R. Russell Rhinehart's Books

Applied Engineering Statistics

By: R.Russell Rhinehart

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Applied Engineering Statistics

By: R.Russell Rhinehart

Originally published in 1991. Textbook on the understanding and application of statistical procedures to engineering problems, for practicing engineers who once had an introductory course in statistics, but haven't used the techniques in a long time.

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Page Count

444

Publisher

Routledge

Published Date

ISBN 10

1351466097

ISBN 13

9781351466097

Engineering Optimization

Applications, Methods and Analysis

By: R. Russell Rhinehart

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Engineering Optimization

Applications, Methods and Analysis

By: R. Russell Rhinehart

An Application-Oriented Introduction to Essential Optimization Concepts and Best Practices Optimization is an inherent human tendency that gained new life after the advent of calculus; now, as the world grows increasingly reliant on complex systems, optimization has become both more important and more challenging than ever before. Engineering Optimization provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout each stage of the optimization process. Although essential algorithms are explained in detail, the focus lies more in the human function: how to create an appropriate objective function, choose decision variables, identify and incorporate constraints, define convergence, and other critical issues that define the success or failure of an optimization project. Examples, exercises, and homework throughout reinforce the author’s “do, not study” approach to learning, underscoring the application-oriented discussion that provides a deep, generic understanding of the optimization process that can be applied to any field. Providing excellent reference for students or professionals, Engineering Optimization: Describes and develops a variety of algorithms, including gradient based (such as Newton’s, and Levenberg-Marquardt), direct search (such as Hooke-Jeeves, Leapfrogging, and Particle Swarm), along with surrogate functions for surface characterization Provides guidance on optimizer choice by application, and explains how to determine appropriate optimizer parameter values Details current best practices for critical stages of specifying an optimization procedure, including decision variables, defining constraints, and relationship modeling Provides access to software and Visual Basic macros for Excel on the companion website, along with solutions to examples presented in the book Clear explanations, explicit equation derivations, and practical examples make this book ideal for use as part of a class or self-study, assuming a basic understanding of statistics, calculus, computer programming, and engineering models. Anyone seeking best practices for “making the best choices” will find value in this introductory resource.

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Page Count

773

Publisher

John Wiley & Sons

Published Date

ISBN 10

1118936310

ISBN 13

9781118936313

Nonlinear Regression Modeling for Engineering Applications

Modeling, Model Validation, and Enabling Design of Experiments

By: R. Russell Rhinehart

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Nonlinear Regression Modeling for Engineering Applications

Modeling, Model Validation, and Enabling Design of Experiments

By: R. Russell Rhinehart

Since mathematical models express our understanding of how nature behaves, we use them to validate our understanding of the fundamentals about systems (which could be processes, equipment, procedures, devices, or products). Also, when validated, the model is useful for engineering applications related to diagnosis, design, and optimization. First, we postulate a mechanism, then derive a model grounded in that mechanistic understanding. If the model does not fit the data, our understanding of the mechanism was wrong or incomplete. Patterns in the residuals can guide model improvement. Alternately, when the model fits the data, our understanding is sufficient and confidently functional for engineering applications. This book details methods of nonlinear regression, computational algorithms,model validation, interpretation of residuals, and useful experimental design. The focus is on practical applications, with relevant methods supported by fundamental analysis. This book will assist either the academic or industrial practitioner to properly classify the system, choose between the various available modeling options and regression objectives, design experiments to obtain data capturing critical system behaviors, fit the model parameters based on that data, and statistically characterize the resulting model. The author has used the material in the undergraduate unit operations lab course and in advanced control applications.

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Page Count

400

Publisher

John Wiley & Sons

Published Date

ISBN 10

1118597931

ISBN 13

9781118597934

Book's by R. Russell Rhinehart

Applied Engineering Statistics

Applied Engineering Statistics

Engineering Optimization

Engineering Optimization

Nonlinear Regression Modeling for Engineering Applications

Nonlinear Regression Modeling for Engineering Applications

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