Identification of Viscously Damped Distributed Structural Dynamic Systems

1991 ◽  
Author(s):  
R. Chander ◽  
M. Meyyappa ◽  
S. Hanagud
Keyword(s):  
Dynamic Systems ◽  
Beam Theory ◽  
Continuous Functions ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Structural Dynamic ◽  
Domain Identification ◽  
Forcing Function ◽  
Cardinal Splines ◽  
Stiffness And Damping

Abstract A frequency domain identification technique applicable to damped distributed structural dynamic systems is presented. The technique is developed for beams whose behavior can be modeled using the Euler-Bernoulli beam theory. External damping of the system is included by means of a linear viscous damping model. Parameters to be identified, mass, stiffness and damping distributions are assumed to be continuous functions over the beam. The response at a discrete number of points along the length of the beam for a given forcing function is used as the data for identification. The identification scheme involves approximating the infinite dimensional response and parameter spaces by using quintic B-splines and cubic cardinal splines, respectively. A Galerkin type weighted residual procedure, in conjunction with the least squares technique, is employed to determine the unknown parameters. Numerically simulated response data for an applied impulse load are utilized to validate the developed technique. Estimated values for the mass, stiffness and damping distributions are discussed.

Modeling and Identification of Elastomeric Bearing Characteristics for Large-Scale Simulations

1998 ◽  
Vol 120 (1) ◽  
pp. 63-73 ◽  
Author(s):  
K. N. Morman ◽  
E. Nikolaidis ◽  
J. Rakowska ◽  
S. Seth
Keyword(s):  
Large Scale ◽  
Dynamic Stiffness ◽  
Testing Procedure ◽  
Nonlinear Dependence ◽  
Model Parameters ◽  
Elastomeric Bearing ◽  
Nonlinear Viscoelastic ◽  
Component Test ◽  
Differential Type ◽  
Stiffness And Damping

A constitutive equation of the differential type is introduced to model the nonlinear viscoelastic response behavior of elastomeric bearings in large-scale system simulations for vibration assessment and component loads prediction. The model accounts for the nonlinear dependence of dynamic stiffness and damping on vibration amplitude commonly observed in the behavior of bearings made of particle-reinforced elastomers. A testing procedure for the identification of the model parameters from bearing component test data is described. The experimental and analytical results for predicting the behavior of four (4) different car bushings are presented. In an example application, the model is incorporated in an ADAMS simulation to study the dynamic behavior of a car rear suspension.

FORCED RESPONSE OF STRUCTURAL DYNAMIC SYSTEMS WITH LOCAL TIME-DEPENDENT STIFFNESSES

2000 ◽  
Vol 237 (5) ◽  
pp. 761-773 ◽  
Author(s):  
R. DELTOMBE ◽  
D. MORAUX ◽  
G. PLESSIS ◽  
P. LEVEL
Keyword(s):  
Local Time ◽  
Dynamic Systems ◽  
Forced Response ◽  
Time Dependent ◽  
Structural Dynamic

scholarly journals Uncertainty Quantification in Epidemiological Models for COVID-19 Pandemic

2020 ◽  
Author(s):  
Leila Taghizadeh ◽  
Ahmad Karimi ◽  
Clemens Heitzinger
Keyword(s):  
Inverse Modeling ◽  
Posteriori Probability ◽  
Model Parameters ◽  
Epidemiological Models ◽  
Unknown Parameters ◽  
Protective Measures ◽  
A Posteriori Probability ◽  
Estimated Parameters ◽  
Forward And Inverse Modeling ◽  
Inversion Techniques

AbstractThe main goal of this paper is to develop the forward and inverse modeling of the Coronavirus (COVID-19) pandemic using novel computational methodologies in order to accurately estimate and predict the pandemic. This leads to governmental decisions support in implementing effective protective measures and prevention of new outbreaks. To this end, we use the logistic equation and the SIR system of ordinary differential equations to model the spread of the COVID-19 pandemic. For the inverse modeling, we propose Bayesian inversion techniques, which are robust and reliable approaches, in order to estimate the unknown parameters of the epidemiological models. We use an adaptive Markov-chain Monte-Carlo (MCMC) algorithm for the estimation of a posteriori probability distribution and confidence intervals for the unknown model parameters as well as for the reproduction number. Furthermore, we present a fatality analysis for COVID-19 in Austria, which is also of importance for governmental protective decision making. We perform our analyses on the publicly available data for Austria to estimate the main epidemiological model parameters and to study the effectiveness of the protective measures by the Austrian government. The estimated parameters and the analysis of fatalities provide useful information for decision makers and makes it possible to perform more realistic forecasts of future outbreaks.

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