Monotone positive solutions for a fourth order equation with nonlinear boundary conditions

This work is concerned with the existence of monotone positive solutions for a class of beam equations with nonlinear boundary conditions. The results are obtained by using the monotone iteration method and they extend early works on beams with null boundary conditions. Numerical simulations are also presented. (C) 2009 Elsevier Ltd. All rights reserved.

Calculation of the eigenfunctions of the two-group neutron diffusion equation and application to modal decomposition of BWR instabilities

In this thesis, numerical methods aiming at determining the eigenfunctions, their adjoint and the corresponding eigenvalues of the two-group neutron diffusion equations representing any heterogeneous system are investigated. First, the classical power iteration method is modified so that the calculation of modes higher than the fundamental mode is possible. Thereafter, the Explicitly-Restarted Arnoldi method, belonging to the class of Krylov subspace methods, is touched upon. Although the modified power iteration method is a computationally-expensive algorithm, its main advantage is its robustness, i.e. the method always converges to the desired eigenfunctions without any need from the user to set up any parameter in the algorithm. On the other hand, the Arnoldi method, which requires some parameters to be defined by the user, is a very efficient method for calculating eigenfunctions of large sparse system of equations with a minimum computational effort. These methods are thereafter used for off-line analysis of the stability of Boiling Water Reactors. Since several oscillation modes are usually excited (global and regional oscillations) when unstable conditions are encountered, the characterization of the stability of the reactor using for instance the Decay Ratio as a stability indicator might be difficult if the contribution from each of the modes are not separated from each other. Such a modal decomposition is applied to a stability test performed at the Swedish Ringhals-1 unit in September 2002, after the use of the Arnoldi method for pre-calculating the different eigenmodes of the neutron flux throughout the reactor. The modal decomposition clearly demonstrates the excitation of both the global and regional oscillations. Furthermore, such oscillations are found to be intermittent with a time-varying phase shift between the first and second azimuthal modes.

Climatological analysis of temperature and salinity fields in the Mediterranean sea

A climatological field is a mean gridded field that represents the monthly or seasonal trend of an ocean parameter. This instrument allows to understand the physical conditions and physical processes of the ocean water and their impact on the world climate. To construct a climatological field, it is necessary to perform a climatological analysis on an historical dataset. In this dissertation, we have constructed the temperature and salinity fields on the Mediterranean Sea using the SeaDataNet 2 dataset. The dataset contains about 140000 CTD, bottles, XBT and MBT profiles, covering the period from 1900 to 2013. The temperature and salinity climatological fields are produced by the DIVA software using a Variational Inverse Method and a Finite Element numerical technique to interpolate data on a regular grid. Our results are also compared with a previous version of climatological fields and the goodness of our climatologies is assessed, according to the goodness criteria suggested by Murphy (1993). Finally the temperature and salinity seasonal cycle for the Mediterranean Sea is described.

Physical properties of sediment cores of the Bengal Fan

We examined geophysical data from a Multi-Sensor Core Logger (MSCL), a logging device providing continuous measurements of gamma-ray attenuation, p-wave travel time, and magnetic susceptibility on marine sediment cores. In the first part we focused on the gamma-ray system and compared two different calibration methods. From the gamma-ray attenuation, we calculated densities and porosities by incorporating mass weighted attenuation coefficients. The application of an iteration method reduces the error of the density and porosity estimates compared to GRAPE data. In addition, we derived equations to calculate water content and dry bulk density from gamma-ray attenuation measurements. Comparison with physical properties determined on discrete samples revealed a very good correlation of both data sets (r = 0.99). This correlation is valid for sediments from substantially different geological settings (e.g., turbidites, hemipelagic muds, and opal-rich sediments). In the second part we applied our data to marine geological questions. For sediments from the Antarctic Polar Frontal Zone, there is indication that the content of biogenic opal can be assessed using a correlation of density and p-wave velocity. For sediments from the Bengal Fan, the relationship between the MSCL acoustic impedance (the product of density and p-wave velocity) and the grain-size distribution in discrete samples can be used to predict clay and sand/silt ratios for sediment cores from the shelf and upper continental slope.

Atenuação de vibrações em pás de helicópteros utilizando circuito piezelétrico semi-passivo

O uso de materiais inteligentes em problemas de controle de vibração tem sido investigado em diversas pesquisas ao longo dos últimos anos. Apesar de que diferentes materiais inteligentes estão disponíveis, o piezelétrico tem recebido grande atenção devido à facilidade de uso como sensores, atuadores, ou ambos simultaneamente. As principais técnicas de controle usando materiais piezoelétricos são os ativos e passivos. Circuitos piezelétricos passivos são ajustados para uma frequência específica e, portanto, a largura de banda efetiva é pequena. Embora os sistemas ativos possam apresentar um bom desempenho no controle de vibração, a quantidade de energia externa e hardware adicionado são questões importantes. As técnicas SSD (Synchronized Switch Damping) foram desenvolvidas como uma alternativa aos controladores passivos e controladores ativos de vibração. Elas podem ser técnicas semi-ativas ou semi-passivas que introduzem um tratamento não linear na tensão elétrica proveniente do material piezelétrico e induz um aumento na conversão de energia mecânica para energia elétrica e, consequentemente, um aumento no efeito de amortecimento. Neste trabalho, o controle piezoelétrico semi-passivo de uma pá piezelétrica engastada é apresentado e comparado com outros controladores. O modelo não linear electromecânico de uma pá com piezocerâmicas incorporados é determinado com base no método variacional-assintótico (VAM). O sistema rotativo acoplado não linear é resolvido no domínio do tempo, utilizando um método de integração alfa-generalizado afim de garantir a estabilidade numérica. As simulações são realizadas para uma vasta gama de velocidades de rotação. Em primeiro lugar, um conjunto de resistências (variando desde a condição de curto-circuito para a condição de circuito aberto) é considerada. O efeito da resistência ótima (que resulta em máximo amortecimento) sobre o comportamento do sistema é investigado para o aumento da velocidade de rotação. Mais tarde, a técnica SSDS é utilizada para amortecer as oscilações da pá com o aumento da velocidade de rotação. Os resultados mostram que a técnica SSDS pode ser um método útil para o controle de vibrações de vigas rotativas não lineares, tais como pás de helicóptero.

Data assimilation for precipitation nowcasting using Bayesian inference

This work introduces a new variational Bayes data assimilation method for the stochastic estimation of precipitation dynamics using radar observations for short term probabilistic forecasting (nowcasting). A previously developed spatial rainfall model based on the decomposition of the observed precipitation field using a basis function expansion captures the precipitation intensity from radar images as a set of ‘rain cells’. The prior distributions for the basis function parameters are carefully chosen to have a conjugate structure for the precipitation field model to allow a novel variational Bayes method to be applied to estimate the posterior distributions in closed form, based on solving an optimisation problem, in a spirit similar to 3D VAR analysis, but seeking approximations to the posterior distribution rather than simply the most probable state. A hierarchical Kalman filter is used to estimate the advection field based on the assimilated precipitation fields at two times. The model is applied to tracking precipitation dynamics in a realistic setting, using UK Met Office radar data from both a summer convective event and a winter frontal event. The performance of the model is assessed both traditionally and using probabilistic measures of fit based on ROC curves. The model is shown to provide very good assimilation characteristics, and promising forecast skill. Improvements to the forecasting scheme are discussed

Bayesian data assimilation

This thesis addresses data assimilation, which typically refers to the estimation of the state of a physical system given a model and observations, and its application to short-term precipitation forecasting. A general introduction to data assimilation is given, both from a deterministic and' stochastic point of view. Data assimilation algorithms are reviewed, in the static case (when no dynamics are involved), then in the dynamic case. A double experiment on two non-linear models, the Lorenz 63 and the Lorenz 96 models, is run and the comparative performance of the methods is discussed in terms of quality of the assimilation, robustness "in the non-linear regime and computational time. Following the general review and analysis, data assimilation is discussed in the particular context of very short-term rainfall forecasting (nowcasting) using radar images. An extended Bayesian precipitation nowcasting model is introduced. The model is stochastic in nature and relies on the spatial decomposition of the rainfall field into rain "cells". Radar observations are assimilated using a Variational Bayesian method in which the true posterior distribution of the parameters is approximated by a more tractable distribution. The motion of the cells is captured by a 20 Gaussian process. The model is tested on two precipitation events, the first dominated by convective showers, the second by precipitation fronts. Several deterministic and probabilistic validation methods are applied and the model is shown to retain reasonable prediction skill at up to 3 hours lead time. Extensions to the model are discussed.

Single diode model parameters analysis of photovoltaic cell

In this paper it is proposed to obtain enhanced and more efficient parameters model from generalized five parameters (single diode) model of PV cells. The paper also introduces, describes and implements a seven parameter model for photovoltaic cell (PV cell) which includes two internal parameters and five external parameters. To obtain the model the mathematical equations and an equivalent circuit consisting of a photo generated current source, a series resistor, a shunt resistor and a diode is used. The fundamental equation of PV cell is used to analyse and best fit the observation data. Especially bisection iteration method is used to obtain the expected result and to understand the deviation of changes in different parameters situation at various conditions respectively. The produced model can be used of measuring and understanding the actions of photovoltaic cells for certain changes and parameters extraction. The effect is also studied with I-V and P-V characteristics of PV cells though it is a challenge to optimize the output with real time simulation. The working procedure is also discussed and an experiment presented to get the closure and insight about the produced model and to decide upon the model validity. At the end, we observed that the result of the simulation is very close to the produced model.

A variational method for identifying a spacewise-dependent heat source

The inverse problem of determining a spacewise-dependent heat source for the parabolic heat equation using the usual conditions of the direct problem and information from one supplementary temperature measurement at a given instant of time is studied. This spacewise-dependent temperature measurement ensures that this inverse problem has a unique solution, but the solution is unstable and hence the problem is ill-posed. We propose a variational conjugate gradient-type iterative algorithm for the stable reconstruction of the heat source based on a sequence of well-posed direct problems for the parabolic heat equation which are solved at each iteration step using the boundary element method. The instability is overcome by stopping the iterative procedure at the first iteration for which the discrepancy principle is satisfied. Numerical results are presented which have the input measured data perturbed by increasing amounts of random noise. The numerical results show that the proposed procedure yields stable and accurate numerical approximations after only a few iterations.

A variational method for the calculation of spin-rovibronic energy levels of any triatomic molecule in an electronic triplet state

The triatomic spin-rovibronic variational code RVIB3 has been extended to include the effect of two uncoupled electrons, for both (3)Sigma(-) and (3)Pi (Renner-Teller) electronic states. The spin-orbital-rotational kinetic energy is included in the usual way, via terms (J+L+S). The phenomenological terms AL.S and lambda 2/3(3S(z)(2)) are introduced to reproduce the 3 spin-orbit and spin-spin splittings, respectively. Calculations are performed to evaluate the spin-rovibronic energy levels of CCO (X) over tilde (3) Sigma(-) and CCO (A) over tilde (3) Pi for which the Born-Oppenheimer potentials are derived from high-accuracy ab initio calculations.

A method for merging flow-dependent forecast error statistics from an ensemble with static statistics for use in high resolution variational data assimilation

The background error covariance matrix, B, is often used in variational data assimilation for numerical weather prediction as a static and hence poor approximation to the fully dynamic forecast error covariance matrix, Pf. In this paper the concept of an Ensemble Reduced Rank Kalman Filter (EnRRKF) is outlined. In the EnRRKF the forecast error statistics in a subspace defined by an ensemble of states forecast by the dynamic model are found. These statistics are merged in a formal way with the static statistics, which apply in the remainder of the space. The combined statistics may then be used in a variational data assimilation setting. It is hoped that the nonlinear error growth of small-scale weather systems will be accurately captured by the EnRRKF, to produce accurate analyses and ultimately improved forecasts of extreme events.

A variational method to retrieve the extinction profile in liquid clouds using multiple field-of-view lidar

Liquid clouds play a profound role in the global radiation budget but it is difficult to remotely retrieve their vertical profile. Ordinary narrow field-of-view (FOV) lidars receive a strong return from such clouds but the information is limited to the first few optical depths. Wideangle multiple-FOV lidars can isolate radiation scattered multiple times before returning to the instrument, often penetrating much deeper into the cloud than the singly-scattered signal. These returns potentially contain information on the vertical profile of extinction coefficient, but are challenging to interpret due to the lack of a fast radiative transfer model for simulating them. This paper describes a variational algorithm that incorporates a fast forward model based on the time-dependent two-stream approximation, and its adjoint. Application of the algorithm to simulated data from a hypothetical airborne three-FOV lidar with a maximum footprint width of 600m suggests that this approach should be able to retrieve the extinction structure down to an optical depth of around 6, and total opticaldepth up to at least 35, depending on the maximum lidar FOV. The convergence behavior of Gauss-Newton and quasi-Newton optimization schemes are compared. We then present results from an application of the algorithm to observations of stratocumulus by the 8-FOV airborne “THOR” lidar. It is demonstrated how the averaging kernel can be used to diagnose the effective vertical resolution of the retrieved profile, and therefore the depth to which information on the vertical structure can be recovered. This work enables exploitation of returns from spaceborne lidar and radar subject to multiple scattering more rigorously than previously possible.

SUPERSYMMETRY, VARIATIONAL METHOD AND HULTHEN POTENTIAL

The formalism of supersymmetric quantum mechanics provides us with the eigenfunctions to be used in the variational method to obtain the eigenvalues for the Hulthen potential.

Supersymmetry, variational method and the Lennard-Jones (12,6) potential

The formalism of supersymmetric quantum mechanics is used to determine trial functions in order to obtain eigenvalues for the Lennard-Jones (12, 6) potential from variational method. The superpotential obtained provides an effective potential which can be directly comparable to the original one.