gh. r. vossughi

The aim of this research was to provide a simple yet realistic model of the sino-nasal tissue as a major requirement for developing more efficient endoscopic neurosurgery simulation systems. Ex-vivo indention tests were performed on the orbital floor soft tissue of four sheep specimens. The resulting force-displacement data was incorporated into an inverse finite element model to obtain the hyperelastic mechanical properties of the tissue. Material characterization was performed for Polynomial, Yeoh, Mooney-Rivlin and Neo-Hookean hyperelastic models, using a Sequential Quadratic Programming algorithm. Experimental results indicated a relatively large elastic deformation, up to 6mm, during indentation test with a considerable nonlinearity in the force-displacement response. All hyperelastic models could satisfy the convergence criteria of the optimization procedure, with the highest convergence rate and a close fittings accuracy associated with the Yeoh hyperelastic model. The initial guess of the material constants was found to affect the number of iterations before converging, but not the optimization results. The normalized mean square errors of fitting between the model and experimental curves were obtained as 2.39%, 4.26% and 4.65% for three sheep samples, suggesting that the Yeoh model can adequately describe the typical hyperelastic mechanical behavior of the sino-nasal tissue for surgery simulation.

In this study, a real-time flexible modular modeling approach for the simulation of gas turbine engines dynamic behavior based on nonlinear thermodynamic and dynamic laws is addressed. The introduced model, which is developed in Matlab-Simulink environment, is an object-oriented high speed real-time computer model and is capable of simulating the dynamic behavior of a broad group of gas turbine engines due to its modular structure. Moreover, a Kalman filter-based model tuning procedure is applied to decrease the modeling errors. Modeling errors are defined as the mismatch between simulation results and available experimental data. This tuning procedure is an underdetermined estimation problem, where there are more tuning parameters than available measured data. Here, an innovative approach to produce a tuning parameter vector is introduced. This approach is based on seeking an optimal initial value for the Kalman filter tuning procedure. Three simulation studies are carried out in this paper to demonstrate the advantages, capabilities and performance of the proposed scheme. Furthermore, simulation results are compared with manufacturer’s published data, and with the experimental results gathered in either turbo-generator or turbo-compressor applications. Computational time requirement of the model is discussed at the end of the paper.