In this paper an augmented sequential Bayesian filtering approach is proposed for parameter and modeling error estimation of linear dynamic systems of civil structures using time domain input-output data through a seq...
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ISBN:
(纸本)9783030120757;9783030120740
In this paper an augmented sequential Bayesian filtering approach is proposed for parameter and modeling error estimation of linear dynamic systems of civil structures using time domain input-output data through a sequential maximum a posteriori (MAP) estimation approach, which is similar to Kalman filtering method. However, in the application of existing Kalman filters, the estimation of modelingerrors is rarely considered. Unlike traditional Kalman filter which provides state estimation at every time step, the proposed filtering approach estimates the parameter and modelingerror on a windowing basis, i.e., the input and output data are divided into windows for estimation which would save computation burden. The analytical derivation of the proposed augmented sequential Bayesian filtering method is first presented, and then the method is verified through a numerical case study of a 3-story building model. An earthquake excitation is used as the input and the acceleration time history response of the building model is simulated. The simulated response is then polluted with different levels of Gaussian white noise to account for the measurement noise. The simulated response is used as the measured data for calibrating another 3-story shear building model which is different from the original model for simulation. modelingerrors are introduced in this shear building model including the shear building assumption, grouping strategy and boundary conditions. The augmented sequential Bayesian filtering approach is applied to estimate the model parameters and modelingerror. The performance of the proposed method is studied with respect to modelingerrors, the number of sensors and the level of noise.
AMT(automated manual transmission) launch control performance cannot be guaranteed when the clutch friction coefficient, vehicle mass and road slope angle vary to some large extent. In this paper, a linear quadratic o...
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ISBN:
(纸本)9781509044238
AMT(automated manual transmission) launch control performance cannot be guaranteed when the clutch friction coefficient, vehicle mass and road slope angle vary to some large extent. In this paper, a linear quadratic output regulator with disturbance/modelingerror observer is proposed to provide consistent optimal performance. The control law is derived as a linear feedback form of the system states and the disturbance/estimated modelingerror, and the system output is guaranteed to be asymptotically stable, which will significantly relax the dependence of the controller on modeling accuracy, and extend the application of LQR theory. Finally, the proposed control law is tested through simulation and experiment tests of a mid-size passenger car.
In this paper a new method is introduced for the estimation of modelingerror resulting from homogenization of elastic heterogeneous bodies. The approach is similar to the well known explicit residual approximation er...
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In this paper a new method is introduced for the estimation of modelingerror resulting from homogenization of elastic heterogeneous bodies. The approach is similar to the well known explicit residual approximation errorestimation. It is proved that besides the residuum of the equilibrium equation and interelement traction jump also a difference of stress divergences as well as traction jump along the material interfaces contribute to the modelingerror estimate. Moreover, explicit specification and numerical evaluation of "stability" constants provide reasonable effectivity index of this error indicator. Selected numerical examples illustrate the promise of this approach. Therefore, the proposed methodology is a computationally inexpensive option for the other methods of modelingerror assessment. (C) 2013 Elsevier Ltd. All rights reserved.
We consider in this paper the initial-boundary value problem for the 1D neutron transport equation with isotropic scattering, set in some bounded interval with inflow boundary conditions. The usual parabolic scaling y...
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We consider in this paper the initial-boundary value problem for the 1D neutron transport equation with isotropic scattering, set in some bounded interval with inflow boundary conditions. The usual parabolic scaling yields the diffusive limit. A surrogate model, coupling transport and diffusion equations, is then introduced in order to accurately assess the value of specific quantities of interest. The control of the quality of the computation (with respect to such a quantity of interest) is performed by means of an approach for modeling error estimation combined with an adaptive strategy to control the surrogate model, if necessary. (c) 2013 Elsevier B.V. All rights reserved.
We consider the multi-scale analysis of the mechanics of multiphase composites with complex microstructure, based on the Goal-Oriented Adaptive modeling method [1-10]. The underlying approach of this method is to perf...
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We consider the multi-scale analysis of the mechanics of multiphase composites with complex microstructure, based on the Goal-Oriented Adaptive modeling method [1-10]. The underlying approach of this method is to perform an initial analysis using homogenized, or effective, properties and sequentially improve these by adding only enough of the actual microstructure to control the modelingerror in a user-specified quantity of interest. The quantification of the modelingerror is established by providing residual-based a posteriori error estimates [11]. However, this involves solving an additional global dual problem and computing global integrals of governing residual functionals. In the case of multiphase composite materials this estimation process can be computationally prohibitive. We therefore propose a technique for local, a posteriori estimation of the modelingerror. It requires solving a local dual problem, of computationally small size, and computing local residual integrals. We introduce this new approach for the analysis of linear elastostatics problems of multiphase composites and show two-dimensional numerical verifications.
In the numerical approximation of hierarchical models of thin bodies, there are two separate contributions to the total error: (a) modelingerror is the error due to dimensional reduction, and (b) discretization error...
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In the numerical approximation of hierarchical models of thin bodies, there are two separate contributions to the total error: (a) modelingerror is the error due to dimensional reduction, and (b) discretization error is the error due to the numerical approximation. Traditionally, estimation of these errors has been performed in terms of abstract quantities like the global energy norm, which does not give a clear representation of the error in the local features of the solution. Recently, a new class of finite element errorestimation techniques has emerged wherein errors in a simulation are measured not in norms but in quantities of interest to the analyst. Following a brief overview of the literature, we focus on extending the theory of a posteriori estimation of the modelingerror to local quantities of interest that have physical meaning. Independent estimates are obtained for the modeling and discretization errors in quantities of interest. These local estimates are used to develop goal-oriented adaptive modeling strategies in which the model is automatically adapted by controlling the two errors independently and effectively to obtain local quantities of interest to within a preset level of accuracy. Numerical examples are presented to demonstrate the accuracy of the error estimates and the adaptive strategy. (c) 2007 Elsevier B.V. All rights reserved.
The reliability of computer predictions of physical events depends on several factors: the mathematical model of the event, the numerical approximation of the model, and the random nature of data characterizing the mo...
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The reliability of computer predictions of physical events depends on several factors: the mathematical model of the event, the numerical approximation of the model, and the random nature of data characterizing the model. This paper addresses the mathematical theories, algorithms, and results aimed at estimating and controlling modelingerror, numerical approximation error, and error due to randomness in material coefficients and loads. A posteriori error estimates are derived and applications to problems in solid mechanics are presented. (C) 2004 Elsevier B.V. All rights reserved.
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