This compact text is a powerful introduction to the Excel/VBA computing environment. The book presents some of the most useful features of Excel. First by introducing mathematical puzzles that will grab the reader’s attention with the reader invited to think hard on solving those puzzles. Then, solutions are presented in a logical manner. The book goes on to describe modern and up-to-date engineering problems and their solutions. Based on many years of the authors’ teaching, the book provides a practical, useful and enjoyable learning methods for readers to become expert in Excel and its application to engineering.
Author(s): Munner, Tariq; Ivanova, Stoyanka
Year: 2022
Language: English
Commentary: Introduction to the Excel/VBA computing environment, Become an Excel expert for Engineering
Pages: 183
Tags: Introduction to the Excel/VBA computing environment, Become an Excel expert for Engineering
Acknowledgments
Review of an Earlier Book by Prof. T. Muneer
Contents
About the Authors
List of MS Excel/VBA Workbooks
1 Introduction
1.1 Conclusion
2 Games and Puzzles
2.1 Conclusion
Reference
3 Numerical Analysis
3.1 Error Analysis Using Chauvenet’s Technique
3.2 Conclusion
References
4 Computer Graphics in Excel
4.1 Computer Graphics with MS Excel and VBA
4.2 Geometric Calculations with VBA
4.3 Geometric Transformations of Objects in the Plane
4.4 Conclusion
References
5 Dynamics
5.1 Comparison of ICE and BEV
5.2 Regenerative Braking Offered by BEV
5.3 Factors That Affect Energy Consumption of BEV
5.4 Driving Cycles
5.5 The Vehicle Dynamic and Energy Consumption (VEDEC) Simulation Equations
5.6 Supporting Software
5.7 Validation of Simulations
5.8 MS Excel-VBA Functions in VEDEC Simulation Software
5.9 Conclusion
References
6 Thermodynamics
6.1 Conclusion
References
7 Solar Energy
7.1 Conclusion
References
8 Heat Transfer and View Factors
8.1 Heat Transfer—Introduction
8.1.1 Heat Transfer and View Factors
8.1.2 View Factor Algebra
8.2 View Factor Between Two Surfaces with a Common Edge
8.2.1 View Factor from a Sloped Surface with Infinite Width to Infinite Uniform Horizon
8.2.1.1 View Factor from a Sloped Surface with Infinite Width to Infinite Uniform Horizon
8.2.1.2 View Factor from a Sloped Surface with Infinite Width to Finite Uniform Horizon
8.2.2 View Factor from a Sloped Surface with Finite Width to Infinite Uniform Horizon
8.2.2.1 View Factor from a Vertical Surface with Finite Width to Finite Uniform Horizon Estimated with an Analytical Approach
8.2.2.2 View Factor from a Sloped Surface with Finite Width to Finite Uniform Horizon Estimated with an Analytical Approach
8.2.3 View Factor for Generalized Inclined-Rectangle Arrangement
8.2.3.1 View Factor from a Sloped Surface to a Horizontal Surface with the Same Width—Solved with an Analytical Approach
8.2.3.2 View Factor for Generalized Inclined-Rectangle Arrangement, Estimated with an Analytic Approach
8.2.3.3 View Factor for a Generalized Inclined-Rectangle Arrangement, with Finite Element Approach with a Uniform Grid
8.2.3.4 View Factor for Generalized Inclined-Rectangle Arrangement, with Finite Element Approach with Uniform Grid, Combined with Monte Carlo Method
8.3 View Factor Between Two Parallel Surfaces
8.3.1 View Factor Between Two Parallel Surfaces with Infinite Width and Finite Height
8.3.1.1 View Factor Between Two Parallel Surfaces with Infinite Width and Finite Height, Directly Opposite
8.3.1.2 View Factor Between Two Parallel Surfaces with Infinite Width and Different Finite Height
8.3.2 View Factor Between Two Directly Opposite Parallel Rectangles with Equal Size
8.3.3 View Factor for Generalized Parallel Rectangles Arrangement
8.3.3.1 View Factor Between Two Parallel Rectangles in General Arrangement
8.3.3.2 View Factor Between Two Parallel Rectangles in General Arrangement, with Finite Element Approach with a Uniform Grid
8.3.3.3 View Factor Between Two Parallel Rectangles in General Arrangement, with Finite Element Approach with Uniform Grid and Monte Carlo Method
8.4 Conclusion
References
9 Artificial Neural Networks in Excel
9.1 Introduction to Artificial Neural Networks
9.1.1 Principle of ANN Training with Back Propagation
9.1.2 Features of the Use of ANN. Areas of Application
9.2 Neural Network with One Hidden Layer in VBA for Regression Analysis
9.2.1 Preparation of the Complete Neural Network Training Database
9.2.2 Dividing the Complete Neural Network Training Database into Two Parts
9.2.3 Neural Network Training
9.2.4 Improving the Algorithm for Training of ANN
9.2.5 Use of a Trained Neural Network
9.3 Conclusion
References
10 Fluid Mechanics
10.1 Conclusion
