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Design and Optimization of Energy Systems

Design and Optimization of Energy Systems. Instructor: Prof. C. Balaji, Department of Mechanical Engineering, IIT Madras. This course provides an introduction to design and optimization of energy systems. Topics covered in this course include introduction to system design; regression analysis and curve fitting; modeling of thermal equipment; system simulation (successive substitution, Newton-Raphson method); Lagrange multipliers, search methods, linear programming, geometric programming; simulated annealing, genetic algorithms; examples applied to heat transfer problems and energy systems such as gas and steam power plants, refrigeration systems, heat pumps and so on. (from nptel.ac.in)

Lecture 20 - Nonlinear Regression (Gauss-Newton Algorithm)

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Lecture 01 - Introduction to Optimization

Lecture 02 - System Design and Analysis

Lecture 03 - Workable System

Lecture 04 - System Simulation

Lecture 05 - Introduction to Flow Diagrams

Lecture 06 - Successive Substitution Method

Lecture 07 - Successive Substitution Method (cont.)

Lecture 08 - Successive Substitution Method and Newton-Raphson Method

Lecture 09 - Newton-Raphson Method (cont.)

Lecture 10 - Convergence Characteristics of Newton-Raphson Method

Lecture 11 - Newton-Raphson Method for Multiple Variables

Lecture 12 - Solution of System of Linear Equations

Lecture 13 - Introduction to Curve Fitting

Lecture 14 - Example for Lagrange Interpolation

Lecture 15 - Lagrange Interpolation (cont.)

Lecture 16 - Best Fit

Lecture 17 - Least Square Regression

Lecture 18 - Least Square Regression (cont.)

Lecture 19 - Least Square Regression (cont.)

Lecture 20 - Nonlinear Regression (Gauss-Newton Algorithm)

Lecture 21 - Optimization: Basic Ideas

Lecture 22 - Properties of Objective Function and Cardinal Ideas in Optimization

Lecture 23 - Unconstrained Optimization

Lecture 24 - Constrained Optimization Problems

Lecture 25 - Mathematical Proof of the Lagrange Multiplier Method

Lecture 26 - Test for Maxima/Minima

Lecture 27 - Handling Inequality Constraints

Lecture 28 - Kuhn-Tucker Conditions

Lecture 29 - Unimodal Function and Search Methods

Lecture 30 - Dichotomous Search

Lecture 31 - Fibonacci Search Method

Lecture 32 - Reduction Ratio of Fibonacci Search Method

Lecture 33 - Introduction to Multivariable Optimization

Lecture 34 - The Conjugate Gradient Method

Lecture 35 - The Conjugate Gradient Method (cont.)

Lecture 36 - Linear Programming

Lecture 37 - Dynamic Programming

Lecture 38 - Genetic Algorithms

Lecture 39 - Genetic Algorithms (cont.)

Lecture 40 - Simulated Annealing and Summary