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Literatura académica sobre el tema "Reliable quantification of uncertainty"

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Publicado: 4 de junio de 2021

Última modificación: 9 de febrero de 2022

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Artículos de revistas sobre el tema "Reliable quantification of uncertainty"

Xue, Yujia, Shiyi Cheng, Yunzhe Li y Lei Tian. "Reliable deep-learning-based phase imaging with uncertainty quantification". Optica 6, n.º 5 (7 de mayo de 2019): 618. http://dx.doi.org/10.1364/optica.6.000618.

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Russi, Trent, Andy Packard y Michael Frenklach. "Uncertainty quantification: Making predictions of complex reaction systems reliable". Chemical Physics Letters 499, n.º 1-3 (octubre de 2010): 1–8. http://dx.doi.org/10.1016/j.cplett.2010.09.009.

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Xue, Yujia, Shiyi Cheng, Yunzhe Li y Lei Tian. "Reliable deep-learning-based phase imaging with uncertainty quantification: erratum". Optica 7, n.º 4 (9 de abril de 2020): 332. http://dx.doi.org/10.1364/optica.392632.

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Alrashed, Mosab, Theoklis Nikolaidis, Pericles Pilidis y Soheil Jafari. "Turboelectric Uncertainty Quantification and Error Estimation in Numerical Modelling". Applied Sciences 10, n.º 5 (6 de marzo de 2020): 1805. http://dx.doi.org/10.3390/app10051805.

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Turboelectric systems can be considered complex systems that may comprise errors and uncertainty. Uncertainty quantification and error estimation processes can, therefore, be useful in achieving accurate system parameters. Uncertainty quantification and error estimation processes, however, entail some stages that provide results that are more positive. Since accurate approximation and power optimisation are crucial processes, it is essential to focus on higher accuracy levels. Integrating computational models with reliable algorithms into the computation processes leads to a higher accuracy level. Some of the current models, like Monte Carlo and Latin hypercube sampling, are reliable. This paper focuses on uncertainty quantification and error estimation processes in turboelectric numerical modelling. The current study integrates the current evidence with scholarly sources to ensure the incorporation of the most reliable evidence into the conclusions. It is evident that studies on the current subject began a long time ago, and there is sufficient scholarly evidence for analysis. The case study used to obtain this evidence is NASA N3-X, with three aircraft conditions: rolling to take off, cruising and taking off. The results show that the electrical elements in turboelectric systems can have decent outcomes in statistical analysis. Moreover, the risk of having overload branches is up to 2% of the total aircraft operation lifecycle, and the enhancement of the turboelectric system through electrical power optimisation management could lead to higher performance.

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Scheidt, C., I. Zabalza-Mezghani, M. Feraille y D. Collombier. "Toward a Reliable Quantification of Uncertainty on Production Forecasts: Adaptive Experimental Designs". Oil & Gas Science and Technology - Revue de l'IFP 62, n.º 2 (marzo de 2007): 207–24. http://dx.doi.org/10.2516/ogst:2007018.

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Tran, Anh V. y Yan Wang. "Reliable Molecular Dynamics: Uncertainty quantification using interval analysis in molecular dynamics simulation". Computational Materials Science 127 (febrero de 2017): 141–60. http://dx.doi.org/10.1016/j.commatsci.2016.10.021.

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Liu, Xuejun, Hailong Tang, Xin Zhang y Min Chen. "Gaussian Process Model-Based Performance Uncertainty Quantification of a Typical Turboshaft Engine". Applied Sciences 11, n.º 18 (8 de septiembre de 2021): 8333. http://dx.doi.org/10.3390/app11188333.

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The gas turbine engine is a widely used thermodynamic system for aircraft. The demand for quantifying the uncertainty of engine performance is increasing due to the expectation of reliable engine performance design. In this paper, a fast, accurate, and robust uncertainty quantification method is proposed to investigate the impact of component performance uncertainty on the performance of a classical turboshaft engine. The Gaussian process model is firstly utilized to accurately approximate the relationships between inputs and outputs of the engine performance simulation model. Latin hypercube sampling is subsequently employed to perform uncertainty analysis of the engine performance. The accuracy, robustness, and convergence rate of the proposed method are validated by comparing with the Monte Carlo sampling method. Two main scenarios are investigated, where uncertain parameters are considered to be mutually independent and partially correlated, respectively. Finally, the variance-based sensitivity analysis is used to determine the main contributors to the engine performance uncertainty. Both approximation and sampling errors are explained in the uncertainty quantification to give more accurate results. The final results yield new insights about the engine performance uncertainty and the important component performance parameters.

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Ryu, Seongok, Yongchan Kwon y Woo Youn Kim. "A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification". Chemical Science 10, n.º 36 (2019): 8438–46. http://dx.doi.org/10.1039/c9sc01992h.

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Ba, Huanhuan, Shenglian Guo, Yixuan Zhong, Shaokun He y Xushu Wu. "Quantification of the forecast uncertainty using conditional probability and updating models". Hydrology Research 50, n.º 6 (27 de septiembre de 2019): 1751–71. http://dx.doi.org/10.2166/nh.2019.094.

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Abstract Quantifying forecast uncertainty is of great importance for reservoir operation and flood control. However, deterministic hydrological forecasts do not consider forecast uncertainty. This study develops a conditional probability model based on copulas to quantify forecast uncertainty. Three updating models, namely auto-regressive (AR) model, AR exogenous input model, and adaptive neuro fuzzy inference system model, are applied to update raw deterministic inflow forecasts of the Three Gorges Reservoir on the Yangtze River, China with lead times of 1d, 2d, and 3d. Results show that the conditional probability model provides a reasonable and reliable forecast interval. The updating models both enhance the forecast accuracy and improve the reliability of probabilistic forecasts. The conditional probability model based on copula functions is a useful tool to describe and quantify forecast uncertainty, and using an updating model is an effective measure to improve the accuracy and reliability of probabilistic forecast.

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Zhou, Shuang, Jianguo Zhang, Lingfei You y Qingyuan Zhang. "Uncertainty propagation in structural reliability with implicit limit state functions under aleatory and epistemic uncertainties". Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, n.º 2 (4 de febrero de 2021): 231–41. http://dx.doi.org/10.17531/ein.2021.2.3.

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Uncertainty propagation plays a pivotal role in structural reliability assessment. This paper introduces a novel uncertainty propagation method for structural reliability under different knowledge stages based on probability theory, uncertainty theory and chance theory. Firstly, a surrogate model combining the uniform design and least-squares method is presented to simulate the implicit limit state function with random and uncertain variables. Then, a novel quantification method based on chance theory is derived herein, to calculate the structural reliability under mixed aleatory and epistemic uncertainties. The concepts of chance reliability and chance reliability index (CRI) are defined to show the reliable degree of structure. Besides, the selection principles of uncertainty propagation types and the corresponding reliability estimation methods are given according to the different knowledge stages. The proposed methods are finally applied in a practical structural reliability problem, which illustrates the effectiveness and advantages of the techniques presented in this work.

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Tesis sobre el tema "Reliable quantification of uncertainty"

Elfverson, Daniel. "Multiscale Methods and Uncertainty Quantification". Doctoral thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-262354.

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In this thesis we consider two great challenges in computer simulations of partial differential equations: multiscale data, varying over multiple scales in space and time, and data uncertainty, due to lack of or inexact measurements. We develop a multiscale method based on a coarse scale correction, using localized fine scale computations. We prove that the error in the solution produced by the multiscale method decays independently of the fine scale variation in the data or the computational domain. We consider the following aspects of multiscale methods: continuous and discontinuous underlying numerical methods, adaptivity, convection-diffusion problems, Petrov-Galerkin formulation, and complex geometries. For uncertainty quantification problems we consider the estimation of p-quantiles and failure probability. We use spatial a posteriori error estimates to develop and improve variance reduction techniques for Monte Carlo methods. We improve standard Monte Carlo methods for computing p-quantiles and multilevel Monte Carlo methods for computing failure probability.

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Parkinson, Matthew. "Uncertainty quantification in Radiative Transport". Thesis, University of Bath, 2019. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767610.

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We study how uncertainty in the input data of the Radiative Transport equation (RTE), affects the distribution of (functionals of) its solution (the output data). The RTE is an integro-differential equation, in up to seven independent variables, that models the behaviour of rarefied particles (such as photons and neutrons) in a domain. Its applications include nuclear reactor design, radiation shielding, medical imaging, optical tomography and astrophysics. We focus on the RTE in the context of nuclear reactor physics where, to design and maintain safe reactors, understanding the effects of uncertainty is of great importance. There are many potential sources of uncertainty within a nuclear reactor. These include the geometry of the reactor, the material composition and reactor wear. Here we consider uncertainty in the macroscopic cross-sections ('the coefficients'), representing them as correlated spatial random fields. We wish to estimate the statistics of a problem-specific quantity of interest (under the influence of the given uncertainty in the cross-sections), which is defined as a functional of the scalar flux. This is the forward problem of Uncertainty Quantification. We seek accurate and efficient methods for estimating these statistics. Thus far, the research community studying Uncertainty Quantification in radiative transport has focused on the Polynomial Chaos expansion. However, it is known that the number of terms in the expansion grows exponentially with respect to the number of stochastic dimensions and the order of the expansion, i.e. polynomial chaos suffers from the curse of dimensionality. Instead, we focus our attention on variants of Monte Carlo sampling - studying standard and quasi-Monte Carlo methods, and their multilevel and multi-index variants. We show numerically that the quasi-Monte Carlo rules, and the multilevel variance reduction techniques, give substantial gains over the standard Monte Carlo method for a variety of radiative transport problems. Moreover, we report problems in up to 3600 stochastic dimensions, far beyond the capability of polynomial chaos. A large part of this thesis is focused towards a rigorous proof that the multilevel Monte Carlo method is superior to the standard Monte Carlo method, for the RTE in one spatial and one angular dimension with random cross-sections. This is the first rigorous theory of Uncertainty Quantification for transport problems and the first rigorous theory for Uncertainty Quantification for any PDE problem which accounts for a path-dependent stability condition. To achieve this result, we first present an error analysis (including a stability bound on the discretisation parameters) for the combined spatial and angular discretisation of the spatially heterogeneous RTE, which is explicit in the heterogeneous coefficients. We can then extend this result to prove probabilistic bounds on the error, under assumptions on the statistics of the cross-sections and provided the discretisation satisfies the stability condition pathwise. The multilevel Monte Carlo complexity result follows. Amongst other novel contributions, we: introduce a method which combines a direct and iterative solver to accelerate the computation of the scalar flux, by adaptively choosing the fastest solver based on the given coefficients; numerically test an iterative eigensolver, which uses a single source iteration within each loop of a shifted inverse power iteration; and propose a novel model for (random) heterogeneity in concrete which generates (piecewise) discontinuous coefficients according to the material type, but where the composition of materials are spatially correlated.

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Carson, J. "Uncertainty quantification in palaeoclimate reconstruction". Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/29076/.

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Studying the dynamics of the palaeoclimate is a challenging problem. Part of the challenge lies in the fact that our understanding must be based on only a single realisation of the climate system. With only one climate history, it is essential that palaeoclimate data are used to their full extent, and that uncertainties arising from both data and modelling are well characterised. This is the motivation behind this thesis, which explores approaches for uncertainty quantification in problems related to palaeoclimate reconstruction. We focus on uncertainty quantification problems for the glacial-interglacial cycle, namely parameter estimation, model comparison, and age estimation of palaeoclimate observations. We develop principled data assimilation schemes that allow us to assimilate palaeoclimate data into phenomenological models of the glacial-interglacial cycle. The statistical and modelling approaches we take in this thesis means that this amounts to the task of performing Bayesian inference for multivariate stochastic differential equations that are only partially observed. One contribution of this thesis is the synthesis of recent methodological advances in approximate Bayesian computation and particle filter methods. We provide an up-to-date overview that relates the different approaches and provides new insights into their performance. Through simulation studies we compare these approaches using a common benchmark, and in doing so we highlight the relative strengths and weaknesses of each method. There are two main scientific contributions in this thesis. The first is that by using inference methods to jointly perform parameter estimation and model comparison, we demonstrate that the current two-stage practice of first estimating observation times, and then treating them as fixed for subsequent analysis, leads to conclusions that are not robust to the methods used for estimating the observation times. The second main contribution is the development of a novel age model based on a linear sediment accumulation model. By extending the target of the particle filter we are able to jointly perform parameter estimation, model comparison, and observation age estimation. In doing so, we are able to perform palaeoclimate reconstruction using sediment core data that takes age uncertainty in the data into account, thus solving the problem of dating uncertainty highlighted above.

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Boopathy, Komahan. "Uncertainty Quantification and Optimization Under Uncertainty Using Surrogate Models". University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1398302731.

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Cheng, Haiyan. "Uncertainty Quantification and Uncertainty Reduction Techniques for Large-scale Simulations". Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28444.

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Modeling and simulations of large-scale systems are used extensively to not only better understand a natural phenomenon, but also to predict future events. Accurate model results are critical for design optimization and policy making. They can be used effectively to reduce the impact of a natural disaster or even prevent it from happening. In reality, model predictions are often affected by uncertainties in input data and model parameters, and by incomplete knowledge of the underlying physics. A deterministic simulation assumes one set of input conditions, and generates one result without considering uncertainties. It is of great interest to include uncertainty information in the simulation. By ``Uncertainty Quantification,'' we denote the ensemble of techniques used to model probabilistically the uncertainty in model inputs, to propagate it through the system, and to represent the resulting uncertainty in the model result. This added information provides a confidence level about the model forecast. For example, in environmental modeling, the model forecast, together with the quantified uncertainty information, can assist the policy makers in interpreting the simulation results and in making decisions accordingly. Another important goal in modeling and simulation is to improve the model accuracy and to increase the model prediction power. By merging real observation data into the dynamic system through the data assimilation (DA) technique, the overall uncertainty in the model is reduced. With the expansion of human knowledge and the development of modeling tools, simulation size and complexity are growing rapidly. This poses great challenges to uncertainty analysis techniques. Many conventional uncertainty quantification algorithms, such as the straightforward Monte Carlo method, become impractical for large-scale simulations. New algorithms need to be developed in order to quantify and reduce uncertainties in large-scale simulations. This research explores novel uncertainty quantification and reduction techniques that are suitable for large-scale simulations. In the uncertainty quantification part, the non-sampling polynomial chaos (PC) method is investigated. An efficient implementation is proposed to reduce the high computational cost for the linear algebra involved in the PC Galerkin approach applied to stiff systems. A collocation least-squares method is proposed to compute the PC coefficients more efficiently. A novel uncertainty apportionment strategy is proposed to attribute the uncertainty in model results to different uncertainty sources. The apportionment results provide guidance for uncertainty reduction efforts. The uncertainty quantification and source apportionment techniques are implemented in the 3-D Sulfur Transport Eulerian Model (STEM-III) predicting pollute concentrations in the northeast region of the United States. Numerical results confirm the efficacy of the proposed techniques for large-scale systems and the potential impact for environmental protection policy making. ``Uncertainty Reduction'' describes the range of systematic techniques used to fuse information from multiple sources in order to increase the confidence one has in model results. Two DA techniques are widely used in current practice: the ensemble Kalman filter (EnKF) and the four-dimensional variational (4D-Var) approach. Each method has its advantages and disadvantages. By exploring the error reduction directions generated in the 4D-Var optimization process, we propose a hybrid approach to construct the error covariance matrix and to improve the static background error covariance matrix used in current 4D-Var practice. The updated covariance matrix between assimilation windows effectively reduces the root mean square error (RMSE) in the solution. The success of the hybrid covariance updates motivates the hybridization of EnKF and 4D-Var to further reduce uncertainties in the simulation results. Numerical tests show that the hybrid method improves the model accuracy and increases the model prediction quality.
Ph. D.

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Fiorito, Luca. "Nuclear data uncertainty propagation and uncertainty quantification in nuclear codes". Doctoral thesis, Universite Libre de Bruxelles, 2016. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/238375.

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Uncertainties in nuclear model responses must be quantified to define safety limits, minimize costs and define operational conditions in design. Response uncertainties can also be used to provide a feedback on the quality and reliability of parameter evaluations, such as nuclear data. The uncertainties of the predictive model responses sprout from several sources, e.g. nuclear data, model approximations, numerical solvers, influence of random variables. It was proved that the largest quantifiable sources of uncertainty in nuclear models, such as neutronics and burnup calculations, are the nuclear data, which are provided as evaluated best estimates and uncertainties/covariances in data libraries. Nuclear data uncertainties and/or covariances must be propagated to the model responses with dedicated uncertainty propagation tools. However, most of the nuclear codes for neutronics and burnup models do not have these capabilities and produce best-estimate results without uncertainties. In this work, the nuclear data uncertainty propagation was concentrated on the SCK•CEN code burnup ALEPH-2 and the Monte Carlo N-Particle code MCNP.Two sensitivity analysis procedures, i.e. FSAP and ASAP, based on linear perturbation theory were implemented in ALEPH-2. These routines can propagate nuclear data uncertainties in pure decay models. ASAP and ALEPH-2 were tested and validated against the decay heat and uncertainty quantification for several fission pulses and for the MYRRHA subcritical system. The decay uncertainty is necessary to define the reliability of the decay heat removal systems and prevent overheating and mechanical failure of the reactor components. It was proved that the propagation of independent fission yield and decay data uncertainties can be carried out with ASAP also in neutron irradiation models. Because of the ASAP limitations, the Monte Carlo sampling solver NUDUNA was used to propagate cross section covariances. The applicability constraints of ASAP drove our studies towards the development of a tool that could propagate the uncertainty of any nuclear datum. In addition, the uncertainty propagation tool was supposed to operate with multiple nuclear codes and systems, including non-linear models. The Monte Carlo sampling code SANDY was developed. SANDY is independent of the predictive model, as it only interacts with the nuclear data in input. Nuclear data are sampled from multivariate probability density functions and propagated through the model according to the Monte Carlo sampling theory. Not only can SANDY propagate nuclear data uncertainties and covariances to the model responses, but it is also able to identify the impact of each uncertainty contributor by decomposing the response variance. SANDY was extensively tested against integral parameters and was used to quantify the neutron multiplication factor uncertainty of the VENUS-F reactor.Further uncertainty propagation studies were carried out for the burnup models of light water reactor benchmarks. Our studies identified fission yields as the largest source of uncertainty for the nuclide density evolution curves of several fission products. However, the current data libraries provide evaluated fission yields and uncertainties devoid of covariance matrices. The lack of fission yield covariance information does not comply with the conservation equations that apply to a fission model, and generates inconsistency in the nuclear data. In this work, we generated fission yield covariance matrices using a generalised least-square method and a set of physical constraints. The fission yield covariance matrices solve the inconsistency in the nuclear data libraries and reduce the role of the fission yields in the uncertainty quantification of burnup models responses.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished

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Alvarado, Martin Guillermo. "Quantification of uncertainty during history matching". Texas A&M University, 2003. http://hdl.handle.net/1969/463.

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Jimenez, Edwin. "Uncertainty quantification of nonlinear stochastic phenomena". Tallahassee, Florida : Florida State University, 2009. http://etd.lib.fsu.edu/theses/available/etd-11092009-161351/.

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Thesis (Ph. D.)--Florida State University, 2009.
Advisor: M.Y. Hussaini, Florida State University, College of Arts and Sciences, Dept. of Mathematics. Title and description from dissertation home page (viewed on Mar. 16, 2010). Document formatted into pages; contains xii, 113 pages. Includes bibliographical references.

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Kalmikov, Alexander G. "Uncertainty Quantification in ocean state estimation". Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/79291.

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Thesis (Ph. D.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 158-160).
Quantifying uncertainty and error bounds is a key outstanding challenge in ocean state estimation and climate research. It is particularly difficult due to the large dimensionality of this nonlinear estimation problem and the number of uncertain variables involved. The "Estimating the Circulation and Climate of the Oceans" (ECCO) consortium has developed a scalable system for dynamically consistent estimation of global time-evolving ocean state by optimal combination of ocean general circulation model (GCM) with diverse ocean observations. The estimation system is based on the "adjoint method" solution of an unconstrained least-squares optimization problem formulated with the method of Lagrange multipliers for fitting the dynamical ocean model to observations. The dynamical consistency requirement of ocean state estimation necessitates this approach over sequential data assimilation and reanalysis smoothing techniques. In addition, it is computationally advantageous because calculation and storage of large covariance matrices is not required. However, this is also a drawback of the adjoint method, which lacks a native formalism for error propagation and quantification of assimilated uncertainty. The objective of this dissertation is to resolve that limitation by developing a feasible computational methodology for uncertainty analysis in dynamically consistent state estimation, applicable to the large dimensionality of global ocean models. Hessian (second derivative-based) methodology is developed for Uncertainty Quantification (UQ) in large-scale ocean state estimation, extending the gradient-based adjoint method to employ the second order geometry information of the model-data misfit function in a high-dimensional control space. Large error covariance matrices are evaluated by inverting the Hessian matrix with the developed scalable matrix-free numerical linear algebra algorithms. Hessian-vector product and Jacobian derivative codes of the MIT general circulation model (MITgcm) are generated by means of algorithmic differentiation (AD). Computational complexity of the Hessian code is reduced by tangent linear differentiation of the adjoint code, which preserves the speedup of adjoint checkpointing schemes in the second derivative calculation. A Lanczos algorithm is applied for extracting the leading rank eigenvectors and eigenvalues of the Hessian matrix. The eigenvectors represent the constrained uncertainty patterns. The inverse eigenvalues are the corresponding uncertainties. The dimensionality of UQ calculations is reduced by eliminating the uncertainty null-space unconstrained by the supplied observations. Inverse and forward uncertainty propagation schemes are designed for assimilating observation and control variable uncertainties, and for projecting these uncertainties onto oceanographic target quantities. Two versions of these schemes are developed: one evaluates reduction of prior uncertainties, while another does not require prior assumptions. The analysis of uncertainty propagation in the ocean model is time-resolving. It captures the dynamics of uncertainty evolution and reveals transient and stationary uncertainty regimes. The system is applied to quantifying uncertainties of Antarctic Circumpolar Current (ACC) transport in a global barotropic configuration of the MITgcm. The model is constrained by synthetic observations of sea surface height and velocities. The control space consists of two-dimensional maps of initial and boundary conditions and model parameters. The size of the Hessian matrix is 0(1010) elements, which would require 0(60GB) of uncompressed storage. It is demonstrated how the choice of observations and their geographic coverage determines the reduction in uncertainties of the estimated transport. The system also yields information on how well the control fields are constrained by the observations. The effects of controls uncertainty reduction due to decrease of diagonal covariance terms are compared to dynamical coupling of controls through off-diagonal covariance terms. The correlations of controls introduced by observation uncertainty assimilation are found to dominate the reduction of uncertainty of transport. An idealized analytical model of ACC guides a detailed time-resolving understanding of uncertainty dynamics. Keywords: Adjoint model uncertainty, sensitivity, posterior error reduction, reduced rank Hessian matrix, Automatic Differentiation, ocean state estimation, barotropic model, Drake Passage transport.
by Alexander G. Kalmikov.
Ph.D.

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Roy, Pamphile. "Uncertainty quantification in high dimensional problems". Thesis, Toulouse, INPT, 2019. http://www.theses.fr/2019INPT0038.

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Les incertitudes font partie du monde qui nous entoure. Se limiter à une seule valeur nominale est bien souvent trop restrictif, et ce d'autant plus lorsqu'il est question de systèmes complexes. Comprendre la nature et l'impact de ces incertitudes est devenu un aspect important de tout travail d'ingénierie. D'un point de vue sociétal, les incertitudes jouent un rôle important dans les processus de décision. Les dernières recommandations de la Commission européenne en matière d'analyses des risques souligne l'importance du traitement des incertitudes. Afin de comprendre les incertitudes, une nouvelle discipline mathématique appelée la quantification des incertitudes a été créée. Ce domaine regroupe un large éventail de méthodes d'analyse statistique qui visent à lier des perturbations sur les paramètres d'entrée d'un système (plan d'expérience) à une quantité d'intérêt. L'objectif de ce travail de thèse est de proposer des améliorations sur divers aspects méthodologiques de la quantification des incertitudes dans le cadre de simulation numérique coûteuse. Cela passe par une utilisation des méthodes existantes avec une approche multi-stratégie mais aussi la création de nouvelles méthodes. Dans ce contexte, de nouvelles méthodes d'échantillonnage et de ré-échantillonnage ont été développées afin de mieux capturer la variabilité dans le cas d'un problème de grande dimension. Par ailleurs, de nouvelles méthodes de visualisation des incertitudes sont proposées dans le cas d'une grande dimension des paramètres d'entrée et d'une grande dimension de la quantité d'intérêt. Les méthodes développées peuvent être utilisées dans divers domaines comme la modélisation hydraulique ou encore la modélisation aérodynamique. Leur apport est démontré sur des systèmes réalistes en faisant appel à des outils de mécanique des fluides numérique. Enfin, ces méthodes ne sont pas seulement utilisables dans le cadre de simulation numérique, mais elles peuvent être utilisées sur de réels dispositifs expérimentaux
Uncertainties are predominant in the world that we know. Referring therefore to a nominal value is too restrictive, especially when it comes to complex systems. Understanding the nature and the impact of these uncertainties has become an important aspect of engineering work. On a societal point of view, uncertainties play a role in terms of decision-making. From the European Commission through the Better Regulation Guideline, impact assessments are now advised to take uncertainties into account. In order to understand the uncertainties, the mathematical field of uncertainty quantification has been formed. UQ encompasses a large palette of statistical tools and it seeks to link a set of input perturbations on a system (design of experiments) towards a quantity of interest. The purpose of this work is to propose improvements on various methodological aspects of uncertainty quantification applied to costly numerical simulations. This is achieved by using existing methods with a multi-strategy approach but also by creating new methods. In this context, novel sampling and resampling approaches have been developed to better capture the variability of the physical phenomenon when dealing with a high number of perturbed inputs. These allow to reduce the number of simulation required to describe the system. Moreover, novel methods are proposed to visualize uncertainties when dealing with either a high dimensional input parameter space or a high dimensional quantity of interest. The developed methods can be used in various fields like hydraulic modelling and aerodynamic modelling. Their capabilities are demonstrated in realistic systems using well established computational fluid dynamics tools. Lastly, they are not limited to the use of numerical experiments and can be used equally for real experiments

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Libros sobre el tema "Reliable quantification of uncertainty"

Soize, Christian. Uncertainty Quantification. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0.

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Sullivan, T. J. Introduction to Uncertainty Quantification. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23395-6.

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Ghanem, Roger, David Higdon y Houman Owhadi, eds. Handbook of Uncertainty Quantification. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-11259-6.

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Dienstfrey, Andrew M. y Ronald F. Boisvert, eds. Uncertainty Quantification in Scientific Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32677-6.

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Le Maître, O. P. y Omar M. Knio. Spectral Methods for Uncertainty Quantification. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3520-2.

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Grigoriu, Mircea. Stochastic Systems: Uncertainty Quantification and Propagation. London: Springer London, 2012.

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McClarren, Ryan G. Uncertainty Quantification and Predictive Computational Science. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0.

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Bijl, Hester, Didier Lucor, Siddhartha Mishra y Christoph Schwab, eds. Uncertainty Quantification in Computational Fluid Dynamics. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00885-1.

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Mao, Zhu, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47638-0.

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Atamturktur, H. Sezer, Babak Moaveni, Costas Papadimitriou y Tyler Schoenherr, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15224-0.

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Capítulos de libros sobre el tema "Reliable quantification of uncertainty"

Terejanu, Gabriel. "From Model Calibration and Validation to Reliable Extrapolations". En Model Validation and Uncertainty Quantification, Volume 3, 205–11. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29754-5_20.

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Gottlieb, Allan, Utpal Banerjee, Gianfranco Bilardi, Geppino Pucci, William Carlson y Phillip Merkey. "Uncertainty Quantification". En Encyclopedia of Parallel Computing, 2103. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2400.

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Borgonovo, Emanuele. "Uncertainty Quantification". En Sensitivity Analysis, 117–27. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52259-3_13.

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Römer, Ulrich. "Uncertainty Quantification". En Springer Theses, 65–90. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41294-8_5.

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Richardson, Andrew D., Marc Aubinet, Alan G. Barr, David Y. Hollinger, Andreas Ibrom, Gitta Lasslop y Markus Reichstein. "Uncertainty Quantification". En Eddy Covariance, 173–209. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2351-1_7.

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Saouma, Victor E. y M. Amin Hariri-Ardebili. "Uncertainty Quantification". En Aging, Shaking, and Cracking of Infrastructures, 423–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57434-5_18.

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Sadeghi, Behnam, Eric Grunsky y Vera Pawlowsky-Glahn. "Uncertainty Quantification". En Encyclopedia of Mathematical Geosciences, 1–7. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-26050-7_334-1.

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Soize, Christian. "Fundamental Notions in Stochastic Modeling of Uncertainties and Their Propagation in Computational Models". En Uncertainty Quantification, 1–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_1.

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Soize, Christian. "Random Fields and Uncertainty Quantification in Solid Mechanics of Continuum Media". En Uncertainty Quantification, 245–300. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_10.

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Soize, Christian. "Elements of Probability Theory". En Uncertainty Quantification, 17–40. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_2.

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Actas de conferencias sobre el tema "Reliable quantification of uncertainty"

Tanaka, Shusei, Kaveh Dehghani y Wang Zhenzhen. "Methods for Probabilistic Uncertainty Quantification with Reliable Subsurface Assessment and Robust Decision-Making". En SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 2019. http://dx.doi.org/10.2118/195837-ms.

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Beck, James L. y Young Huang. "Bayesian uncertainty quantification and sparse Bayesian learning for model updating in SHM". En Joint COST TU1402 - COST TU1406 - IABSE WC1 Workshop: The Value of Structural Health Monitoring for the reliable Bridge Management. University of Zagreb Faculty of Civil Engineering, 2017. http://dx.doi.org/10.5592/co/bshm2017.2.2.

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Cheng, Yueming, W. John Lee y Duane A. McVay. "Quantification of Uncertainty in Reserves Estimation From Decline Curve Analysis of Production Data for Unconventional Reservoirs". En ASME 2007 26th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2007. http://dx.doi.org/10.1115/omae2007-29694.

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Decline curve analysis is the most commonly used technique to estimate reserves from historical production data for evaluation of unconventional resources. Quantifying uncertainty of reserve estimates is an important issue in decline curve analysis, particularly for unconventional resources since forecasting future performance is particularly difficult in analysis of unconventional oil or gas wells. Probabilistic approaches are sometimes used to provide a distribution of reserve estimates with three confidence levels (P10, P50 and P90) and a corresponding 80% confidence interval to quantify uncertainties. Our investigation indicates that uncertainty is commonly underestimated in practice when using traditional statistical analyses. The challenge in probabilistic reserves estimation is not only how to appropriately characterize probabilistic properties of complex production data sets, but also how to determine and then improve the reliability of the uncertainty quantifications. In this paper, we present an advanced technique for probabilistic quantification of reserve estimates using decline curve analysis. We examine the reliability of uncertainty quantification of reserve estimates by analyzing actual oil and gas wells that have produced to near-abandonment conditions, and also show how uncertainty in reserves estimates changes with time as more data become available. We demonstrate that our method provides more reliable probabilistic reserves estimation than other methods proposed in the literature. These results have important impacts on economic risk analysis and on reservoir management.

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Samson, Sundeep, Sravya Thoomu, Georges Fadel y James Reneke. "Reliable Design Optimization Under Aleatory and Epistemic Uncertainties". En ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86473.

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In the engineering design community, decision making methodologies to select the “best” design from among feasible designs is one of the most critical part of the design process. As the design models become increasingly realistic, the decision making methodology becomes increasingly complex. That is, because of the realistic design models, more and more decisions are made under uncertain environments without making any unrealistic assumptions. A decision maker is usually forced to work with uncertainties of which some stochastic information is known (aleatory) or no information is known (epistemic). In this paper, we discuss both forms of uncertainties and their modeling methodologies. We also define risk as a random function of these uncertainties and propose a risk quantification technique. Existing methods to handle aleatory uncertainties are discussed and an alternative search based decision making methodology is proposed to handle epistemic uncertainties. We illustrate our decision making methodology using the side-impact crashworthiness problem presented by Gu, et.al. [1]. In addition to the aleatory uncertainties considered by these researchers, we model a couple of non-design variables as epistemic uncertainties in our decision problem. Lack of information of these epistemic uncertainties increases the complexity of the side-impact crashworthiness problem significantly. However, the proposed methodology helps to identify a robust design with respect to epistemic uncertainty.

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Sankaran, Sivasubramaniyan, Giuseppe Sassanelli, Giuseppe Iurisci y Andrea Panizza. "Performance Uncertainty Quantification for Centrifugal Compressors: Part 2—Flange to Flange Variability". En ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-68220.

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This paper presents a methodology to control flange to flange performance prediction of centrifugal compressors using a probabilistic approach. In order to have reliable prediction for the performance of centrifugal compressors, a thorough knowledge of critical parameters contributing to the deviation and an efficient way to control the variation of these parameters becomes necessary. This paper discusses about a robust methodology for identifying and controlling the variation of these parameters and hence the predicted performance. This probabilistic technique involves a Design of Experiments (DoE) study to handle large number of input parameters, sensitivity study to identify critical ones and a Monte-Carlo based approach to identify the uncertainty in flange to flange performance. This approach takes into consideration the compressor stage performance variability driven by impeller manufacturing tolerances, statoric component losses variability and leakages variability in order to compute overall performance variation in a compressor. An in-house developed probabilistic optimization code (PEZ) is interfaced with a well-validated & calibrated thermodynamic tool to analyse large sets of possible combinations and to provide best possible solution for a given design space. This concept is successfully applied for different compressor configurations by varying the stage numbers and process conditions. The results give an insight on the main sources and magnitude of variations on compressor performance, thus enabling to control the predictions in an efficient way. This methodology will provide a novel and an efficient way to generate robust compressor performance, where it will be possible to take into account design and manufacturing uncertainty. The use of this methodology can thus drastically improve the performance predictability and risk associated with each compressor selection.

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Lewis, John R., Dusty Brooks y Michael L. Benson. "Methods for Uncertainty Quantification and Comparison of Weld Residual Stress Measurements and Predictions". En ASME 2017 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/pvp2017-65552.

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Weld residual stress (WRS) is a major driver of primary water stress corrosion cracking (PWSCC) in safety critical components of nuclear power plants. Accurate understanding of WRS is thus crucial for reliable prediction of safety performance of component design throughout the life of the plant. However, measurement uncertainty in WRS is significant, driven by the method and the indirect nature in which WRS must be measured. Likewise, model predictions of WRS vary due to uncertainty induced by individual modeling choices. The uncertainty in WRS measurements and modeling predictions is difficult to quantify and complicates the use of WRS measurements in validating WRS predictions for future use in safety evaluations. This paper describes a methodology for quantifying WRS uncertainty that facilitates the comparison of predictions and measurements and informs design safety evaluations. WRS is considered as a function through the depth of the weld. To quantify its uncertainty, functional data analysis techniques are utilized to account for the two types of variation observed in functional data: phase and amplitude. Phase variability, also known as horizontal variability, describes the variability in the horizontal direction (i.e., through the depth of the weld). Amplitude variability, also known as vertical variability, describes the variation in the vertical direction (i.e., magnitude of stresses). The uncertainty in both components of variability is quantified using statistical models in principal component space. Statistical confidence/tolerance bounds are constructed using statistical bootstrap (i.e., resampling) techniques applied to these models. These bounds offer a succinct quantification of the uncertainty in both the predictions and measurements as well as a method to quantitatively compare the two. Major findings show that the level of uncertainty among measurements is comparable to that among predictions and further experimental work is recommended to inform a validation effort for prediction models.

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Magradey, John W., Christopher C. Daniels y Heather A. Oravec. "Leak Rate Uncertainty Parametric Study". En ASME 2017 Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/fedsm2017-69075.

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The desire of humans to explore beyond low-Earth orbit has provided motivation for technology development to reliably confine gases. The new technologies have driven the need to accurately quantify the mass flow rate past gas pressure seals. While the classic method of helium leak detection is widely accepted and reliable, the method has weaknesses when applied to determining air leak rates. The newest advance in air leak rate quantification acts as an extension of the pressure decay method. In this enhanced method, the downstream pressure is controlled and a constant pressure differential is maintained across the seal under test. The effect of the enhancement is improved measurement uncertainty of the leak rate over the basic pressure decay method which may chase a valid measurement as the differential pressure changes with uncontrolled downstream pressure. Theoretically, each measurement system should produce the same value for a given leak rate; however, the measurement uncertainty is dependent upon the instruments used. As this enhanced method can be accomplished using off-the-shelf equipment and instrumentation, the measurement uncertainty strongly depends upon each unique test set-up. A parametric study of the leak rate uncertainty equation was completed, and the effect of changing the quality and quantity of the pressure and temperature measurement instruments was observed. The study quantified the differences in measurement uncertainty at different test conditions depending upon the instrument choices, as well as, the differences made in uncertainty by modifying the test conditions alone. The calculated measurement uncertainty was beneficially lower when using a single high-quality pressure measurement device when compared to utilizing multiple low-quality pressure transducers. Similar results were shown for temperature measurement devices. The data acquisition sampling rate, initial pressure, and mass flow rate also affected the uncertainty values in some cases, but may not be variable parameters.

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Montomoli, Francesco y Michela Massini. "Gas Turbines and Uncertainty Quantification: Impact of PDF Tails on UQ Predictions, the Black Swan". En ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94306.

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In the last five years Uncertainty Quantification (UQ) techniques became popular to predict gas turbine performances. Taking into account the uncertainties in the input parameters it is possible to evaluate the impact of random variations and to overcome the limitations of deterministic studies. These methods, that only recently have been widely used in computational fluid dynamics, have some limitations that must be considered. One of the most important limitations is that these models cannot predict a “Black Swan” (BS) event. In probability a Black Swan is an event rare, possible and with serious consequences. A reliable design requires a correct evaluation of the probabilities of occurrence of the Black Swan that could strongly affect the life of the turbine. Black Swans are generated by the variability of the input parameters in the “tail” of the statistical distributions. Being far from the mean value design geometry/condition, these events have a low probability of occurrence. In this paper is shown that the use of the Gaussian distribution for the input parameters could strongly underestimate the probability of occurrence of a Black Swan event. Despite that most of the models used in UQ for aerodesign are neglecting the problem. As an example of Black Swan, the hot gas ingestion across a stator is analysed. The gaps have been assumed to be affected by uncertainty with a variation of +/-50% of the nominal value. By using a Monte Carlo simulation with 108 realizations and a Gauss distribution as input, the configuration is initially considered reliable. The six sigma criterion is also satisfied and the probability to have a failure is only 2.54 10−4%. However if a “fat tail” for the input distribution is used instead, the probability to have hot gas ingestion becomes 2.33%, 104 times higher. Most of the methods used in literature aim to have an accurate reproduction of the PDF moments such as mean, standard deviation, skew and kurtosis. However the “tail” of the distribution affects the gas turbine life and must be considered. In particular “fat tails”, the mathematical origin of Black Swans events, can have serious consequences, but in modern stochastic models used for computational fluid dynamics they are not accounted for.

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Durocher, Antoine, Philippe Versailles, Gilles Bourque y Jeffrey M. Bergthorson. "Uncertainty Quantification of NOx Emissions Induced Through the Prompt Route in Premixed Alkane Flames". En ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-75579.

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Increasingly stringent regulations on emissions in the gas turbine industry require novel designs to minimize the environmental impact of oxides of nitrogen (NOx). The development of advanced low-NOx technologies depends on accurate and reliable thermochemical mechanisms to achieve emissions targets. However, current combustion models have high levels of uncertainty in kinetic rates that, when propagated through calculations, yield significant variations in predictions. A recent study identified and optimized nine elementary reactions involved in CH formation to accurately capture its concentration and improve prompt-NO predictions. The current work quantifies the uncertainty on peak CH concentration and NOx emissions generated by these nine reaction rates only, when propagated through the San Diego mechanism. Various non-intrusive spectral methods are used to study atmospheric alkane-air flames. 1st- and 2nd-order total-order expansions and tensor-product expansions are compared against a reference Monte Carlo analysis to assess the ability of the different techniques to accurately quantify the effect of uncertainties on the quantities of interest. Sparse grids, subsets of the full tensor-product expansion, are shown to retain the advantages of tensor formulation compared to total-order expansions while requiring significantly fewer collocation points to develop a surrogate model. The high resolution per dimension can capture complex probability distributions witnessed in radical species concentrations. The uncertainty analysis of lean to rich flames demonstrated a high variability in NOx predictions reaching up to 400 % of nominal predictions. Wider concentration intervals were observed in rich conditions where prompt-NOx is the dominant contributor to emissions. The high variability and scale of uncertainty in NOx emissions originating from these nine elementary reactions demonstrate the need for future experiments and data assimilation to constrain current models to accurately capture CH for robust NOx emissions predictions.

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Romei, A., R. Maffulli, C. Garcia Sanchez y S. Lavagnoli. "Design and Optimization of Multi-Stage Centrifugal Compressors With Uncertainty Quantification of Off Design Performance". En ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-63770.

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The use of multi-stage centrifugal compressors carries out a leading role in oil and gas process applications. Green operation and market competitiveness require the use of low-cost reliable compression units with high efficiencies and wide operating range. A methodology is presented for the design optimization of multi-stage centrifugal compressors with prediction of the compressor map and estimation of the uncertainty limits. A one-dimensional (1D) design tool has been developed that automatically generates a multi-stage radial compressor satisfying the target machine requirements based on a few input parameters. The compressor performance map is then assessed using the method proposed by Casey-Robinson [1], and the approach developed by Al-Busaidi-Pilidis [2]. The off-design performance method relies on empirical correlations calibrated on the performance maps of many single-stage centrifugal compressors. An uncertainty quantification study on the predicted performance maps was conducted using Monte Carlo method (MCM) and generalized Polynomial Chaos Expansion (gPCE). Finally, the design procedure has been coupled to an in-house optimizer based on evolutionary algorithms. The complete design procedure has been applied to a multi-stage industrial compressor test case. A multi-objective optimization of a multi-stage industrial compressor has been performed targeting maximum compressor efficiency and flow range. The results of the optimization show the existence of optimum compressor architectures and how the Pareto fronts evolve depending on the number of stages and shafts.

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Informes sobre el tema "Reliable quantification of uncertainty"

Croft, Stephen y Andrew Nicholson. OR14-V-Uncertainty-PD2La Uncertainty Quantification Workshop Report. Office of Scientific and Technical Information (OSTI), julio de 2017. http://dx.doi.org/10.2172/1784220.

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Urban, Nathan Mark. Climate Uncertainty Quantification at LANL. Office of Scientific and Technical Information (OSTI), abril de 2016. http://dx.doi.org/10.2172/1250690.

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Stracuzzi, David, Maximillian Chen, Michael Darling, Matthew Peterson y Charlie Vollmer. Uncertainty Quantification for Machine Learning. Office of Scientific and Technical Information (OSTI), junio de 2017. http://dx.doi.org/10.2172/1733262.

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Thiagarajan, J. Uncertainty Quantification in Scientific ML. Office of Scientific and Technical Information (OSTI), septiembre de 2020. http://dx.doi.org/10.2172/1670557.

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Karpius, Peter. Nuclide Identification, Quantification, and Uncertainty. Office of Scientific and Technical Information (OSTI), mayo de 2021. http://dx.doi.org/10.2172/1782632.

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Seifried, Jeffrey E. Adjoint-Based Uncertainty Quantification with MCNP. Office of Scientific and Technical Information (OSTI), septiembre de 2011. http://dx.doi.org/10.2172/1110395.

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Srinivasan, Gowri. Need for Uncertainty Quantification in Predictions. Office of Scientific and Technical Information (OSTI), julio de 2015. http://dx.doi.org/10.2172/1191117.

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Le MaÒitre, Olivier P., Matthew T. Reagan, Omar M. Knio, Roger Georges Ghanem y Habib N. Najm. Uncertainty quantification in reacting flow modeling. Office of Scientific and Technical Information (OSTI), octubre de 2003. http://dx.doi.org/10.2172/918251.

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Nadiga, Balasubramanya T. y Emilio Baglietto. Uncertainty Quantification of Multi-Phase Closures. Office of Scientific and Technical Information (OSTI), octubre de 2017. http://dx.doi.org/10.2172/1406195.

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Williams, Mark L. Whitepaper on Uncertainty Quantification for MPACT. Office of Scientific and Technical Information (OSTI), diciembre de 2015. http://dx.doi.org/10.2172/1255677.

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