Methodology for Performing Submodel Analysis for Random Vibration Problems using Modal Analysis Results Collins Aerospace

Format:

Book

Conference/Event

Author/Creator:

Vinjanampati, Pavan Kumar, author.

Contributor:

Veerarapu, Subrahmanyam

Conference Name:

AeroCON 2022 (2022-06-02 : Bangalore, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2022

Summary:

The FE analysis of complex systems with lot of intricate features and fillets modeled in detail would result in a huge FE model size making it difficult to handle. Therefore, to reduce the computation time, defeaturing of such regions are carried out as a common practice and these critical stress concentration regions can be studied using submodeling approach later based on response superimposed from the global models. This method is widely practiced and is quite easy to implement for static and harmonic analysis problems. However, there is no well documented methodology exists for submodeling in the random vibration environment. In case of Random vibration analysis, the cut boundary displacements from Power Spectral Density (PSD) analysis would result in unrealistic stresses.This paper describes the methodology to perform submodel analysis for random vibration analysis problems for defeatured regions, local design changes, refined mesh and complex problems involving contact non-linearity. The proposed method considers the cut-boundary displacements based on covariance values obtained from random vibration analysis (PSD). Covariance values are utilized to get the mode coefficient for each mode and scale the modal analysis results to be considered for submodel analysis for each mode using static analysis either linear or nonlinear as desired. Square root of Sum of Squares (SRSS) method is considered for predicting Root Mean Square (RMS) values in the submodel regions. Validation of the methodology using a Multi Degree of Freedom (MDOF) system in ANSYS Workbench is also discussed in this paper

Notes:

Vendor supplied data

Publisher Number:

2022-26-0012

Access Restriction:

Restricted for use by site license