, KaRD2018

This volume collects the papers published on the Special Issue “Kinematics and Robot Design I, KaRD2018” (https://www.mdpi.com/journal/robotics/special_issues/KARD), which is the first issue of the KaRD Special Issue series, hosted by the open access journal “MDPI Robotics”. The KaRD series aims at creating an open environment where researchers can present their works and discuss all the topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”. KaRD2018 received 22 papers and, after the peer-review process, accepted only 14 papers. The accepted papers cover some theoretical and many design/applicative aspects