Experimental Uncertainty Analysis A Textbook For Science And Engineering Students

Uncertainties are inevitable in any experimental measurement. Therefore, it is essential for science and engineering graduates to design and develop reliable experiments and estimate the uncertainty in the measurements. This book describes the methods and application of uncertainty analysis during the planning, data analysis, and reporting stages of an experiment. This book is aimed at postgraduate and advanced undergraduate students of various branches of science and engineering. The book teaches methods for estimating random and systematic uncertainties and combining them to determine the overall uncertainty in a measurement. In addition, the method for propagating measurement uncertainties in the calculated result is discussed. The book also discusses methods of reducing the uncertainties through proper instrumentation, data acquisition, and experiment planning. This book provides detailed background and assumptions underlying the uncertainty analysis techniques for the reader to understand their applicability. Various solved examples are provided to demonstrate the application of the uncertainty analysis techniques. The exercises at the end of the chapters have been chosen carefully to reinforce the concepts discussed in the text.

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.

This text for an undergraduate junior or senior course covers the most common elements necessary to design, execute, analyze, and document an engineering experiment or measurement system and to specify instrumentation for a production process. In addition to descriptions of common measurement systems, the text covers computerized data acquisition systems, common statistical techniques, experimental uncertainty analysis, and guidelines for planning and documenting experiments. The authors are affiliated with the school of engineering at San Francisco State University. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com)

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.

An overview of experimental methods providing practical advice to students seeking guidance with their experimental work.

"This would be an excellent book for undergraduate, graduate and beyond....The style of writing is easy to read and the author does a good job of adding humor in places. The integration of basic programming in R with the data that is collected for any experiment provides a powerful platform for analysis of data.... having the understanding of data analysis that this book offers will really help researchers examine their data and consider its value from multiple perspectives – and this applies to people who have small AND large data sets alike! This book also helps people use a free and basic software system for processing and plotting simple to complex functions." Michelle Pantoya, Texas Tech University Measurements of quantities that vary in a continuous fashion, e.g., the pressure of a gas, cannot be measured exactly and there will always be some uncertainty with these measured values, so it is vital for researchers to be able to quantify this data. Uncertainty Analysis of Experimental Data with R covers methods for evaluation of uncertainties in experimental data, as well as predictions made using these data, with implementation in R. The books discusses both basic and more complex methods including linear regression, nonlinear regression, and kernel smoothing curve fits, as well as Taylor Series, Monte Carlo and Bayesian approaches. Features: 1. Extensive use of modern open source software (R). 2. Many code examples are provided. 3. The uncertainty analyses conform to accepted professional standards (ASME). 4. The book is self-contained and includes all necessary material including chapters on statistics and programming in R. Benjamin D. Shaw is a professor in the Mechanical and Aerospace Engineering Department at the University of California, Davis. His research interests are primarily in experimental and theoretical aspects of combustion. Along with other courses, he has taught undergraduate and graduate courses on engineering experimentation and uncertainty analysis. He has published widely in archival journals and became an ASME Fellow in 2003.

An introduction to error analysis for first degree students who do experimental work in science and engineering courses.

Presenting the fundamental tools of experimentation that are currently used by engineers and scientists, Measurement and Data Analysis for Engineering and Science, Second Edition covers the basics of experimentation, hardware of experiments, and methods of data analysis. It also offers historical perspectives throughout. Updating and reorganizing its popular predecessor, this second edition makes the text much easier to follow and enhances the presentation with electronic material. New to the Second Edition Order of chapters now reflects the sequence of topics usually included in an undergraduate course Asterisked sections denote material not typically covered formally during lecture in an introductory undergraduate course More than 150 new problems, bringing the total to over 420 problems Supplementary website that provides unit conversions, learning objectives, review crossword puzzles and solutions, differential equation derivations, laboratory exercise descriptions, MATLAB® sidebars with M-files, and homework data files Thorough and up to date, this edition continues to help students gain a fundamental understanding of the tools of experimentation. It discusses basic concepts related to experiments, measurement system components and responses, data analysis, and effective communication of experimental findings. Ancillary materials for instructors are available on a CD-ROM and a solutions manual is available for qualifying instructors. More data available on www.nd.edu/~pdunn/www.text/measurements.html