ntroducing engineering students to numerical analysis and computing, this book covers a range of topics suitable for the first three years of a four year undergraduate engineering degree. The teaching of computing to engineers is hampered by the lack of suitable problems for the students to tackle so much effort has gone into making the problems realistic and relevant while at the same time solvable for undergraduates. Taking a balanced approach to teaching computing and computer methods at the same time, this book satisfies the need to beable to use computers (using both formal languages such as Fortran and also other applications such as Matlab and Microsoft Excel), and the need to be ableto solve realistic engineering problems. INDICE: Preface Part 1: Introduction 1. Engineering Modelling & Analysis 2. Accuracy, Speed & Algorithms Part 2: Roots of Equations 3. Introduction 4. Bracket Methods 5. Open Methods Part 3: Numerical Integration 6. Trapezoidal Rule 7. Simpson's Rule Part 4: Numerical Interpolation 8. Newton's Method 9. Cubic Splines & Other Methods Part 5: Systems of Equations 10. Introduction 11. Gauss Seidel Method 12. LU Decomposition & Thomas Algorithm Part 6: Ordinary Differential Equations 13. Euler's Method 14. Heun & Runge-Kutta Methods Part 7: Finite Difference Modelling 15. Introduction 16. Laplace's Hot Plate 17. Solution of Pure Convection Equation 18. Solution of Pure Diffusion Equation 19. Solution of Full Transport Equation 20. Alternate Schemes Part 8: Probability & Statistics 21. Descriptive Statistics 22. Populations & Sample 23. Linear Combination of Random Variables 24. Correlation & Regression 25. Multiple Regression 26. Non-Linear Regression Part 9: Probability Distributions 27. Introduction 28. Bernoulli, Binomial, Geometric 29. Poisson, Exponential, Gamma 30. Normal & Lognormal 31. Extreme Values 32. Chi-square & Rayleigh 33. Mulitvariate Part 10: Monte Carlo Method 34. Introduction 35. Generation of Random Numbers 36. Acceptance-Rejection 37. Metropolis Applications Part 11: Stochastic Modelling 38. Goodness of Fit & Model Calibration 39. Likelihood & Uncertainty 40. Markov Chains 41. Time Series Part 12: Optimisation 42. Local Optimisation 43.Global Optimisation Part 13: Linear Systems & Resonance 44. Linear Systems & Resonance Part 14: Spectral Analysis 45. Introduction 46. Discrete Fourier Transform 47. Practical Aspects I 48. Practical Aspects II Appendix A: Taylor Series Appedix B: Error Function & Gamma Function Appendix C: Complex Sinusoid Appendix D: Open Source Software.
- ISBN: 978-0-415-46962-3
- Editorial: Taylor and Francis
- Encuadernacion: Rústica
- Páginas: 440
- Fecha Publicación: 01/10/2008
- Nº Volúmenes: 1
- Idioma: Inglés