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Uncertainty Quantification with Applications to Engineering Problems

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Abstract

The systematic quantification of the uncertainties affecting dynamical systems and the characterization of the uncertainty of their outcomes is critical for engineering design and analysis, where risks must be reduced as much as possible. Uncertainties stem naturally from our limitations in measurements, predictions and manufacturing, and we can say that any dynamical system used in engineering is subject to some of these uncertainties. The first part of this work presents an overview of the mathematical framework used in Uncertainty Quantification (UQ) analysis and introduces the spectral tensor-train (STT) decomposition, a novel high-order method for the effective propagation of uncertainties which aims at providing an exponential convergence rate while tackling the curse of dimensionality. The curse of dimensionality is a problem that afflicts many methods based on meta-models, for which the computational cost increases exponentially with the number of inputs of the approximated function – which we will call dimension in the following. The STT-decomposition is based on the Polynomial Chaos (PC) approximation and the low-rank decomposition of the function describing the Quantity of Interest of the considered problem. The low-rank decomposition is obtained through the discrete tensor-train decomposition, which is constructed using an optimization algorithm for the selection of the relevant points on which the function needs to be evaluated. The selection of these points is informed by the approximated function and thus it is able to adapt to its features. The number of function evaluations needed for the construction grows only linearly with the dimension and quadratically with the rank. In this work we will present and use the functional counterpart of this low-rank decomposition and, after proving some auxiliary properties, we will apply PC on it, obtaining the STT-decomposition. This will allow the decoupling of each dimension, leading to a much cheaper construction of the PC surrogate. In the associated paper, the capabilities of the STT-decomposition are checked on commonly used test functions and on an elliptic problem with random inputs. This work will also present three active research directions aimed at improving the efficiency of the STT-decomposition. In this context, we propose three new strategies for solving the ordering problem suffered by the tensor-train decomposition, for computing better estimates with respect to the norms usually employed in UQ and for the anisotropic adaptivity of the method. The second part of this work presents engineering applications of the UQ framework. Both the applications are characterized by functions whose evaluation is computationally expensive and thus the UQ analysis of the associated systems will benefit greatly from the application of methods which require few function evaluations. We first consider the propagation of the uncertainty and the sensitivity analysis of the non-linear dynamics of railway vehicles with suspension components whose characteristics are uncertain. These analysis are carried out using mostly PC methods, and resorting to random sampling methods for comparison and when strictly necessary. The second application of the UQ framework is on the propagation of the uncertainties entering a fully non-linear and dispersive model of water waves. This computationally challenging task is tackled with the adoption of state-of-the-art software for its numerical solution and of efficient PC methods. The aim of this study is the construction of stochastic benchmarks where to test UQ methodologies before being applied to full-scale problems, where efficient methods are necessary with today’s computational resources. The outcome of this work was also the creation of several freely available Python modules for Uncertainty Quantification, which are listed and described in the appendix.
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... The underlying principle here lies in an alternative representation of the tensor (x) in Eq. (1) as a low rank tensor by adopting tensor decomposition techniques. These approaches enable low rank approximations (LRA) and have been recently used to develop computationally efficient approximation models in high dimensional problems across multiple fields like quantum computations [28,29], stochastic wave simulation [30], vehicle nonlinear dynamics [30], approximating potential energy surfaces [31,32], fluid flow around blunt bodies [33], approximating elasto-viscoplastic constitutive laws [34] and computational homogenization of materials [35]. The rest of the paper is organized as follows: "Low Rank Approximation" section presents the general principle of LRA and subsequently the rank-one approximation. ...
... The underlying principle here lies in an alternative representation of the tensor (x) in Eq. (1) as a low rank tensor by adopting tensor decomposition techniques. These approaches enable low rank approximations (LRA) and have been recently used to develop computationally efficient approximation models in high dimensional problems across multiple fields like quantum computations [28,29], stochastic wave simulation [30], vehicle nonlinear dynamics [30], approximating potential energy surfaces [31,32], fluid flow around blunt bodies [33], approximating elasto-viscoplastic constitutive laws [34] and computational homogenization of materials [35]. The rest of the paper is organized as follows: "Low Rank Approximation" section presents the general principle of LRA and subsequently the rank-one approximation. ...
... The study presented in [18] showed that for MVEs with PBC, for a given level of spatial discretization the FFT-based solvers are more accurate than FE-based solvers. The calibration dataset has been generated using DAMASK for the elementary macroscale strain states in Eq. (30) with v ij = 2 × 10 −3 ∀ ij . In this work, the elastic properties of Ni were utilized, i.e., ℂ 11 = 246.5 GPa, ℂ 12 = 147.3 ...
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... Uncertainty Quantification is a very active research area that studies the impact of uncertainties on the prediction capabilities. Probability and the measure theory provide essential tools for the quantitative mathematical treatment of uncertainty [22]. In predictive science, UQ is defined as the process of identifying and quantifying the uncertainties associated with models, numerical algorithms, experiments, and their predicted outcomes or Quantity of Interest (QoI) [23]. ...
... Moreover, the sensitivity of the parameters plays a vital role in finding the solution of the inverse problem. The sensitivity makes the solution unstable, as a small change in the inputs x can lead to a significant change in the estimated model [22,50]. ...
... x M AP or mode as statistically named is quantified. MAP represents the values of inferred parameters with the highest probabilities of occurrence, 22) and in this case, there is no need to calculate the normalization factor z [57]. ...
Thesis
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This work presents a robust status monitoring approach for detecting damage in cantilever structures based on logistic functions. Also, a stochastic damage identification approach based on changes of eigenfrequencies is proposed. The proposed algorithms are verified using catenary poles of electrified railways track. The proposed damage features overcome the limitation of frequency-based damage identification methods available in the literature, which are valid to detect damage in structures to Level 1 only. Changes in eigenfrequencies of cantilever structures are enough to identify possible local damage at Level 3, i.e., to cover damage detection, localization, and quantification. The proposed algorithms identified the damage with relatively small errors, even at a high noise level.
... Uncertainty Quantification is a very active research area that studies the impact of uncertainties on the prediction capabilities. Probability and the measure theory provide essential tools for the quantitative mathematical treatment of uncertainty [22]. In predictive science, UQ is defined as the process of identifying and quantifying the uncertainties associated with models, numerical algorithms, experiments, and their predicted outcomes or Quantity of Interest (QoI) [23]. ...
... Moreover, the sensitivity of the parameters plays a vital role in finding the solution of the inverse problem. The sensitivity makes the solution unstable, as a small change in the inputs x can lead to a significant change in the estimated model [22,50]. ...
... x M AP or mode as statistically named is quantified. MAP represents the values of inferred parameters with the highest probabilities of occurrence, 22) and in this case, there is no need to calculate the normalization factor z [57]. ...
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This work presents a robust status monitoring approach for detecting damage in cantilever structures based on logistic functions. Also, a stochastic damage identification approach based on changes of eigenfrequencies is proposed. The proposed algorithms are verified using catenary poles of electrified railways track. The proposed damage features overcome the limitation of frequency-based damage identification methods available in the literature, which are valid to detect damage in structures to Level 1 only. Changes in eigenfrequencies of cantilever structures are enough to identify possible local damage at Level 3, i.e., to cover damage detection, localization, and quantification. The proposed algorithms identified the damage with relatively small errors, even at a high noise level.
... Uncertainty Quantification is a very active research area that studies the impact of uncertainties on the prediction capabilities. Probability and the measure theory provide essential tools for the quantitative mathematical treatment of uncertainty [22]. In predictive science, UQ is defined as the process of identifying and quantifying the uncertainties associated with models, numerical algorithms, experiments, and their predicted outcomes or Quantity of Interest (QoI) [23]. ...
... Moreover, the sensitivity of the parameters plays a vital role in finding the solution of the inverse problem. The sensitivity makes the solution unstable, as a small change in the inputs x can lead to a significant change in the estimated model [22,50]. ...
... x M AP or mode as statistically named is quantified. MAP represents the values of inferred parameters with the highest probabilities of occurrence, 22) and in this case, there is no need to calculate the normalization factor z [57]. ...
Thesis
Full-text available
This work presents a robust status monitoring approach for detecting damage in cantilever structures based on logistic functions. Also, a stochastic damage identification approach based on changes of eigenfrequencies is proposed. The proposed algorithms are verified using catenary poles of electrified railways track. The proposed damage features overcome the limitation of frequency-based damage identification methods available in the literature, which are valid to detect damage in structures to Level 1 only. Changes in eigenfrequencies of cantilever structures are enough to identify possible local damage at Level 3, i.e., to cover damage detection, localization, and quantification. The proposed algorithms identified the damage with relatively small errors, even at a high noise level.
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