Sfoglia per Soggetto "Uncertainty analysis"
Items 1-20 di 88
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Accounting for modeling errors and inherent structural variability through a hierarchical bayesian model updating approach: An overview
(2020)Mechanics-based dynamic models are commonly used in the design and performance assessment of structural systems, and their accuracy can be improved by integrating models with measured data. This paper provides an overview ... -
Adaptive Bayesian Inference Framework for Joint Model and Noise Identification
(2022)Model updating, the process of inferring a model from data, is prone to the adverse effects of modeling error, which is caused by simplification and idealization assumptions in the mathematical models. In this study, an ... -
Aerodynamic shape optimization for minimum robust drag and lift reliability constraint
(2016)A methodology for shape optimization of aerodynamic bodies under uncertainties is presented. Flow-related and geometrical uncertainties are considered and quantified by probability distribution functions. The optimal shape ... -
An analytical perspective on Bayesian uncertainty quantification and propagation in mode shape assembly
(2020)Assembling local mode shapes identified from multiple setups to form global mode shapes is of practical importance when the degrees of freedom (dofs) of interest are measured separately in individual setups or when one ... -
Approximate Bayesian computation for granular and molecular dynamics simulations
(2016)The effective integration of models with data through Bayesian uncertainty quantification hinges on the formulation of a suitable likelihood function. In many cases such a likelihood may not be readily available or it may ... -
Bayesian annealed sequential importance sampling: An unbiased version of transitional Markov chain Monte Carlo
(2018)The transitional Markov chain Monte Carlo (TMCMC) is one of the efficient algorithms for performing Markov chain Monte Carlo (MCMC) in the context of Bayesian uncertainty quantification in parallel computing architectures. ... -
Bayesian damage characterization based on probabilistic model of scattering coefficients and hybrid wave finite element model scheme
(2019)Ultrasonic Guided Wave(GW) has been proven to be sensitive to small damage. Motivated by the fact that the quantitative relationship between wave scattering and damage intensity can be described by scattering properties, ... -
A Bayesian Expectation-Maximization (BEM) methodology for joint input-state estimation and virtual sensing of structures
(2022)The joint input-state estimation and virtual sensing of structures are reformulated on a Bayesian probabilistic foundation, focusing on data-driven uncertainty quantification and propagation. The variation of input forces ... -
A Bayesian framework for calibration of multiaxial fatigue curves
(2022)A Bayesian framework is proposed to re-formulate a multiaxial fatigue model and produce probabilistic stress-life fatigue curves from experimental data. The proposed framework identifies the experimentally-driven parameters ... -
A bayesian framework for optimal experimental design in structural dynamics
(2016)A Bayesian framework for optimal experimental design in structural dynamics is presented. The optimal design is based on an expected utility function that measures the value of the information arising from alternative ... -
Bayesian Hierarchical Models for Uncertainty Quantification in Structural Dynamics
(2014)The Bayesian framework for hierarchical modeling is applied to quantify uncertainties, arising mainly due to manufacturing variability, for a group of identical structural components. Parameterized Gaussian models of ... -
Bayesian inference for damage identification based on analytical probabilistic model of scattering coefficient estimators and ultrafast wave scattering simulation scheme
(2020)Ultrasonic Guided Waves (GW) actuated by piezoelectric transducers installed on structures have proven to be sensitive to small structural defects, with acquired scattering signatures being dependent on the damage type. ... -
A Bayesian methodology for crack identification in structures using strain measurements
(2010)A Bayesian system identification methodology is presented for estimating the crack location, size and orientation in a structure using strain measurements. The Bayesian statistical approach combines information from measured ... -
Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures
(2018)This paper presents a new framework for output-only nonlinear system and damage identification of civil structures. This framework is based on nonlinear finite element (FE) model updating in the time-domain, using only the ... -
Bayesian uncertainty quantification and propagation in nonlinear structural dynamics
(2013)A Bayesian uncertainty quantification and propagation (UQ&P) framework is presented for identifying nonlinear models of dynamic systems using vibration measurements of their components. The measurements are taken to be ... -
Bayesian uncertainty quantification and propagation in nonlinear structural dynamics
(2009)Nonlinear modelling and parametric identification of an experimental vehicle model, are employed in this paper. The composite structure of the vehicle model is split into a frame substructure and to four support substructures. ... -
Bayesian uncertainty quantification and propagation using adjoint techniques
(2014)This paper presents the Bayesian inference framework enhanced by analytical approximations for uncertainty quantification and propagation and parameter estimation. A Gaussian distribution is used to approximate the posterior ... -
Bayesian uncertainty quantification for machine-learned models in physics
(2022)Being able to quantify uncertainty when comparing a theoretical or computational model to observations is critical to conducting a sound scientific investigation. With the rise of data-driven modelling, understanding various ... -
Bayesian uncertainty quantification of turbulence models based on high-order adjoint
(2015)The uncertainties in the parameters of turbulence models employed in computational fluid dynamics simulations are quantified using the Bayesian inference framework and analytical approximations. The posterior distribution ... -
Comparison of safety indexes for chemical processes under uncertainty
(2021)The fatal consequences of industrial incidents have made evident the need for suitable tools to develop inherently safer process design options. Traditionally, in a process design project, the evaluation of safety aspects ...