Combination of Partial Stochastic Linearization and Karhunen-Loeve Expansion to Design Coriolis Dynamic Vibration Absorber

Representation of Random Shock Via the Karhunen Loeve Expansion

Shock excitations are normally random process realizations, and most of our efforts to represent them either directly or indirectly reflect this fact. The most common indirect representation of shock sources is the shock response spectrum. It seeks to establish the damage-causing potential of random shocks in terms of responses excited in linear, single-degree-of-freedom systems. This paper sho...

Model Reduction via the Karhunen-Loeve Expansion: An Exposition

Model Reduction, Centering, and the Karhunen-Loeve Expansion

We propose a new computationally efficient modeling method that captures existing translation symmetry in a system. To obtain a low order approximate system of ODEs prior to performing Karhunen Loeve expansion we process the available data set using a “centering” procedure. This approach has been shown to be efficient in nonlinear scalar wave equations.

Convergence study of the truncated Karhunen–Loeve expansion for simulation of stochastic processes

A random process can be represented as a series expansion involving a complete set of deterministic functions with corresponding random coe<cients. Karhunen–Loeve (K–L) series expansion is based on the eigen-decomposition of the covariance function. Its applicability as a simulation tool for both stationary and non-stationary Gaussian random processes is examined numerically in this paper. The ...

Simulation of strongly non-Gaussian processes using Karhunen–Loeve expansion

The non-Gaussian Karhunen–Loeve (K–L) expansion is very attractive because it can be extended readily to non-stationary and multidimensional fields in a unified way. However, for strongly non-Gaussian processes, the original procedure is unable to match the distribution tails well. This paper proposes an effective solution to this tail mismatch problem using a modified orthogonalization techniq...