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In the global framework of improving vibro-acoustic numerical prediction quality together with the need to decrease the number of prototyping stages, this manuscript focuses on achieving greater accuracy for acoustic numerical simulations by making use of a parametric updating technique, which enables tuning the model parameters inside physically meaningful boundaries. The improved model is used for the next prototyping stages, allowing more accurate results within reduced simulation times. The updating technique is based on recent works dealing with the constitutive relation error method (CRE) applied to acoustics. The updating process focuses on improving the acoustic damping matrix related to the absorbing properties of the materials covering the borders of the acoustic domain.

No description available.

Recent developments in instrumentation, communication and software have enabled the integration of real-time data into the decision-making process of hydrocarbon production. Applications of real-time data integration in drilling operations and horizontal-well lateral placement are becoming industry common practice. In reservoir management, the use of real-time data has been shown to be advantageous in tasks such as improving smart-well performance and in pressure-maintenance programs. Such capabilities allow for a paradigm change in which reservoir management can be looked at as a strategy that enables a semi-continuous process of model updates and decision optimizations instead of being periodic or reactive. This is referred to as closed-loop reservoir management (CLRM). Due to the complexity of the dynamic physical processes, large sizes, and huge uncertainties associated with reservoir description, continuous model updating is a large-scale problem with a highly dimensional parameter space and high computational costs. The need for an algorithm that is both feasible for practical applications and capable of generating reliable estimates of reservoir uncertainty is a key element in CLRM. This thesis investigates the validity of Markov Chain Monte Carlo (MCMC) sampling used in a Bayesian framework as an uncertainty quantification and model-updating tool suitable for real-time applications. A 3-phase, dual-porosity, dual-permeability reservoir model is used in a synthetic experiment. Continuous probability density functions of cumulative oil production for two cases with different model updating frequencies and reservoir maturity levels are generated and compared to a case with a known geology, i.e., truth case. Results show continuously narrowing ranges for cumulative oil production, with mean values approaching the truth case as model updating advances and the reservoir becomes more mature. To deal with MCMC sampling sensitivity to increasing numbers of observed measurements, as in the case of real-time applications, a new formulation of the likelihood function is proposed. Changing the likelihood function significantly improved chain convergence, chain mixing and forecast uncertainty quantification. Further, methods to validate the sampling quality and to judge the prior model for the MCMC process in real applications are advised.

The characterization of damping is important in making accurate predictions of both the true response and the frequency response of any device or structure dominated by energy dissipation. The process of modeling damping matrices and experimental verification of those is challenging because damping can not be determined via static tests as can mass and stiffness. Furthermore, damping is more difficult to determine from dynamic measurements than natural frequency. However, damping is extremely important in formulating predictive models of structures. In addition, damping matrix identification may be useful in diagnostics or health monitoring of structures. The objective of this work is to find a robust, practical procedure to identify damping matrices. All aspects of the damping identification procedure are investigated. The procedures for damping identification presented herein are based on prior knowledge of the finite element or analytical mass matrices and measured eigendata. Alternately, a procedure is based on knowledge of the mass and stiffness matrices and the eigendata. With this in mind, an exploration into model reduction and updating is needed to make the problem more complete for practical applications. Additionally, high performance computing is used as a tool to deal with large problems. High Performance Fortran is exploited for this purpose. Finally, several examples, including one experimental example are used to illustrate the use of these new damping matrix identification algorithms and to explore their robustness. / Ph. D.

Engineers and infrastructure owners have to manage an aging civil infrastructure in the US. Engineers have the opportunity to analyze structures using finite element models (FEM), and often base their engineering decisions on the outcome of the results. Ultimately, the success of these decisions is directly related to the accuracy of the finite element model in representing the real-life structure. Improper assumptions in the model such as member properties or connections, can lead to inaccurate results. A major source of modeling error in many finite element models of existing structures is due to improper representation of the boundary conditions. In this study, it is aimed to integrate experimental and analytical concepts by means of parameter estimation, whereby the boundary condition parameters of a structure in question are determined. FEM updating is a commonly used method to determine the "as-is" condition of an existing structure. Experimental testing of the structure using static and/or dynamic measurements can be utilized to update the unknown parameters. Optimization programs are used to update the unknown parameters by minimizing the error between the analytical and experimental measurements. Through parameter estimation, unknown parameters of the structure such as stiffness, mass or support conditions can be estimated, or more appropriately, "updated", so that the updated model provides for a better representation of the actual conditions of the system. In this study, a densely instrumented laboratory test beam was used to carry-out both analytical and experimental analysis of multiple boundary condition setups. The test beam was instrumented with an array of displacement transducers, tiltmeters and accelerometers. Linear vertical springs represented the unknown boundary stiffness parameters in the numerical model of the beam. Nine different load cases were performed and static measurements were used to update the spring stiffness, while dynamic measurements and additional load cases were used to verify these updated parameters. Two different optimization programs were used to update the unknown parameters and then the results were compared. One optimization tool was developed by the author, Spreadsheet Parameter Estimation (SPE), which utilized the Solver function found in the widely available Microsoft Excel software. The other one, comprehensive MATLAB-based PARameter Identification System (PARIS) software, was developed at Tufts University. Optimization results from the two programs are presented and discussed for different boundary condition setups in this thesis. For this purpose, finite element models were updated using the static data and then these models were checked against dynamic measurements for model validation. Model parameter updating provides excellent insight into the behavior of different boundary conditions and their effect on the overall structural behavior of the system. Updated FEM using estimated parameters from both optimization software programs generally shows promising results when compared to the experimental data sets. Although the use of SPE is simple and generally straight-forward, we will see the apparent limitations when dealing with complex, non-linear support conditions. Due to the inherent error associated with experimental measurements and FEM modeling assumptions, PARIS serves as a better suited tool to perform parameter estimation. Results from SPE can be used for quick analysis of structures, and can serve as initial inputs for the more in depth PARIS models. A number of different sensor types and spatial resolution were also investigated for the possible minimum instrumentation to have an acceptable model representation in terms of model and experimental data correlation.

Accurate models are necessary in critical applications. Key parameters in dynamic systems often change during their life cycle due to repair and replacement of parts or environmental changes. This dissertation presents a new approach to update system models, accounting for these changes. The approach uses frequency domain data and a neural network to produce estimates of the parameters being updated, yielding a model representative of the measured data. Current iterative methods developed to solve the model updating problem rely on minimization techniques to find the set of model parameters that yield the best match between experimental and analytical responses. Since the minimization procedure requires a fair amount of computation time, it makes the existing techniques infeasible for use as part of an adaptive control scheme correcting the model parameters as the system changes. They also require either mode shape expansion or model reduction before they can be applied, introducing errors in the procedure. Furthermore, none of the existing techniques has been applied to nonlinear systems. The neural network estimates the parameters being updated quickly and accurately without the need to measure all degrees of freedom of the system. This avoids the use of mode shape expansion or model reduction techniques, and allows for its implementation as part of an adaptive control scheme. The proposed technique is also capable of updating weakly nonlinear systems. Numerical simulations and experimental results show that the proposed method has good accuracy and generalization properties, and it is therefore, a suitable alternative for the solution of the model updating problem of this class of systems. / Ph. D.

This dissertation addresses the task of updating finite element models with frequency response data acquired in a structural dynamics test. Standard statistical techniques are used to generate statistically qualified data, which is then used in a Bayesian statistics regression formulation to update the finite element model. The Bayesian formulation allows the analyst to incorporate engineering judgment (in the form of prior knowledge) into the analysis and helps ensure that reasonable and realistic answers are obtained. The formulation includes true statistical weights derived from experimental data as well as a new formulation of the Bayesian regression problem that reduces the effects of numerical ill-conditioning. Model updates are performed with a simulated free-free beam, a simple steel frame, and a cantilever beam. Improved finite element models of the structures are obtained and several statistical tests are used to ensure that the models are improved. / Ph. D.

Initial forms of analytical models created to simulate real engineering structures may generally yield dynamic response predictions different than those obtained from experimental tests. Since testing a real structure under every possible excitation is not practical, it is essential to transform the initial mathematical model to a model which reflects the characteristics of the actual structure in a better way. By using structural model updating techniques, the initial mathematical model is adjusted so that it simulates the experimental measurements more closely. In this study, a sensitivity-based finite element (FE) model updating method using experimental frequency response (FRF) data is presented. This study bases on a technique developed in an earlier study on the computation of the so-called Mis-correlation Index (MCI) used for identifying the system matrices which require updating. MCI values are calculated for each required coordinate, and non-zero numerical values indicate coordinates carrying error. In this work a new model updating procedure based on the minimization of this index is developed. The method uses sensitivity approach. FE models are iteratively updated by minimizing MCI values using sensitivities. The validation of the method is realized through some case studies. In order to demonstrate the application of the method for real systems, a real test data obtained from the modal test of a scaled aircraft model (GARTEUR SM-AG19) is used. In the application, the FE model of the scaled aircraft is updated. In the case studies the generic software developed in this study is used along with some commercial programs.

Orientador: Luiz de Paula do Nascimento / Banca: Katia Lucchesi Cavalca Dedini / Banca: Gilberto Pechoto de Melo / Resumo: Neste trabalho são otimizadas rotinas computacionais de um método de estimação de rigidez de mancais de máquinas através de um processo de ajuste de modelo, utilizando a análise de sensibilidade. Este método consiste em utilizar a análise de sensibilidade dos autovalores com relação à variação da rigidez dos mancais de um rotor. A eficácia e a robustez do método são analisadas através de simulações teóricas, bem como através de dados experimentais obtidos de um rotor de rotação variável e rigidezes dos mancais ajustáveis. O modelo matemático de ajuste do sistema é desenvolvido pelo método dos elementos finitos e o método de ajuste converge empregando-se um processo iterativo. Este método de ajuste baseia-se na minimização da diferença entre autovalores experimentais e autovalores obtidos com o modelo matemático de ajuste a partir de valores de rigidez dos mancais previamente adotados. A análise é feita com o rotor em diversas velocidades de rotação para verificar a influência do efeito giroscópio, e em diversas condições de valores da rigidez dos mancais para analisar o método quando aplicado em rotores flexíveis e em rotores rígidos. O desempenho do método é analisado com resultados teóricos e experimentais. / Abstract: In this work, computational routines of estimation method of stiffness bearing of machine via a model updating process are optimized, using the sensitivity analysis. This method consists of using the eigenvalue sensitivity analysis, relating to the stiffness bearing variation of a rotor. The efficacy and the robustness of the method are analyzed through the theoretical simulations, as well as, based on the experimental data obtained of a test rotor with variable rotating speeds and adjustable bearing stiffness values. The mathematical model system is developed by the finite element method and the method of adjustment should converge employing an iterative process. The method of adjustment is based on the minimization of the difference between experimental eigenvalues and eigenvalues obtained via mathematical model from previously adopted stiffness bearing values. The analysis is made by using the rotor in different rotating speeds in order to check the influence of the gyroscopic effect, and in several conditions of the stiffness bearing values to analyze the method when applied on flexible and rigid rotors. The performance of the method is analyzed through theoretical and experimental results. / Mestre