5 resultados para Regime switching
em CaltechTHESIS
Resumo:
A neural network is a highly interconnected set of simple processors. The many connections allow information to travel rapidly through the network, and due to their simplicity, many processors in one network are feasible. Together these properties imply that we can build efficient massively parallel machines using neural networks. The primary problem is how do we specify the interconnections in a neural network. The various approaches developed so far such as outer product, learning algorithm, or energy function suffer from the following deficiencies: long training/ specification times; not guaranteed to work on all inputs; requires full connectivity.
Alternatively we discuss methods of using the topology and constraints of the problems themselves to design the topology and connections of the neural solution. We define several useful circuits-generalizations of the Winner-Take-All circuitthat allows us to incorporate constraints using feedback in a controlled manner. These circuits are proven to be stable, and to only converge on valid states. We use the Hopfield electronic model since this is close to an actual implementation. We also discuss methods for incorporating these circuits into larger systems, neural and nonneural. By exploiting regularities in our definition, we can construct efficient networks. To demonstrate the methods, we look to three problems from communications. We first discuss two applications to problems from circuit switching; finding routes in large multistage switches, and the call rearrangement problem. These show both, how we can use many neurons to build massively parallel machines, and how the Winner-Take-All circuits can simplify our designs.
Next we develop a solution to the contention arbitration problem of high-speed packet switches. We define a useful class of switching networks and then design a neural network to solve the contention arbitration problem for this class. Various aspects of the neural network/switch system are analyzed to measure the queueing performance of this method. Using the basic design, a feasible architecture for a large (1024-input) ATM packet switch is presented. Using the massive parallelism of neural networks, we can consider algorithms that were previously computationally unattainable. These now viable algorithms lead us to new perspectives on switch design.
Resumo:
n-heptane/air premixed turbulent flames in the high-Karlovitz portion of the thin reaction zone regime are characterized and modeled in this thesis using Direct Numerical Simulations (DNS) with detailed chemistry. In order to perform these simulations, a time-integration scheme that can efficiently handle the stiffness of the equations solved is developed first. A first simulation with unity Lewis number is considered in order to assess the effect of turbulence on the flame in the absence of differential diffusion. A second simulation with non-unity Lewis numbers is considered to study how turbulence affects differential diffusion. In the absence of differential diffusion, minimal departure from the 1D unstretched flame structure (species vs. temperature profiles) is observed. In the non-unity Lewis number case, the flame structure lies between that of 1D unstretched flames with "laminar" non-unity Lewis numbers and unity Lewis number. This is attributed to effective Lewis numbers resulting from intense turbulent mixing and a first model is proposed. The reaction zone is shown to be thin for both flames, yet large chemical source term fluctuations are observed. The fuel consumption rate is found to be only weakly correlated with stretch, although local extinctions in the non-unity Lewis number case are well correlated with high curvature. These results explain the apparent turbulent flame speeds. Other variables that better correlate with this fuel burning rate are identified through a coordinate transformation. It is shown that the unity Lewis number turbulent flames can be accurately described by a set of 1D (in progress variable space) flamelet equations parameterized by the dissipation rate of the progress variable. In the non-unity Lewis number flames, the flamelet equations suggest a dependence on a second parameter, the diffusion of the progress variable. A new tabulation approach is proposed for the simulation of such flames with these dimensionally-reduced manifolds.
Resumo:
The complex domain structure in ferroelectrics gives rise to electromechanical coupling, and its evolution (via domain switching) results in a time-dependent (i.e. viscoelastic) response. Although ferroelectrics are used in many technological applications, most do not attempt to exploit the viscoelastic response of ferroelectrics, mainly due to a lack of understanding and accurate models for their description and prediction. Thus, the aim of this thesis research is to gain better understanding of the influence of domain evolution in ferroelectrics on their dynamic mechanical response. There have been few studies on the viscoelastic properties of ferroelectrics, mainly due to a lack of experimental methods. Therefore, an apparatus and method called Broadband Electromechanical Spectroscopy (BES) was designed and built. BES allows for the simultaneous application of dynamic mechanical and electrical loading in a vacuum environment. Using BES, the dynamic stiffness and loss tangent in bending and torsion of a particular ferroelectric, viz. lead zirconate titanate (PZT), was characterized for different combinations of electrical and mechanical loading frequencies throughout the entire electric displacement hysteresis. Experimental results showed significant increases in loss tangent (by nearly an order of magnitude) and compliance during domain switching, which shows promise as a new approach to structural damping. A continuum model of the viscoelasticity of ferroelectrics was developed, which incorporates microstructural evolution via internal variables and associated kinetic relations. For the first time, through a new linearization process, the incremental dynamic stiffness and loss tangent of materials were computed throughout the entire electric displacement hysteresis for different combinations of mechanical and electrical loading frequencies. The model accurately captured experimental results. Using the understanding gained from the characterization and modeling of PZT, two applications of domain switching kinetics were explored by using Micro Fiber Composites (MFCs). Proofs of concept of set-and-hold actuation and structural damping using MFCs were demonstrated.
Resumo:
This thesis puts forth a theory-directed approach coupled with spectroscopy aimed at the discovery and understanding of light-matter interactions in semiconductors and metals.
The first part of the thesis presents the discovery and development of Zn-IV nitride materials.The commercial prominence in the optoelectronics industry of tunable semiconductor alloy materials based on nitride semiconductor devices, specifically InGaN, motivates the search for earth-abundant alternatives for use in efficient, high-quality optoelectronic devices. II-IV-N2 compounds, which are closely related to the wurtzite-structured III-N semiconductors, have similar electronic and optical properties to InGaN namely direct band gaps, high quantum efficiencies and large optical absorption coefficients. The choice of different group II and group IV elements provides chemical diversity that can be exploited to tune the structural and electronic properties through the series of alloys. The first theoretical and experimental investigation of the ZnSnxGe1−xN2 series as a replacement for III-nitrides is discussed here.
The second half of the thesis shows ab−initio calculations for surface plasmons and plasmonic hot carrier dynamics. Surface plasmons, electromagnetic modes confined to the surface of a conductor-dielectric interface, have sparked renewed interest because of their quantum nature and their broad range of applications. The decay of surface plasmons is usually a detriment in the field of plasmonics, but the possibility to capture the energy normally lost to heat would open new opportunities in photon sensors, energy conversion devices and switching. A theoretical understanding of plasmon-driven hot carrier generation and relaxation dynamics in the ultrafast regime is presented here. Additionally calculations for plasmon-mediated upconversion as well as an energy-dependent transport model for these non-equilibrium carriers are shown.
Finally, this thesis gives an outlook on the potential of non-equilibrium phenomena in metals and semiconductors for future light-based technologies.
Resumo:
Yields were measured for 235U sputtered from UF4 by 16O, 19F, and 35Cl over the energy range ~.12 to 1.5 MeV/ amu sing a charge equilibrated beam in the stripped beam arrangement for all the incident ions and in the transmission arrangement for 19F and 35Cl. In addition, yields were measured for 19F incident in a wide range of discrete charge states. The angular dependence of all the measured yields were consistent with cosʋ. The stripped beam and transmission data were well fit by the form (Az2eqln(BƐ)/Ɛ)4 (where Ɛ was the ion energy in MeV/amu and zeq(Ɛ) was taken from Zeigler(80). The fitted values of B for the various sets of data were consistent with a constant B0, equal to 36.3 ± 2.7, independent of incident ion. The fitted values of A show no consistent variation with incident ion although a difference can be noted between the stripped beam and transmission values, the transmission values being higher.
The incident charge data were well fit by the assumptions that the sputtering yield depended locally on a power of the incident ion charge and that the sputtering from the surface is exponentially correlated to conditions in the bulk. The equilibrated sputtering yields derived from these data are in agreement with the stripped beam yields.
In addition, to aid in the understanding of these data, the data of Hakansson(80,81a,81b) were examined and contrasted with the UF4 results. The thermal models of Seiberling(80) and Watson(81) were discussed and compared to the data.