Massive shales and fractures are the main cause of seismic anisotropy in the upper-most part of the crust, caused either by sedimentary or tectonic processes. Neglecting the effect of seismic anisotropy in seismic processing algorithms may incorrectly image the seismic reflectors. This will also influence the quantitative amplitude analysis such as the acoustic or elastic impedance inversion and amplitude versus offsets analysis. Therefore it is important to obtain anisotropy parameters from seismic data. Conventional layer stripping inversion schemes and reflector based reflectivity inversion methods are solely dependent upon a specific reflector, without considering the effect of the other layers. This, on one hand, does not take the effect of transmission in reflectivity inversion into the account, and on the other hand, ignores the information from the waves travelling toward the lower layers. I provide a framework to integrate the information for each specific layer from all the rays which have travelled across this layer. To estimate anisotropy parameters I have implemented unconstrained minimization algorithms such as nonlinear conjugate gradients and variable metric methods, I also provide a nonlinear least square method, based on the Levenberg-Marquardt algorithm. In a stack of horizontal transversely isotropic layers with vertical axis of symmetry, where the layer properties are laterally invariant, we provide two different inversion schemes; traveltime and waveform inversion. / Both inversion schemes utilize compressional and joint compressional and converted shear waves. A new exact traveltime equation has been formulated for a dipping transversely isotropic system of layers. These traveltimes are also parametrized by the ray parameters for each ray element. I use the Newton method of minimization to estimate the ray parameter using a random prior model from a uniform distribution. Numerical results show that with the assumption of weak anisotropy, Thomsen’s anisotropy parameters can be estimated with a high accuracy. The inversion algorithms have been implemented as a software package in a C++ object oriented environment.

The purpose of this work was to develop methods to estimate the diffusion coefficient and elimination constant for dexamethasone in subcutaneous tissue. Solutions to the diffusion equation were found for different conditions relevant to implantation and injection. These solutions were then used as models for measured autoradiography data where the unknown model parameters were the diffusion coefficient and the elimination constant. The diffusion coefficient and elimination constant were then estimated by curve fitting the measured data to these models. Having these estimates would be of practical importance since inflammation surrounding implantable glucose sensors may be controlled through local release of dexamethasone at the site of implantation. Derivation of the appropriate model, how the model was used to estimate D and k, and various specific profile examples were investigated in detail. Osmotic pumps containing [3H]- dexamethasone were implanted into the subcutaneous tissue of rats. Digital autoradiography was used to measure the distribution of the [3H]-dexamethasone within the subcutaneous tissue at 6, 24, and 60 hours after implantation. Measured concentration profiles, near the catheter tip through which the agent was released, were compared to solutions of the diffusion equation in order to characterize drug diffusion coefficients and elimination constants. There was good agreement between the experimental data and the mathematical model used for estimation. The diffusion coefficient for dexamethasone in subcutaneous tissue was found to be D = 4.11+-1.77x10E-10 m2/s, and the elimination rate constant was found to be k = 3.65+-2.24x10E-5/s. Additionally, [3H]-dexamethasone was injected into the subcutaneous tissue of rats. Digital autoradiography was again used to measure the distribution of the [3H]- dexamethasone within the subcutaneous tissue at 2.5 and 20 minutes after injection. Measured concentration profiles were again compared to a mathematical model of drug diffusion for injection. There was good agreement between the experimental data and the mathematical model. The diffusion coefficient found using this simple injection method was 4.01+-2.01x10E-10 m2/s. The simple method given here for the determination of the diffusion coefficient is general enough to be applied to other substances and tissues as well.

This thesis focuses on detecting, classifying and locating faults on electric power transmission lines. Fault detection, fault classification and fault location have been achieved by using artificial neural networks. Feedforward networks have been employed along with backpropagation algorithm for each of the three phases in the Fault location process. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. Simulation results have been provided to demonstrate that artificial neural network based methods are efficient in locating faults on transmission lines and achieve satisfactory performances.

The main theme of this thesis are problems of locating of wireless units in localization systems represented by wireless sensor networks. The thesis describes principle of trilateration and Levenberg-Marquardt algorithm. The main theme of this thesis is simulation of behavior of localisation systems and possible optimization of localization.

Identificação e predição do comportamento aeroelástico representa um grande desafio para a análise e controle de fenômenos aeroelásticos adversos. A modelagem aeroelástica requer informações tanto sobre a dinâmica estrutural quanto sobre o comportamento aerodinâmico não estacionário. No entanto, a maioria das metodologias disponíveis atualmente são baseadas no desacoplamento entre o modelo estrutural e o modelo aerodinâmico não estacionário. Conseqüentemente, métodos alternativos são bem vindos na área de pesquisa aerolástica. Entre os métodos alternativos está o funcional multicamada, que fornece uma rigorosa representação matemática apropriada para modelagem aeroelástica e pode ser obtido através de redes neurais artificiais. Esse trabalho apresenta uma aplicação desse método, consistindo de um procedimento de identificação baseado em redes neurais artificiais que representam o funcional da resposta aeroelástica. O modelo neural foi treinado usando o algoritmo de Levenberg-Marquardt, o qual tem sido considerado um método de otimização muito eficiente. Ele combina a garantia de convergência do método do gradiente e o alto desempenho do método de Newton, sem a necessidade de calcular as derivadas de segunda ordem. Um modelo de asa ensaiado em túnel de vento foi usado para fornecer a resposta aeroelástica. A asa foi fixada a uma mesa giratória e um motor elétrico lhe fornecia o movimento de incidência. Essa representação aeroelástica funcional foi testada para diversas condições operacionais do túnel de vento. Os resultados mostraram que o uso de redes neurais na identificação da resposta aeroelástica é um método alternativo promissor, o qual permite uma rápida avaliação da resposta aerolástica do modelo. / Identification and prediction of aeroelastic behavior presents a significant challenge for the analysis and control of adverse aeroelastic phenomena. Aeroelastic modeling requires information from both structural dynamics and unsteady aerodynamic behavior. However, the majority of methodologies available today are based on the decoupling of structural model from the unsteady aerodynamic model. Therefore, alternative methods are mostly welcome in the aeroelastic research field. Among the alternative methods there is the multi-layer functional (MLF), that allows a rigorous mathematical framework appropriate for aeroelastic modeling and can be realized by means of artificial neural networks. This work presents an identification procedure based on artificial neural networks to represent the motion-induced aeroelastic response functional. The neural network model has been trained using the Levenberg-Marquardt algorithm that has been considered a very efficient optimization method. It combines the guaranteed convergence of steepest descent and the higher performance of the Newton\'s method, without the necessity of second derivatives calculation. A wind tunnel aeroelastic wing model has been used to provide motion-induced aeroelastic responses. The wing has been fixed to a turntable, and an electrical motor provides the incidence motion to the wing. This aeroelastic functional representation is then tested for a range of the wind tunnel model operational boundaries. The results showed that the use of neural networks in the aeroelastic response identification is a promising alternative method, which allows fast evaluation of aeroelastic response model.

Identificação e predição do comportamento aeroelástico representa um grande desafio para a análise e controle de fenômenos aeroelásticos adversos. A modelagem aeroelástica requer informações tanto sobre a dinâmica estrutural quanto sobre o comportamento aerodinâmico não estacionário. No entanto, a maioria das metodologias disponíveis atualmente são baseadas no desacoplamento entre o modelo estrutural e o modelo aerodinâmico não estacionário. Conseqüentemente, métodos alternativos são bem vindos na área de pesquisa aerolástica. Entre os métodos alternativos está o funcional multicamada, que fornece uma rigorosa representação matemática apropriada para modelagem aeroelástica e pode ser obtido através de redes neurais artificiais. Esse trabalho apresenta uma aplicação desse método, consistindo de um procedimento de identificação baseado em redes neurais artificiais que representam o funcional da resposta aeroelástica. O modelo neural foi treinado usando o algoritmo de Levenberg-Marquardt, o qual tem sido considerado um método de otimização muito eficiente. Ele combina a garantia de convergência do método do gradiente e o alto desempenho do método de Newton, sem a necessidade de calcular as derivadas de segunda ordem. Um modelo de asa ensaiado em túnel de vento foi usado para fornecer a resposta aeroelástica. A asa foi fixada a uma mesa giratória e um motor elétrico lhe fornecia o movimento de incidência. Essa representação aeroelástica funcional foi testada para diversas condições operacionais do túnel de vento. Os resultados mostraram que o uso de redes neurais na identificação da resposta aeroelástica é um método alternativo promissor, o qual permite uma rápida avaliação da resposta aerolástica do modelo. / Identification and prediction of aeroelastic behavior presents a significant challenge for the analysis and control of adverse aeroelastic phenomena. Aeroelastic modeling requires information from both structural dynamics and unsteady aerodynamic behavior. However, the majority of methodologies available today are based on the decoupling of structural model from the unsteady aerodynamic model. Therefore, alternative methods are mostly welcome in the aeroelastic research field. Among the alternative methods there is the multi-layer functional (MLF), that allows a rigorous mathematical framework appropriate for aeroelastic modeling and can be realized by means of artificial neural networks. This work presents an identification procedure based on artificial neural networks to represent the motion-induced aeroelastic response functional. The neural network model has been trained using the Levenberg-Marquardt algorithm that has been considered a very efficient optimization method. It combines the guaranteed convergence of steepest descent and the higher performance of the Newton\'s method, without the necessity of second derivatives calculation. A wind tunnel aeroelastic wing model has been used to provide motion-induced aeroelastic responses. The wing has been fixed to a turntable, and an electrical motor provides the incidence motion to the wing. This aeroelastic functional representation is then tested for a range of the wind tunnel model operational boundaries. The results showed that the use of neural networks in the aeroelastic response identification is a promising alternative method, which allows fast evaluation of aeroelastic response model.

This thesis examines how to find the best fit to a series of data points when curve fitting using power-exponential models. We describe the different numerical methods such as the Gauss-Newton and Levenberg-Marquardt methods to compare them for solving non-linear least squares of curve fitting using different power-exponential functions. In addition, we show the results of numerical experiments that illustrate the effectiveness of this approach.Furthermore, we show its application to the practical problems by using different sets of data such as death rates and rocket-triggered lightning return strokes based on the transmission line model.

<p>The study of Surface Air Cooled Oil Coolers (SACOC) is motivated by the need for new cooling concepts for compact machinery designs with high thermal load. Installing finned heat exchangers in the bypass duct of a turbofan engine provides an additional cooling source having transonic flow as a heat sink. The characterization of the heat transfer and the aerodynamics of the design are essential to minimize the impact on the overall efficiency of the engine. In the present study, the SACOC is studied numerically and experimentally. Two geometries are tested in a high-speed linear wind tunnel where measurements are taken with multiple sensors and optical techniques. For the heat transfer characterization, an Inverse Heat Conduction Methodology (IHCM) based on a Levenberg-Marquardt Algorithm is developed. The experimental results are matched to numerical simulations using a Reynolds Averaged Navier-Stokes (RANS) solver. Finally, a multi-objective optimization algorithm is coupled <a>with the RANS solver</a> to explore new geometries that maximize the heat transfer and minimize the pressure drop across the studied domain. The 400 profiles generated allow for the identification of the features that have a higher influence on the performance of the fins and six profiles that present large improvements are chosen for further analysis.</p>

The work done in this thesis is to examine various methods for curve fitting. Linear least squares and non-linear least squares will be described and compared, and the Newton method, Gauss--Newton method and Levenberg--Marquardt method will be applied to example problems. / Syftet med denna uppsats är att beskriva och använda olika metoder för kurvanpassning, det vill säga att passa matematiska funktioner till data. De metoder som undersöks är Newtons metod, Gauss--Newton metoden och Levenberg--Marquardt metoden. Även skillnaden mellan linjär minsta kvadrat anpassning och olinjär minsta kvadrat anpassning. Till sist tillämpas Newton, Gauss Newton och Levenberg--Marquardt metoderna på olika exempel.

Performance engineers face the major challenge in modeling and simulation for the after-market power system due to system degradation and measurement errors. Currently, the majority in power generation industries utilizes the deterministic data matching method to calibrate the model and cascade system degradation, which causes significant calibration uncertainty and also the risk of providing performance guarantees. In this research work, a maximum-likelihood based simultaneous data reconciliation and model calibration (SDRMC) is used for power system modeling and simulation. By replacing the current deterministic data matching with SDRMC one can reduce the calibration uncertainty and mitigate the error propagation to the performance simulation. A modeling and simulation environment for a complex power system with certain degradation has been developed. In this environment multiple data sets are imported when carrying out simultaneous data reconciliation and model calibration. Calibration uncertainties are estimated through error analyses and populated to performance simulation by using principle of error propagation. System degradation is then quantified by performance comparison between the calibrated model and its expected new & clean status. To mitigate smearing effects caused by gross errors, gross error detection (GED) is carried out in two stages. The first stage is a screening stage, in which serious gross errors are eliminated in advance. The GED techniques used in the screening stage are based on multivariate data analysis (MDA), including multivariate data visualization and principle component analysis (PCA). Subtle gross errors are treated at the second stage, in which the serial bias compensation or robust M-estimator is engaged. To achieve a better efficiency in the combined scheme of the least squares based data reconciliation and the GED technique based on hypotheses testing, the Levenberg-Marquardt (LM) algorithm is utilized as the optimizer. To reduce the computation time and stabilize the problem solving for a complex power system such as a combined cycle power plant, meta-modeling using the response surface equation (RSE) and system/process decomposition are incorporated with the simultaneous scheme of SDRMC. The goal of this research work is to reduce the calibration uncertainties and, thus, the risks of providing performance guarantees arisen from uncertainties in performance simulation.