Original Full-order Model Research Articles

Efficient low-order auto regressive moving average (ARMA) models for speech signals

Using model reduction, an efficient low order (ARMA) modeling process for speech is presented. In this approach, the modeling process starts with a relatively high order (AR) model obtained by some classical methods. The AR model is then reduced using the SVD-based method. The model reduction yields a reduced order ARMA model which interestingly preserves the key properties of the original full order model such as stability. Line spectral frequencies LSF and signal-to-noise ratio (SNR) behavior are also investigated. To illustrate the performance and the effectiveness of the proposed approach, some simulations are conducted on some practical speech segments, such as phonemes and sentences.

Model reduction based on regional pole and covariance equivalent realizations

In this paper a novel model reduction problem is studied for linear continuous-time time-invariant stochastic systems. The purpose of this problem is to design the reduced-order model so that it has the same dominant pole region and steady state output covariance as those of the original full-order model. The resulting reduced-order model can approximate the corresponding original full-order model in two important aspects, i.e., transient and steady state performances. Necessary and sufficient conditions for the existence of desired reduced-order models are established, and an explicit expression for these reduced order model is also presented. An illustrative example is used to demonstrate the effectiveness of the proposed design method.

Vibration Analysis of Dry Friction Damped Turbine Blades Using Singular Perturbation Theory

The dynamic behavior of a turbine blade with an attached dry friction damper is investigated. Due to the presence of both high and low frequency resonances, the system is numerically stiff and time consuming to simulate. Three reduced-order models that remove the numerical stiffness problem are developed using singular perturbation theory. Two of these models are developed for the case of small damper mass, and the third model is developed for the case of high damper stiffness. It is shown that these models are consistent with two physically-motivated approximate models that have appeared in the literature. The accuracy of each approximate model is examined for a range of system and excitation parameters through comparison with the original full-order model. It is seen that inclusion of damper mass has a significant effect on the blade system response.

Performance of collocation models for simulating transient multistage separation units

A systematic analysis has been developed to investigate the performance of reduced-order models for multistage separation units in transient simulations. In particular, we have analyzed the accuracy of these models in reproducing some characteristic features of the dynamic behavior of the original full-order model, i.e. the eigenvalue of smallest modulus λ min and the singular values obtained through open-loop singular value decomposition. It is found that as the relative volatility increases the maximum reduction factor allowed by a prescribed accuracy decreases. The present analysis shows that, with the above limitations on the value of the reduction factor, reduced-order models are able to capture the true dynamics of the corresponding full-order models. Furthermore, the results indicate that the accuracy requirements in transient simulations are more stringent than in steady-state simulations, i.e. transient models require lower reduction factors for a given accuracy.

Performance of collocation models for simulating transient multistage separation units

A systematic analysis has been developed to investigate the performance of reduced-order models for multistage separation units in transient simulations. In particular, we have analyzed the accuracy of these models in reproducing some characteristic features of the dynamic behavior of the original full-order model, i.e. the eigenvalue of smallest modulus λ min and the singular values obtained through open-loop singular value decomposition. It is found that as the relative volatility increases the maximum reduction factor allowed by a prescribed accuracy decreases. The present analysis shows that, with the above limitations on the value of the reduction factor, reduced-order models are able to capture the true dynamics of the corresponding full-order models. Furthermore, the results indicate that the accuracy requirements in transient simulations are more stringent than in steady-state simulations, i.e. transient models require lower reduction factors for a given accuracy.