Thermal system design and optimization / C. Balaji.
2021
TJ265
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Online Access
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Unlimited
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Authorized users
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Can lend chapters, not whole ebooks
Details
Title
Thermal system design and optimization / C. Balaji.
Edition
Second edition.
ISBN
9783030590468 (electronic book)
3030590461 (electronic book)
3030590453
9783030590451
3030590461 (electronic book)
3030590453
9783030590451
Published
Cham, Switzerland : Springer, [2021]
Language
English
Description
1 online resource (xvii, 377 pages) : illustrations (some color)
Other Standard Identifiers
10.1007/978-3-030-59046-8 doi
Call Number
TJ265
Dewey Decimal Classification
621.402/1
Summary
This highly informative and carefully presented textbook introduces the general principles involved in system design and optimization as applicable to thermal systems, followed by the methods to accomplish them. It introduces contemporary techniques like Genetic Algorithms, Simulated Annealing, and Bayesian Inference in the context of optimization of thermal systems. There is a separate chapter devoted to inverse problems in thermal systems. It also contains sections on Integer Programming and Multi-Objective optimization. The linear programming chapter is fortified by a detailed presentation of the Simplex method. A major highlight of the textbook is the inclusion of workable MATLAB codes for examples of key algorithms discussed in the book. Examples in each chapter clarify the concepts and methods presented and end-of-chapter problems supplement the material presented and enhance the learning process.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed March 16, 2021).
Available in Other Form
Print version: 9783030590451
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Online Access
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Online Resources > Ebooks
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Table of Contents
Introduction to Design and System Design
System Simulation
Curve Fitting
Optimization-Basic Ideas and Formulation
Lagrange Multipliers
Search Methods
Linear Programming and Dynamic Programming
Non-traditional Optimization Techniques
Inverse Problems.
System Simulation
Curve Fitting
Optimization-Basic Ideas and Formulation
Lagrange Multipliers
Search Methods
Linear Programming and Dynamic Programming
Non-traditional Optimization Techniques
Inverse Problems.