Simple eye-closure penalty estimate for amplitude noise-degraded signals

We present a simplified model for a simple estimation of the eye-closure penalty for amplitude noise-degraded signals. Using a typical 40-Gbit/s return-to-zero amplitude-shift-keying transmission, we demonstrate agreement between the model predictions and the results obtained from the conventional numerical estimation method over several thousand kilometers.

A Search for a New Particle Decaying to Pairs of Weak Gauge Bosons with the ATLAS Detector

A search for new heavy resonances decaying to boson pairs (WZ, WW or ZZ) using 20.3 inverse femtobarns of proton-proton collision data at a center of mass energy of 8 TeV is presented. The data were recorded by the ATLAS detector at the Large Hadron Collider (LHC) in 2012. The analysis combines several search channels with the leptonic, semi-leptonic and fully hadronic final states. The diboson invariant mass spectrum is studied for local excesses above the Standard Model background prediction, and no significant excess is observed for the combined analysis. 95$\%$ confidence limits are set on the cross section times branching ratios for three signal models: an extended gauge model with a heavy W boson, a bulk Randall-Sundrum model with a spin-2 graviton, and a simplified model with a heavy vector triplet. Among the individual search channels, the fully-hadronic channel is predominantly presented where boson tagging technique and jet substructure cuts are used. Local excesses are found in the dijet mass distribution around 2 TeV, leading to a global significance of 2.5 standard deviations. This deviation from the Standard Model prediction results in many theory explanations, and the possibilities could be further explored using the LHC Run 2 data.

Linking Parametric CAD with Adjoint Surface Sensitivities

The goal of this work is to present an efficient CAD-based adjoint process chain for calculating parametric sensitivities (derivatives of the objective function with respect to the CAD parameters) in timescales acceptable for industrial design processes. The idea is based on linking parametric design velocities (geometric sensitivities computed from the CAD model) with adjoint surface sensitivities. A CAD-based design velocity computation method has been implemented based on distances between discrete representations of perturbed geometries. This approach differs from other methods due to the fact that it works with existing commercial CAD packages (unlike most analytical approaches) and it can cope with the changes in CAD model topology and face labeling. Use of the proposed method allows computation of parametric sensitivities using adjoint data at a computational cost which scales with the number of objective functions being considered, while it is essentially independent of the number of design variables. The gradient computation is demonstrated on test cases for a Nozzle Guide Vane (NGV) model and a Turbine Rotor Blade model. The results are validated against finite difference values and good agreement is shown. This gradient information can be passed to an optimization algorithm, which will use it to update the CAD model parameters.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Sparse Signal Representation in Digital and Biological Systems

Theories of sparse signal representation, wherein a signal is decomposed as the sum of a small number of constituent elements, play increasing roles in both mathematical signal processing and neuroscience. This happens despite the differences between signal models in the two domains. After reviewing preliminary material on sparse signal models, I use work on compressed sensing for the electron tomography of biological structures as a target for exploring the efficacy of sparse signal reconstruction in a challenging application domain. My research in this area addresses a topic of keen interest to the biological microscopy community, and has resulted in the development of tomographic reconstruction software which is competitive with the state of the art in its field. Moving from the linear signal domain into the nonlinear dynamics of neural encoding, I explain the sparse coding hypothesis in neuroscience and its relationship with olfaction in locusts. I implement a numerical ODE model of the activity of neural populations responsible for sparse odor coding in locusts as part of a project involving offset spiking in the Kenyon cells. I also explain the validation procedures we have devised to help assess the model's similarity to the biology. The thesis concludes with the development of a new, simplified model of locust olfactory network activity, which seeks with some success to explain statistical properties of the sparse coding processes carried out in the network.

Examining the growth and evolution of magnetic fields in clusters of galaxies: a numerical perspective

Magnetic fields are ubiquitous in galaxy cluster atmospheres and have a variety of astrophysical and cosmological consequences. Magnetic fields can contribute to the pressure support of clusters, affect thermal conduction, and modify the evolution of bubbles driven by active galactic nuclei. However, we currently do not fully understand the origin and evolution of these fields throughout cosmic time. Furthermore, we do not have a general understanding of the relationship between magnetic field strength and topology and other cluster properties, such as mass and X-ray luminosity. We can now begin to answer some of these questions using large-scale cosmological magnetohydrodynamic (MHD) simulations of the formation of galaxy clusters including the seeding and growth of magnetic fields. Using large-scale cosmological simulations with the FLASH code combined with a simplified model of the acceleration of cosmic rays responsible for the generation of radio halos, we find that the galaxy cluster frequency distribution and expected number counts of radio halos from upcoming low-frequency sur- veys are strongly dependent on the strength of magnetic fields. Thus, a more complete understanding of the origin and evolution of magnetic fields is necessary to understand and constrain models of diffuse synchrotron emission from clusters. One favored model for generating magnetic fields is through the amplification of weak seed fields in active galactic nuclei (AGN) accretion disks and their subsequent injection into cluster atmospheres via AGN-driven jets and bubbles. However, current large-scale cosmological simulations cannot directly include the physical processes associated with the accretion and feedback processes of AGN or the seeding and merging of the associated SMBHs. Thus, we must include these effects as subgrid models. In order to carefully study the growth of magnetic fields in clusters via AGN-driven outflows, we present a systematic study of SMBH and AGN subgrid models. Using dark-matter only cosmological simulations, we find that many important quantities, such as the relationship between SMBH mass and galactic bulge velocity dispersion and the merger rate of black holes, are highly sensitive to the subgrid model assumptions of SMBHs. In addition, using MHD calculations of an isolated cluster, we find that magnetic field strengths, extent, topology, and relationship to other gas quantities such as temperature and density are also highly dependent on the chosen model of accretion and feedback. We use these systematic studies of SMBHs and AGN inform and constrain our choice of subgrid models, and we use those results to outline a fully cosmological MHD simulation to study the injection and growth of magnetic fields in clusters of galaxies. This simulation will be the first to study the birth and evolution of magnetic fields using a fully closed accretion-feedback cycle, with as few assumptions as possible and a clearer understanding of the effects of the various parameter choices.

Advanced parametrical modelling of 24 GHz radar sensor IC packaging components

This paper deals with the development of an advanced parametrical modelling concept for packaging components of a 24 GHz radar sensor IC used in automotive driver assistance systems. For fast and efficient design of packages for system-in-package modules (SiP), a simplified model for the description of parasitic electromagnetic effects within the package is desirable, as 3-D field computation becomes inefficient due to the high density of conductive elements of the various signal paths in the package. By using lumped element models for the characterization of the conductive components, a fast indication of the design's signal-quality can be gained, but so far does not offer enough flexibility to cover the whole range of geometric arrangements of signal paths in a contemporary package. This work pursues to meet the challenge of developing a flexible and fast package modelling concept by defining parametric lumped-element models for all basic signal path components, e.g. bond wires, vias, strip lines, bumps and balls. © Author(s) 2011. CC Attribution 3.0 License.

Influência de feixes de condutores em linhas de transmissão UAT CA

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.

Phase transitions in gene networks evolved under different selection rules

Mathematical models of gene regulation are a powerful tool for understanding the complex features of genetic control. While various modeling efforts have been successful at explaining gene expression dynamics, much less is known about how evolution shapes the structure of these networks. An important feature of gene regulatory networks is their stability in response to environmental perturbations. Regulatory systems are thought to have evolved to exist near the transition between stability and instability, in order to have the required stability to environmental fluctuations while also being able to achieve a wide variety of functions (corresponding to different dynamical patterns). We study a simplified model of gene network evolution in which links are added via different selection rules. These growth models are inspired by recent work on `explosive' percolation which shows that when network links are added through competitive rather than random processes, the connectivity phase transition can be significantly delayed, and when it is reached, it appears to be first order (discontinuous, e.g., going from no failure at all to large expected failure) instead of second order (continuous, e.g., going from no failure at all to very small expected failure). We find that by modifying the traditional framework for networks grown via competitive link addition to capture how gene networks evolve to avoid damage propagation, we also see significant delays in the transition that depend on the selection rules, but the transitions always appear continuous rather than `explosive'.

Cork structural characteristics and their influence on the oxygen ingress through wine stoppers

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de Agronomia - UL

Stability issues in racing motorcycles: an in-depth analysis of the chatter vibration

Racing motorcycles are prone to an unstable oscillatory motion of the swingarm and rear wheel, commonly known as ‘chatter’. This vibration mode typically has a frequency of 17 Hz to 22 Hz and typically occurs during heavy braking manoeuvres. The appearance of chatter can cause reduced rider confidence, and therefore lead to longer lap times during races and the increased risk of crashing. This thesis looks to further the understanding of this mode. It includes the development of a simplified model to explore the effects roll angle and lateral dynamics have on the chatter mode using linear analysis. The mechanisms of instability and parameter sensitivities are also examined. The effects of the nonlinearities present in the minimal model equations of motion are examined, including the identification of limit cycles and their stability, inspecting individual nonlinear terms and their effects, and introducing tyre relaxation and determining the effect it has on the dynamics. Finally, an exploratory study of the mid-corner region of a typical racing manoeuvre is performed in hopes to better understand if any high frequency tyre induced instabilities like chatter can occur.

Electromagnetic Design of a Solid Steel Rotor Motor for Demanding Operation Environments

The solid-rotor induction motor provides a mechanically and thermally reliable solution for demanding environments where other rotor solutions are prohibited or questionable. Solid rotors, which are manufactured of single pieces of ferromagnetic material, are commonly used in motors in which the rotationspeeds exceed substantially the conventional speeds of laminated rotors with squirrel-cage. During the operation of a solid-rotor electrical machine, the rotor core forms a conductor for both the magnetic flux and the electrical current. This causes an increase in the rotor resistance and rotor leakage inductance, which essentially decreases the power factor and the efficiency of the machine. The electromagnetic problems related to the solid-rotor induction motor are mostly associated with the low performance of the rotor. Therefore, the main emphasis in this thesis is put on the solid steel rotor designs. The rotor designs studied in thisthesis are based on the fact that the rotor construction should be extremely robust and reliable to withstand the high mechanical stresses caused by the rotational velocity of the rotor. In addition, the demanding operation environment sets requirements for the applied materials because of the high temperatures and oxidizing acids, which may be present in the cooling fluid. Therefore, the solid rotors analyzed in this thesis are made of a single piece of ferromagnetic material without any additional parts, such as copper end-rings or a squirrel-cage. A pure solid rotor construction is rigid and able to keep its balance over a large speed range. It also may tolerate other environmental stresses such as corroding substances or abrasive particles. In this thesis, the main target is to improve the performance of an induction motor equipped with a solid steel rotor by traditional methods: by axial slitting of the rotor, by selecting a proper rotor core material and by coating the rotor with a high-resistive stainless ferromagnetic material. In the solid steel rotor calculation, the rotor end-effects have a significant effect on the rotor characteristics. Thus, the emphasis is also put on the comparison of different rotor endfactors. In addition, a corrective slip-dependent end-factor is proposed. The rotor designs covered in this thesis are the smooth solid rotor, the axially slitted solid rotor and the slitted rotor having a uniform ferromagnetic coating cylinder. The thesis aims at design rules for multi-megawatt machines. Typically, mega-watt-size solidrotor machines find their applications mainly in the field of electric-motor-gas-compression systems, in steam-turbine applications, and in various types of largepower pump applications, where high operational speeds are required. In this thesis, a 120 kW, 10 000 rpm solid-rotor induction motor is usedas a small-scale model for such megawatt-range solid-rotor machines. The performance of the 120 kW solid-rotor induction motors is determined by experimental measurements and finite element calculations.

Adaptive control of sinusoidal brushless DC motor actuators

Electrical Power Assisted Steering system (EPAS) will likely be used on future automotive power steering systems. The sinusoidal brushless DC (BLDC) motor has been identified as one of the most suitable actuators for the EPAS application. Motor characteristic variations, which can be indicated by variations of the motor parameters such as the coil resistance and the torque constant, directly impart inaccuracies in the control scheme based on the nominal values of parameters and thus the whole system performance suffers. The motor controller must address the time-varying motor characteristics problem and maintain the performance in its long service life. In this dissertation, four adaptive control algorithms for brushless DC (BLDC) motors are explored. The first algorithm engages a simplified inverse dq-coordinate dynamics controller and solves for the parameter errors with the q-axis current (iq) feedback from several past sampling steps. The controller parameter values are updated by slow integration of the parameter errors. Improvement such as dynamic approximation, speed approximation and Gram-Schmidt orthonormalization are discussed for better estimation performance. The second algorithm is proposed to use both the d-axis current (id) and the q-axis current (iq) feedback for parameter estimation since id always accompanies iq. Stochastic conditions for unbiased estimation are shown through Monte Carlo simulations. Study of the first two adaptive algorithms indicates that the parameter estimation performance can be achieved by using more history data. The Extended Kalman Filter (EKF), a representative recursive estimation algorithm, is then investigated for the BLDC motor application. Simulation results validated the superior estimation performance with the EKF. However, the computation complexity and stability may be barriers for practical implementation of the EKF. The fourth algorithm is a model reference adaptive control (MRAC) that utilizes the desired motor characteristics as a reference model. Its stability is guaranteed by Lyapunov’s direct method. Simulation shows superior performance in terms of the convergence speed and current tracking. These algorithms are compared in closed loop simulation with an EPAS model and a motor speed control application. The MRAC is identified as the most promising candidate controller because of its combination of superior performance and low computational complexity. A BLDC motor controller developed with the dq-coordinate model cannot be implemented without several supplemental functions such as the coordinate transformation and a DC-to-AC current encoding scheme. A quasi-physical BLDC motor model is developed to study the practical implementation issues of the dq-coordinate control strategy, such as the initialization and rotor angle transducer resolution. This model can also be beneficial during first stage development in automotive BLDC motor applications.

Intact flagellar motor of Borrelia burgdorferi revealed by cryo-electron tomography: evidence for stator ring curvature and rotor/C-ring assembly flexion.

The bacterial flagellar motor is a remarkable nanomachine that provides motility through flagellar rotation. Prior structural studies have revealed the stunning complexity of the purified rotor and C-ring assemblies from flagellar motors. In this study, we used high-throughput cryo-electron tomography and image analysis of intact Borrelia burgdorferi to produce a three-dimensional (3-D) model of the in situ flagellar motor without imposing rotational symmetry. Structural details of B. burgdorferi, including a layer of outer surface proteins, were clearly visible in the resulting 3-D reconstructions. By averaging the 3-D images of approximately 1,280 flagellar motors, a approximately 3.5-nm-resolution model of the stator and rotor structures was obtained. flgI transposon mutants lacked a torus-shaped structure attached to the flagellar rod, establishing the structural location of the spirochetal P ring. Treatment of intact organisms with the nonionic detergent NP-40 resulted in dissolution of the outermost portion of the motor structure and the C ring, providing insight into the in situ arrangement of the stator and rotor structures. Structural elements associated with the stator followed the curvature of the cytoplasmic membrane. The rotor and the C ring also exhibited angular flexion, resulting in a slight narrowing of both structures in the direction perpendicular to the cell axis. These results indicate an inherent flexibility in the rotor-stator interaction. The FliG switching and energizing component likely provides much of the flexibility needed to maintain the interaction between the curved stator and the relatively symmetrical rotor/C-ring assembly during flagellar rotation.