Experimentation, Validation, and Uncertainty Analysis for Engineers

When using numerical simulation to make a decision, how can its reliability be determined? What are the common pitfalls and mistakes when assessing the trustworthiness of computed information, and how can they be avoided? Whenever numerical simulation is employed in connection with engineering decision-making, there is an implied expectation of reliability: one cannot base decisions on computed information without believing that information is reliable enough to support those decisions. Using mathematical models to show the reliability of computer-generated information is an essential part of any modelling effort. Giving users of finite element analysis (FEA) software an introduction to verification and validation procedures, this book thoroughly covers the fundamentals of assuring reliability in numerical simulation. The renowned authors systematically guide readers through the basic theory and algorithmic structure of the finite element method, using helpful examples and exercises throughout. Delivers the tools needed to have a working knowledge of the finite element method Illustrates the concepts and procedures of verification and validation Explains the process of conceptualization supported by virtual experimentation Describes the convergence characteristics of the h-, p- and hp-methods Covers the hierarchic view of mathematical models and finite element spaces Uses examples and exercises which illustrate the techniques and procedures of quality assurance Ideal for mechanical and structural engineering students, practicing engineers and applied mathematicians Includes parameter-controlled examples of solved problems in a companion website (www.wiley.com/go/szabo)

The aim of this book is to motivate students into learning Machine Analysis by reinforcing theory and applications throughout the text. The author uses an enthusiastic ‘hands-on’ approach by including photos of actual mechanisms in place of abstract line illustrations, and directs students towards developing their own software for mechanism analysis using Excel & Matlab. An accompanying website includes a detailed list of tips for learning machine analysis, including tips on working homework problems, note taking, preparing for tests, computer programming and other topics to aid in student success. Study guides for each chapter that focus on teaching the thought process needed to solve problems by presenting practice problems are included, as are computer animations for common mechanisms discussed in the text.

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.

Safety and Health for Engineers, 3rd Edition, addresses the fundamentals of safety, legal aspects, hazard recognition and control, and techniques for managing safety decisions, as well as: Completely revises and updates all 38 chapters in the book New edition adds more than 110 stories and cases from practice to illustrate various topics or issues New topics on adapting to new safety concerns that arise from technology innovations; convergence of safety, health and environmental departments in many organizations; the concept of prevention through design; and emphasis on safety management systems and risk management and analysis Includes learning exercises and computational examples based on real world situations along with in-depth references for each chapter Includes a detailed solutions manual for academic adopters Covers the primary topics included in certification exams for professional safety, such as CSP/ASP

Deal with information and uncertainty properly and efficiently using tools emerging from generalized information theory Uncertainty and Information: Foundations of Generalized Information Theory contains comprehensive and up-to-date coverage of results that have emerged from a research program begun by the author in the early 1990s under the name «generalized information theory» (GIT). This ongoing research program aims to develop a formal mathematical treatment of the interrelated concepts of uncertainty and information in all their varieties. In GIT, as in classical information theory, uncertainty (predictive, retrodictive, diagnostic, prescriptive, and the like) is viewed as a manifestation of information deficiency, while information is viewed as anything capable of reducing the uncertainty. A broad conceptual framework for GIT is obtained by expanding the formalized language of classical set theory to include more expressive formalized languages based on fuzzy sets of various types, and by expanding classical theory of additive measures to include more expressive non-additive measures of various types. This landmark book examines each of several theories for dealing with particular types of uncertainty at the following four levels: * Mathematical formalization of the conceived type of uncertainty * Calculus for manipulating this particular type of uncertainty * Justifiable ways of measuring the amount of uncertainty in any situation formalizable in the theory * Methodological aspects of the theory With extensive use of examples and illustrations to clarify complex material and demonstrate practical applications, generous historical and bibliographical notes, end-of-chapter exercises to test readers' newfound knowledge, glossaries, and an Instructor's Manual, this is an excellent graduate-level textbook, as well as an outstanding reference for researchers and practitioners who deal with the various problems involving uncertainty and information. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Science is a quest for certainty, but lack of certainty is the driving force behind all of its endeavors. This book, specifically, examines the uncertainty of technological and industrial science. Uncertainty and Mechanics studies the concepts of mechanical design in an uncertain setting and explains engineering techniques for inventing cost-effective products. Though it references practical applications, this is a book about ideas and potential advances in mechanical science.

The modern drug developers? guide for making informed choices among the diverse target identification methods Target Discovery and Validation: Methods and Strategies for Drug Discovery offers a hands-on review of the modern technologies for drug target identification and validation. With contributions from noted industry and academic experts, the book addresses the most recent chemical, biological, and computational methods. Additionally, the book highlights techologies that are applicable to ?difficult? targets and drugs directed at multiple targets, including chemoproteomics, activity-based protein profiling, pathway mapping, genome-wide association studies, and array-based profiling. Throughout, the authors highlight a range of diverse approaches, and target validation studies reveal how these methods can support academic and drug discovery scientists in their target discovery and validation research. This resource: -Offers a guide to identifying and validating targets, a key enabling technology without which no new drug development is possible -Presents the information needed for choosing the appropriate assay method from the ever-growing range of available options -Provides practical examples from recent drug development projects, e. g. in kinase inhibitor profiling Written for medicinal chemists, pharmaceutical professionals, biochemists, biotechnology professionals, and pharmaceutical chemists, Target Discovery and Validation explores the current methods for the identification and validation of drug targets in one comrpehensive volume. It also includes numerous practical examples.

Learn to fully harness the power of Microsoft Excel® to perform scientific and engineering calculations With this text as your guide, you can significantly enhance Microsoft Excel's® capabilities to execute the calculations needed to solve a variety of chemical, biochemical, physical, engineering, biological, and medicinal problems. The text begins with two chapters that introduce you to Excel's Visual Basic for Applications (VBA) programming language, which allows you to expand Excel's® capabilities, although you can still use the text without learning VBA. Following the author's step-by-step instructions, here are just a few of the calculations you learn to perform: Use worksheet functions to work with matrices Find roots of equations and solve systems of simultaneous equations Solve ordinary differential equations and partial differential equations Perform linear and non-linear regression Use random numbers and the Monte Carlo method This text is loaded with examples ranging from very basic to highly sophisticated solutions. More than 100 end-of-chapter problems help you test and put your knowledge to practice solving real-world problems. Answers and explanatory notes for most of the problems are provided in an appendix. The CD-ROM that accompanies this text provides several useful features: All the spreadsheets, charts, and VBA code needed to perform the examples from the text Solutions to most of the end-of-chapter problems An add-in workbook with more than twenty custom functions This text does not require any background in programming, so it is suitable for both undergraduate and graduate courses. Moreover, practitioners in science and engineering will find that this guide saves hours of time by enabling them to perform most of their calculations with one familiar spreadsheet package

A practical approach to enhancing quality in software models using UML Version 2.0 «Despite its increasing usage, many companies are not taking the best advantage of UML and, occasionally, individuals have experienced frustration in applying its standards. Perhaps this is because they have not yet read this book!» -From the Foreword by Prof. Brian Henderson-Sellers This book presents a practical checklist approach to enhancing the quality of software models created with the Unified Modeling Language (UML) Version 2.0. The foundation for quality is set by the discussion on the nature and creation of UML models. This is followed by a demonstration of how to apply verification and validation checks to these models with three foci: syntactical correctness, semantic meaningfulness, and aesthetic symmetry. The quality work is carried out within three distinct yet related modeling spaces: * Model of problem space (MOPS) * Model of solution space (MOSS) * Model of background space (MOBS) Readers can then choose a specific quality approach according to their roles in their projects. Verification and validation checks are also organized according to these three modeling spaces, making it easier for the reader to focus on the appropriate diagrams and quality checks corresponding to their modeling space. In addition, a major element of this publication is the Strengths, Weaknesses, Objectives, and Traps (SWOT) analysis. This analysis is performed on each UML diagram, enabling readers to fully comprehend these diagrams, their advantages and limitations, and the way in which they can be used in practical projects for modeling. A consistent case study of the Lucky Insurance System is provided throughout the chapters to illustrate the creation of good quality UML diagrams, followed by application of quality checks to them. With its emphasis on quality in UML-based projects, this book is an essential resource for all quality professionals, including quality analysts, process consultants, quality managers, test designers, and testers.

Expanded coverage of essential math, including integral equations, calculus of variations, tensor analysis, and special integrals Math Refresher for Scientists and Engineers, Third Edition is specifically designed as a self-study guide to help busy professionals and students in science and engineering quickly refresh and improve the math skills needed to perform their jobs and advance their careers. The book focuses on practical applications and exercises that readers are likely to face in their professional environments. All the basic math skills needed to manage contemporary technology problems are addressed and presented in a clear, lucid style that readers familiar with previous editions have come to appreciate and value. The book begins with basic concepts in college algebra and trigonometry, and then moves on to explore more advanced concepts in calculus, linear algebra (including matrices), differential equations, probability, and statistics. This Third Edition has been greatly expanded to reflect the needs of today's professionals. New material includes: * A chapter on integral equations * A chapter on calculus of variations * A chapter on tensor analysis * A section on time series * A section on partial fractions * Many new exercises and solutions Collectively, the chapters teach most of the basic math skills needed by scientists and engineers. The wide range of topics covered in one title is unique. All chapters provide a review of important principles and methods. Examples, exercises, and applications are used liberally throughout to engage the readers and assist them in applying their new math skills to actual problems. Solutions to exercises are provided in an appendix. Whether to brush up on professional skills or prepare for exams, readers will find this self-study guide enables them to quickly master the math they need. It can additionally be used as a textbook for advanced-level undergraduates in physics and engineering.