Feb 1, 2006 - reservoir thickness at the Geysers. At Geysers the deepest MEQ responses ..... Geothermal Workshop, in New Zealand, 1998. F. Friedmann, A.
Uncertainty Quantification for Stochastic Subspace Identification on. Multi-Setup Measurements. Michael Döhler, Xuan-Bi
Feb 1, 2006 - and east sides of the field (Figure 3). The north eruption center with a distinct drainage pattern represents the Malabar volcanic complex and.
finite element model of the structure. Damage localization using both finite element information and modal parameters es
Aug 6, 2014 - [3, 6, 4]. While the classical Cauchy problem for solving PDEs is to find the semigroup of the. â Department of Mathematics ... DS] 6 Aug 2014 ...
Nov 22, 2010 - 1. INTRODUCTION. Integrated Gasification Combined Cycle (IGCC) system is a critical component of future sustainable energy alternatives.1-4.
Jan 4, 2016 - Reduction for a Shock Tube Simulation. Chanyoung Park1, M. Giselle Fernández-Godino2, Nam-Ho Kim3 and Raphael T. Haftka4. Department ...
Not only is it important to quantify uncertainty but it is also important to establish a confidence interval for the simulation-based predictions or design (cf. [6]).
Panels. (a)-(d) correspond to Tc, Ïc, Pc, and Zc respectively. The probability densities are defined as the ...... In Figures S.22-S.26 we see that indeed the numerical uncertainty can be quite ...... Industrial & Engineering Chemistry Research,.
We present an uncertainty and sensitivity analysis applied to a thermo-chemical model of radiative heat loads on a capsule entering the Titan atmosphere.
Experimental data for an underexpanded sonic air jet injected into a M = 1.6 .... The RANS simulations were performed using the JOE flow solver (Pecnik et al.
Shutdown cost of thermal unit j in period k cp j,k ... M. Rocklin is with the Department of Computer Science, University of ... University, New York, NY 10012, USA.
Safety analysis in nuclear engineering has been adopting a best estimate plus ... Cooling System (ECCS) performance analysis for Light Water Reactors (LWRs) ...
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Statistical Parameter Estimation and Uncertainty Quantification for. Macro Fiber Composite Actuators Operating in Nonlinear and. Hysteretic Regimes.
Jun 17, 2018 - USA, [email protected], [email protected] b. Department of ... void fraction predictive capability of nuclear reactor system thermal-hydraulics code ..... Regulatory Research, U. S. Nuclear Regulatory Commis- sion, Washington ...
million liters for the travel hotel over a 13-month monitoring program [6]. In Canada, a survey ... The average water heating energy consumption was 21 ... has a characteristic range of flow rates and durations [12]. ..... This example of random coef
Model verification and validation (V&V) are essential before a model can be implemented in practice. Integrating model V&V into the process of model.
An ensemble 4D seismic history matching framework with sparse representation based on wavelet multiresolution analysis. SPE Journal, 22, 985 - 1,010.
Jan 16, 2015 - double spring-mass system and (iv) a typical section nonlinear aeroelastic model. ... solution on a fixed grid, the solution obtained from the proposed adaptive ... Mailing Address: 865 Asp Ave., Felgar Hall Rm 212A, Nor-.
of the authors of this manuscript.. In this work, the NAFEMS challenge problem is solved by using ..... Figure 5: P-Box for inductance L, CASE-C. NAFEMS World ...
Uncertainty Quantification and Stochastic Modeling ...
Uncertainty Quantification and Stochastic Modeling with Matlab®. Eduardo Souza de Cursi, National Institute for Applied Sciences, Rouen, France. Rubens ...
Uncertainty Quantification and Stochastic Modeling with Matlab® Eduardo Souza de Cursi, National Institute for Applied Sciences, Rouen, France Rubens Sampaio, PUC-Rio, Rio de Janeiro, Brazil ISBN: 978178540058 Publication Date: March 2015 Hardback 456 pp. 185.00 USD
eBooks
Description Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with many real-world applications within statistics, mathematics, probability and engineering, but also within the natural sciences. Literature on the topic has up until now been largely based on polynomial chaos, which raises difficulties when considering different types of approximation and does not lead to a unified presentation of the methods. Moreover, this description does not consider either deterministic problems or infinite dimensional ones. This book gives a unified, practical and comprehensive presentation of the main techniques used for the characterization of the effect of uncertainty on numerical models and on their exploitation in numerical problems. In particular, applications to linear and nonlinear systems of equations, differential equations, optimization and reliability are presented. Applications of stochastic methods to deal with deterministic numerical problems are also discussed. Matlab® illustrates the implementation of these methods and makes the book suitable as a textbook and for self-study. Contents 1. Elements of Probability Theory and Stochastic Processes. 2. Maximum Entropy and Information. 3. Representation of Random Variables. 4. Linear Algebraic Equations Under Uncertainty. 5. Nonlinear Algebraic Equations Involving Random Parameters. 6. Differential Equations Under Uncertainty. 7. Optimization Under Uncertainty. 8. Reliability-Based Optimization. About the Authors Eduardo Souza de Cursi is Professor at the National Institute for Applied Sciences in Rouen, France, where he is also Dean of International Affairs and Director of the Laboratory for the Optimization and Reliability in Structural Mechanics. Rubens Sampaio is Professor at PUC-Rio, Rio de Janeiro, Brazil. Downloads
