The bulk modulus of pure hydraulic oil and its dependency on pressure and temperature has been studied extensively over the past years. A comprehensive review of some of the more common definitions of fluid bulk modulus is conducted and comments on some of the confusion over definitions and different methods of measuring the fluid bulk modulus are presented in this thesis. In practice, it is known that there is always some form of air present in hydraulic systems which substantially decreases the oil bulk modulus. The term effective bulk modulus is used to account for the effect of air and/or the compliance of transmission lines. A summary from the literature of the effective bulk modulus models for a mixture of hydraulic oil and air is presented. Based on the reviews, these models are divided into two groups: “compression only” models and “compression and dissolve” models. A comparison of various “compression only” models, where only the volumetric compression of air is considered, shows that the models do not match each other at the same operating conditions. The reason for this difference is explained and after applying some modifications to the models, a theoretical model of the “compression only” model is suggested. The “compression and dissolve” models, obtained from the literature review, include the effects of the volumetric compression of air and the volumetric reduction of air due to the dissolving of air into the oil. It is found that the existing “compression and dissolve” models have a discontinuity at some critical pressure and as a result do not match the experimental results very well. The reason for the discontinuity is discussed and a new “compression and dissolve” model is proposed by introducing some new parameters to the theoretical model. A new critical pressure (PC) definition is presented based on the saturation limit of oil. In the new definition, the air stops dissolving into the oil after this critical pressure is reached and any remaining air will be only compressed afterwards. An experimental procedure is successfully designed and fabricated to verify the new proposed models and to reproduce the operating conditions that underlie the model assumptions. The pressure range is 0 to 6.9 MPa and the temperature is kept constant at °C. Air is added to the oil in different forms and the amount of air varies from about 1 to 5%. Experiments are conducted in three different phases: baseline (without adding air to the oil), lumped air (air added as a pocket of air to the top of the oil column) and distributed air (air is distributed in the oil in the form of small air bubbles). The effect of different forms and amounts of air and various volume change rates are investigated experimentally and it is shown that the value of PC is strongly affected by the volume change rate, the form, and the amount of air. It is also shown that the new model can represent the experimental data with great accuracy. The new proposed “compression and dissolve” model can be considered as a general model of the effective bulk modulus of a mixture of oil and air where it is applicable to any form of a mixture of hydraulic oil and air. However, it is required to identify model parameters using experimental measurements. A method of identifying the model parameters is introduced and the modeling errors are evaluated. An attempt is also made to verify independently the value of some of the parameters. The new proposed model can be used in analyzing pressure variations and improving the accuracy of the simulations in low pressure hydraulic systems. The new method of modeling the air dissolving into the oil can be also used to improve the modeling of cavitation phenomena in hydraulic systems.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Chih-Hang Wu / The fast advancement in computer technologies in the recent years has made the use of simulation to estimate stock/equity performances and pricing possible; however, determining the optimal exercise time and prices of American options using Monte-Carlo simulation is still a computationally challenging task due to the involved computer memory and computational complexity requirements. At each time step, the investor must decide whether to exercise the option to get the immediate payoff, or hold on to the option until a later time. Traditionally, the stock options are simulated using Monte-Carlo methods and all stock prices along the path are stored, and then the optimal exercise time is determined starting at the final time period and continuing backward in time. Also, as the number of paths simulated increases, the number of simultaneous equations that need to be solved at each time step grow proportionally. Currently, two theoretical methods have emerged in determining the optimal exercise problem. The first method uses the concept of least-squares approach in linear regression to estimate the value of continuing to hold on to the option via a set of randomly generated future stock prices. Then, the value of continuing can be compared to the payoff at current time from exercising the option and a decision can be reached, which gives the investor a higher value. The second method uses the finite difference approach to establish an exercise boundary for the American option via an artificially generated mesh on both possible stock prices and decision times. Then, the stock price is simulated and the method checks to see if it is inside the exercise boundary. In this research, these two solution approaches are evaluated and compared using discrete event simulation. This allows complex methods to be simulated with minimal coding efforts. Finally, the results from each method are compared. Although a more conservative method cannot be determined, the least-squares method is faster, more concise, easier to implement, and requires less memory than the mesh method. The motivation for this research stems from interest in simulating and evaluating complicated solution methods to the optimal exercise problem, yet requiring little programming effort to produce accurate and efficient estimation results.

The thesis deals with methods of identification of dynamic systems in time and frequency domain. Nonparametric and parametric methods of identification are analyzed. Selected methods are implemented in Matlab & Simulink sotware. Finally, the user guide for the created Python application is introduced.

The objective of the thesis is to create software, which implements industrial PID controller enabling smooth switch-over and automatic tuning of parameters. The next objective is to create visualization for this controller. Whole controller have to be implemented into PLC Power Panel (B&R company). At the beginning of the thesis there is theoretical description of all implemented algorithms (discrete control algorithms derived from PID, smooth switch-over, antiwindup, identification algorithms). In the following there is designed program structure, which is state machine-shaped. All algorithms are implemented in ANSI C at first as s-function for Matlab/Simulink program, which enables to simulate and verify the controller. Implementation of controller into PLC Power Panel through the use of Automation Studio program from B&R company is described in the next part. Visualization is created in the same program. Simulations and verifications on mathematical and physical model demonstrates functionality of implemented algorithms, but also points out some problems associated with the use of identification algorithms in real world (noise, quantization in A/D and D/A converter).

Master’s thesis is focused on the adaptive controllers. The first theoretic part mainly describes the parametric identification, which belongs to the most important part of the adaptive controller’s structure. Classical identification methods (the recursive least squares methods) are firstly mentioned and afterwards the identification methods based on the neural network (the Marquardt-Levenberg algorithm and the new identification algorithm NIA inspired by the neural networks) are described. At the conclusion of the theoretic part there are mentioned the algorithm of the adaptive controller’s tuning which uses the identification parameters (the modified Z-N method) and the tested types of adaptive controllers. Particular results, which were found out by verifying of the adaptive controllers on the simulation and real models, are contained in second, the practical, part of the thesis. Finally, achieved results are compared with the classical discrete PID controller and with the adaptive controller of the B&R company.

No description available.

Sem resumo / Sem abstract

Esta dissertação investiga numericamente a influência dos efeitos viscosos e elásticos em escoamentos de materiais viscoplásticos no interior de uma cavidade dirigida. O modelo mecânico empregado é constituído pelas equações de conservação de massa e pelo princípio da quantidade de movimento linear, para fluidos incompressíveis, acoplado à equação constitutiva. Esta equação modifica o modelo viscoelástico de Oldroyd-B de modo a acomodar que os tempos de relaxação e retardo do material, bem como sua viscosidade viscoplástica, dependam das mudanças de sua microestrutura. A aproximação numérica do modelo emprega o método multi-campos de Galerkin mínimos-quadrados em termos do tensor de tensão extra, do vetor velocidade e do campo de pressão. Os resultados objetivam a determinção do tamanho e localização das regiões aparentemente não-escoadas do material, bem como sua deformação elástica, intensidade de tensão, e a sua vorticidade no interior da cavidade. Os resultados claramente indicam que o padrão do escoamento é fortemente influenciado pela variação dos efeitos elásticos (variação do tempo de relaxação adimensional, θ0 * ), viscosos (variação do índice de power-law, n) e cinemáticos (variação da velocidade adimensional, U* , do escoamento) no interior da cavidade. / This dissertation investigated numerically the influence of viscous and elastic effects on flows of viscoplastic materials within a lid-driven cavity. The mechanical model used is made up of mass and momentum balance equations, coupled with the constitutive equation. This equation modifies the viscoelastic Oldroyd-B model to accommodate both relaxation and retardation times, and viscosity function, dependent on the microstructure changes. Numerical approximations of the model make use a three-field Galerkin least squares method in terms of the extra stress tensor, velocity vector and pressure field. Computations focus on the determination of the size and position of apparently unyielded regions as well as the elastic deformation, stress intensity, and the vorticity within of the cavity. Results clearly indicate that the flow pattern is strongly influenced by the elastic (variation of the dimensionless relaxation time, θ0 * ), viscous (variation of the power-law index, n) and kinematic (variation of the dimensionless flow velocity, U* ) effects within the cavity.

This thesis studies the Second-order Least Squares (SLS) estimation method in regression models with and without measurement error. Applications of the methodology in general quasi-likelihood and variance function models, censored models, and linear and generalized linear models are examined and strong consistency and asymptotic normality are established. To overcome the numerical difficulties of minimizing an objective function that involves multiple integrals, a simulation-based SLS estimator is used and its asymptotic properties are studied. Finite sample performances of the estimators in all of the studied models are investigated through simulation studies. / February 2009

Maximum likelihood is an ubiquitous method used in the estimation of generalized linear mixed model (GLMM). However, the method entails computational difficulties and relies on the normality assumption for random effects. We propose a second-order least squares (SLS) estimator based on the first two marginal moments of the response variables. The proposed estimator is computationally feasible and requires less distributional assumptions than the maximum likelihood estimator. To overcome the numerical difficulties of minimizing an objective function that involves multiple integrals, a simulation-based SLS estimator is proposed. We show that the SLS estimators are consistent and asymptotically normally distributed under fairly general conditions in the framework of GLMM. Missing data is almost inevitable in longitudinal studies. Problems arise if the missing data mechanism is related to the response process. This thesis develops the proposed estimators to deal with response data missing at random by either adapting the inverse probability weight method or applying the multiple imputation approach. In practice, some of the covariates are not directly observed but are measured with error. It is well-known that simply substituting a proxy variable for the unobserved covariate in the model will generally lead to biased and inconsistent estimates. We propose the instrumental variable method for the consistent estimation of GLMM with covariate measurement error. The proposed approach does not need any parametric assumption on the distribution of the unknown covariates. This makes the method less restrictive than other methods that rely on either a parametric distribution of the covariates, or to estimate the distribution using some extra information. In the presence of data outliers, it is a concern that the SLS estimators may be vulnerable due to the second-order moments. We investigated the robustness property of the SLS estimators using their influence functions. We showed that the proposed estimators have a bounded influence function and a redescending property so they are robust to outliers. The finite sample performance and property of the SLS estimators are studied and compared with other popular estimators in the literature through simulation studies and real world data examples.