Reduced-order Computational Model Research Articles

Advanced Computational Dissipative Structural Acoustics and Fluid-Structure Interaction in Low-and Medium-Frequency Domains. Reduced-Order Models and Uncertainty Quantification

This paper presents an advanced computational method for the prediction of the responses in the frequency domain of general linear dissipative structural-acoustic and fluid-structure systems, in the low-and medium-frequency domains and this includes uncertainty quantification. The system under consideration is constituted of a deformable dissipative structure that is coupled with an internal dissipative acoustic fluid. This includes wall acoustic impedances and it is surrounded by an infinite acoustic fluid. The system is submitted to given internal and external acoustic sources and to the prescribed mechanical forces. An efficient reduced-order computational model is constructed by using a finite element discretization for the structure and an internal acoustic fluid. The external acoustic fluid is treated by using an appropriate boundary element method in the frequency domain. All the required modeling aspects for the analysis of the medium-frequency domain have been introduced namely, a viscoelastic behavior for the structure, an appropriate dissipative model for the internal acoustic fluid that includes wall acoustic impedance and a model of uncertainty in particular for the modeling errors. This advanced computational formulation, corresponding to new extensions and complements with respect to the state-of-the-art are well adapted for the development of a new generation of software, in particular for parallel computers.

Computational stochastic statics of an uncertain curved structure with geometrical nonlinearity in three-dimensional elasticity

A methodology for analyzing the large static deformations of geometrically nonlinear structural systems in the presence of both system parameters uncertainties and model uncertainties is presented. It is carried out in the context of the identification of stochastic nonlinear reduced-order computational models using simulated experiments. This methodology requires the knowledge of a reference calculation issued from the mean nonlinear computational model in order to determine the POD basis (Proper Orthogonal Decomposition) used for the mean nonlinear reduced-order computational model. The construction of such mean reduced-order nonlinear computational model is explicitly carried out in the context of three-dimensional solid finite elements. It allows the stochastic nonlinear reduced-order computational model to be constructed in any general case with the nonparametric probabilistic approach. A numerical example is then presented for a curved beam in which the various steps are presented in details.

Efficient calculation of elementary parameters of transformers

Very efficient procedures for computing elementary parameters (turn leakage inductances and capacitances) in a transformer are presented. The turns are used as a calculation base to permit modeling at very high frequencies. Turn-to-turn (or loop) leakage inductances are obtained by an image method. The charge simulation method is used for finding the capacitances between turns and from turns to ground. The new methods are very efficient compared with the use of the technique of finite elements and are also remarkably accurate. Thus, the short circuit (or test) leakage inductance can be obtained from turn-to-turn information. Examples of calculated parameters are given for illustration. For validation, the results are compared with the parameters obtained using finite elements and tests. The elementary parameters can be used to create reduced-order computational models for the calculation of transient phenomena. >