The Uncertainty Analysis of Model Results

The Uncertainty Analysis of Model Results PDF

Author: Eduard Hofer

Publisher: Springer

Published: 2018-07-01

Total Pages: 346

ISBN-13: 9783319762968

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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.

Uncertainty Analysis for Engineers and Scientists

Uncertainty Analysis for Engineers and Scientists PDF

Author: Faith A. Morrison

Publisher: Cambridge University Press

Published: 2021-01-07

Total Pages: 389

ISBN-13: 1108478352

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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.

Experimentation, Validation, and Uncertainty Analysis for Engineers

Experimentation, Validation, and Uncertainty Analysis for Engineers PDF

Author: Hugh W. Coleman

Publisher: John Wiley & Sons

Published: 2018-04-09

Total Pages: 420

ISBN-13: 1119417708

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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.

Uncertainty

Uncertainty PDF

Author: Millett Granger Morgan

Publisher: Cambridge University Press

Published: 1990-08-31

Total Pages: 376

ISBN-13: 113993581X

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The authors explain the ways in which uncertainty is an important factor in the problems of risk and policy analysis. This book outlines the source and nature of uncertainty, discusses techniques for obtaining and using expert judgment, and reviews a variety of simple and advanced methods for analyzing uncertainty.

Probability Methods for Cost Uncertainty Analysis

Probability Methods for Cost Uncertainty Analysis PDF

Author: Paul R. Garvey

Publisher: CRC Press

Published: 2016-01-06

Total Pages: 526

ISBN-13: 148221976X

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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

Scaling and Uncertainty Analysis in Ecology

Scaling and Uncertainty Analysis in Ecology PDF

Author: Jianguo Wu

Publisher: Springer Science & Business Media

Published: 2006-07-02

Total Pages: 354

ISBN-13: 1402046634

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This is the first book of its kind – explicitly considering uncertainty and error analysis as an integral part of scaling. The book draws together a series of important case studies to provide a comprehensive review and synthesis of the most recent concepts, theories and methods in scaling and uncertainty analysis. It includes case studies illustrating how scaling and uncertainty analysis are being conducted in ecology and environmental science.

Uncertainty Analysis with High Dimensional Dependence Modelling

Uncertainty Analysis with High Dimensional Dependence Modelling PDF

Author: Dorota Kurowicka

Publisher: John Wiley & Sons

Published: 2006-10-02

Total Pages: 302

ISBN-13: 0470863080

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Mathematical models are used to simulate complex real-world phenomena in many areas of science and technology. Large complex models typically require inputs whose values are not known with certainty. Uncertainty analysis aims to quantify the overall uncertainty within a model, in order to support problem owners in model-based decision-making. In recent years there has been an explosion of interest in uncertainty analysis. Uncertainty and dependence elicitation, dependence modelling, model inference, efficient sampling, screening and sensitivity analysis, and probabilistic inversion are among the active research areas. This text provides both the mathematical foundations and practical applications in this rapidly expanding area, including: An up-to-date, comprehensive overview of the foundations and applications of uncertainty analysis. All the key topics, including uncertainty elicitation, dependence modelling, sensitivity analysis and probabilistic inversion. Numerous worked examples and applications. Workbook problems, enabling use for teaching. Software support for the examples, using UNICORN - a Windows-based uncertainty modelling package developed by the authors. A website featuring a version of the UNICORN software tailored specifically for the book, as well as computer programs and data sets to support the examples. Uncertainty Analysis with High Dimensional Dependence Modelling offers a comprehensive exploration of a new emerging field. It will prove an invaluable text for researches, practitioners and graduate students in areas ranging from statistics and engineering to reliability and environmetrics.

Measurement Uncertainty

Measurement Uncertainty PDF

Author: Ronald H. Dieck

Publisher: ISA

Published: 2007

Total Pages: 292

ISBN-13: 9781556179150

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Literally an entire course between two covers, Measurement Uncertainty: Methods and Applications, Fourth Edition, presents engineering students with a comprehensive tutorial of measurement uncertainty methods in a logically categorized and readily utilized format. The new uncertainty technologies embodied in both U.S. and international standards have been incorporated into this text with a view toward understanding the strengths and weaknesses of both. The book is designed to also serve as a practical desk reference in situations that commonly confront an experimenter. The text presents the basics of the measurement uncertainty model, non-symmetrical systematic standard uncertainties, random standard uncertainties, the use of correlation, curve-fitting problems, and probability plotting, combining results from different test methods, calibration errors, and uncertainty propagation for both independent and dependent error sources. The author draws on years of experience in industry to direct special attention to the problem of developing confidence in uncertainty analysis results and using measurement uncertainty to select instrumentation systems.