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Author:
Publisher:
ISBN:
Category : Engineering
Languages : en
Pages : 84
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Book Description
Author:
Publisher:
ISBN:
Category : Engineering
Languages : en
Pages : 84
Get Book Here
Book Description
Author: Eduard Hofer
Publisher: Springer
ISBN: 9783319762968
Category : Mathematics
Languages : en
Pages : 346
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Book Description
This book is a practical guide to the uncertainty analysis of computer model applications. Used in many areas, such as engineering, ecology and economics, computer models are subject to various uncertainties at the level of model formulations, parameter values and input data. Naturally, it would be advantageous to know the combined effect of these uncertainties on the model results as well as whether the state of knowledge should be improved in order to reduce the uncertainty of the results most effectively. The book supports decision-makers, model developers and users in their argumentation for an uncertainty analysis and assists them in the interpretation of the analysis results.
Author: Faith A. Morrison
Publisher: Cambridge University Press
ISBN: 1108478352
Category : Computers
Languages : en
Pages : 389
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Book Description
Build the skills for determining appropriate error limits for quantities that matter with this essential toolkit. Understand how to handle a complete project and how uncertainty enters into various steps. Provides a systematic, worksheet-based process to determine error limits on measured quantities, and all likely sources of uncertainty are explored, measured or estimated. Features instructions on how to carry out error analysis using Excel and MATLAB®, making previously tedious calculations easy. Whether you are new to the sciences or an experienced engineer, this useful resource provides a practical approach to performing error analysis. Suitable as a text for a junior or senior level laboratory course in aerospace, chemical and mechanical engineering, and for professionals.
Author: Paul R. Garvey
Publisher: CRC Press
ISBN: 148221976X
Category : Mathematics
Languages : en
Pages : 526
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Book Description
Probability Methods for Cost Uncertainty Analysis: A Systems Engineering Perspective, Second Edition gives you a thorough grounding in the analytical methods needed for modeling and measuring uncertainty in the cost of engineering systems. This includes the treatment of correlation between the cost of system elements, how to present the analysis to
Author: National Research Council
Publisher: National Academies Press
ISBN: 030904894X
Category : Science
Languages : en
Pages : 668
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Book Description
The public depends on competent risk assessment from the federal government and the scientific community to grapple with the threat of pollution. When risk reports turn out to be overblownâ€"or when risks are overlookedâ€"public skepticism abounds. This comprehensive and readable book explores how the U.S. Environmental Protection Agency (EPA) can improve its risk assessment practices, with a focus on implementation of the 1990 Clean Air Act Amendments. With a wealth of detailed information, pertinent examples, and revealing analysis, the volume explores the "default option" and other basic concepts. It offers two views of EPA operations: The first examines how EPA currently assesses exposure to hazardous air pollutants, evaluates the toxicity of a substance, and characterizes the risk to the public. The second, more holistic, view explores how EPA can improve in several critical areas of risk assessment by focusing on cross-cutting themes and incorporating more scientific judgment. This comprehensive volume will be important to the EPA and other agencies, risk managers, environmental advocates, scientists, faculty, students, and concerned individuals.
Author: Hugh W. Coleman
Publisher: John Wiley & Sons
ISBN: 1119417708
Category : Technology & Engineering
Languages : en
Pages : 404
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Book Description
Helps engineers and scientists assess and manage uncertainty at all stages of experimentation and validation of simulations Fully updated from its previous edition, Experimentation, Validation, and Uncertainty Analysis for Engineers, Fourth Edition includes expanded coverage and new examples of applying the Monte Carlo Method (MCM) in performing uncertainty analyses. Presenting the current, internationally accepted methodology from ISO, ANSI, and ASME standards for propagating uncertainties using both the MCM and the Taylor Series Method (TSM), it provides a logical approach to experimentation and validation through the application of uncertainty analysis in the planning, design, construction, debugging, execution, data analysis, and reporting phases of experimental and validation programs. It also illustrates how to use a spreadsheet approach to apply the MCM and the TSM, based on the authors’ experience in applying uncertainty analysis in complex, large-scale testing of real engineering systems. Experimentation, Validation, and Uncertainty Analysis for Engineers, Fourth Edition includes examples throughout, contains end of chapter problems, and is accompanied by the authors’ website www.uncertainty-analysis.com. Guides readers through all aspects of experimentation, validation, and uncertainty analysis Emphasizes the use of the Monte Carlo Method in performing uncertainty analysis Includes complete new examples throughout Features workable problems at the end of chapters Experimentation, Validation, and Uncertainty Analysis for Engineers, Fourth Edition is an ideal text and guide for researchers, engineers, and graduate and senior undergraduate students in engineering and science disciplines. Knowledge of the material in this Fourth Edition is a must for those involved in executing or managing experimental programs or validating models and simulations.
Author: Hugh W. Coleman
Publisher: John Wiley & Sons
ISBN: 9780471121466
Category : Psychology
Languages : en
Pages : 298
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Book Description
Now, in the only manual available with direct applications to the design and analysis of engineering experiments, respected authors Hugh Coleman and Glenn Steele have thoroughly updated their bestselling title to include the new methodologies being used by the United States and International standards committee groups.
Author: Sergio Daniel Marques Amaral
Publisher:
ISBN:
Category :
Languages : en
Pages : 175
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Book Description
To support effective decision making, engineers should comprehend and manage various uncertainties throughout the design process. In today's modern systems, quantifying uncertainty can become cumbersome and computationally intractable for one individual or group to manage. This is particularly true for systems comprised of a large number of components. In many cases, these components may be developed by different groups and even run on different computational platforms, making it challenging or even impossible to achieve tight integration of the various models. This thesis presents an approach for overcoming this challenge by establishing a divide-and-conquer methodology, inspired by the decomposition-based approaches used in multidisciplinary analysis and optimization. Specifically, this research focuses on uncertainty analysis, also known as forward propagation of uncertainties, and sensitivity analysis. We present an approach for decomposing the uncertainty analysis task amongst the various components comprising a feed-forward system and synthesizing the local uncertainty analyses into a system uncertainty analysis. Our proposed decomposition-based multicomponent uncertainty analysis approach is shown to converge in distribution to the traditional all-at-once Monte Carlo uncertainty analysis under certain conditions. Our decomposition-based sensitivity analysis approach, which is founded on our decomposition-based uncertainty analysis algorithm, apportions the system output variance among the system inputs. The proposed decomposition-based uncertainty quantification approach is demonstrated on a multidisciplinary gas turbine system and is compared to the traditional all-at-once Monte Carlo uncertainty quantification approach. To extend the decomposition-based uncertainty quantification approach to high dimensions, this thesis proposes a novel optimization formulation to estimate statistics from a target distribution using random samples generated from a (different) proposal distribution. The proposed approach employs the well-defined and determinable empirical distribution function associated with the available samples. The resulting optimization problem is shown to be a single linear equality and box-constrained quadratic program and can be solved efficiently using optimization algorithms that scale well to high dimensions. Under some conditions restricting the class of distribution functions, the solution of the optimization problem yields importance weights that are shown to result in convergence in the Ll-norm of the weighted proposal empirical distribution function to the target distribution function, as the number of samples tends to infinity. Results on a variety of test cases show that the proposed approach performs well in comparison with other well-known approaches. The proposed approaches presented herein are demonstrated on a realistic application; environmental impacts of aviation technologies and operations. The results demonstrate that the decomposition-based uncertainty quantification approach can effectively quantify the uncertainty of a multicomponent system for which the models are housed in different locations and owned by different groups.
Author: Bilal M. Ayyub
Publisher: Springer Science & Business Media
ISBN: 0387235507
Category : Business & Economics
Languages : en
Pages : 547
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Book Description
The application areas of uncertainty are numerous and diverse, including all fields of engineering, computer science, systems control and finance. Determining appropriate ways and methods of dealing with uncertainty has been a constant challenge. The theme for this book is better understanding and the application of uncertainty theories. This book, with invited chapters, deals with the uncertainty phenomena in diverse fields. The book is an outgrowth of the Fourth International Symposium on Uncertainty Modeling and Analysis (ISUMA), which was held at the center of Adult Education, College Park, Maryland, in September 2003. All of the chapters have been carefully edited, following a review process in which the editorial committee scrutinized each chapter. The contents of the book are reported in twenty-three chapters, covering more than . . ... pages. This book is divided into six main sections. Part I (Chapters 1-4) presents the philosophical and theoretical foundation of uncertainty, new computational directions in neural networks, and some theoretical foundation of fuzzy systems. Part I1 (Chapters 5-8) reports on biomedical and chemical engineering applications. The sections looks at noise reduction techniques using hidden Markov models, evaluation of biomedical signals using neural networks, and changes in medical image detection using Markov Random Field and Mean Field theory. One of the chapters reports on optimization in chemical engineering processes.
Author: Bilal M. Ayyub
Publisher: Springer Science & Business Media
ISBN: 146155473X
Category : Computers
Languages : en
Pages : 376
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Book Description
Uncertainty has been of concern to engineers, managers and . scientists for many centuries. In management sciences there have existed definitions of uncertainty in a rather narrow sense since the beginning of this century. In engineering and uncertainty has for a long time been considered as in sciences, however, synonymous with random, stochastic, statistic, or probabilistic. Only since the early sixties views on uncertainty have ~ecome more heterogeneous and more tools to model uncertainty than statistics have been proposed by several scientists. The problem of modeling uncertainty adequately has become more important the more complex systems have become, the faster the scientific and engineering world develops, and the more important, but also more difficult, forecasting of future states of systems have become. The first question one should probably ask is whether uncertainty is a phenomenon, a feature of real world systems, a state of mind or a label for a situation in which a human being wants to make statements about phenomena, i. e. , reality, models, and theories, respectively. One cart also ask whether uncertainty is an objective fact or just a subjective impression which is closely related to individual persons. Whether uncertainty is an objective feature of physical real systems seems to be a philosophical question. This shall not be answered in this volume.
