# Computational Modeling for Engineering MECN 6040

## Presentation on theme: "Computational Modeling for Engineering MECN 6040"— Presentation transcript:

Computational Modeling for Engineering MECN 6040
Professor: Dr. Omar E. Meza Castillo Department of Mechanical Engineering

Best known numerical method of approximation
Finite differences Best known numerical method of approximation

FINITE DIFFERENCE FORMULATION OF DIFFERENTIAL EQUATIONS
finite difference form of the first derivative Taylor series expansion of the function f about the point x, The smaller the x, the smaller the error, and thus the more accurate the approximation.

The big question: How good are the FD approximations?
This leads us to Taylor series....

EXPASION OF TAYLOR SERIES
Numerical Methods express functions in an approximate fashion: The Taylor Series. What is a Taylor Series? Some examples of Taylor series which you must have seen

General Taylor Series The general form of the Taylor series is given by provided that all derivatives of f(x) are continuous and exist in the interval [x,x+h], where h=∆x What does this mean in plain English? As Archimedes would have said, “Give me the value of the function at a single point, and the value of all (first, second, and so on) its derivatives at that single point, and I can give you the value of the function at any other point”

Example: Find the value of f(6) given that f(4)=125, f’(4)=74, f’’(4)=30, f’’’(4)=6 and all other higher order derivatives of f(x) at x=4 are zero. Solution: x=4, x+h=6  h=6-x=2 Since the higher order derivatives are zero,

The Taylor Series (xi+1-xi)= h step size (define first)
Reminder term, Rn, accounts for all terms from (n+1) to infinity.

Zero-order approximation
First-order approximation Second-order approximation

Example: Taylor Series Approximation of a polynomial Use zero- through fourth-order Taylor Series approximation to approximate the function: From xi=0 with h=1. That is, predict the function’s value at xi+1=1 f(0)=1.2 f(1)=0.2 - True value

Zero-order approximation
First-order approximation

Second-order approximation

Third-order approximation

Fourth-order approximation

Taylor Series to Estimate Truncation Errors
If we truncate the series after the first derivative term Truncation Error First-order approximation

Numerical Differentiation
Forward Difference Approximation

Numerical Differentiation
The Taylor series expansion of f(x) about xi is From this: This formula is called the first forward divided difference formula and the error is of order O(h). 18

Or equivalently, the Taylor series expansion of f(x) about xi can be written as
From this: This formula is called the first backward divided difference formula and the error is of order O(h). 19

A third way to approximate the first derivative is to subtract the backward from the forward Taylor series expansions: This yields to This formula is called the centered divided difference formula and the error is of order O(h2). 20

Numerical Differentiation
Forward Difference Approximation

Backward Difference Approximation

Centered Difference Approximation

Example: To find the forward, backward and centered difference approximation for f(x) at x=0.5 using step size of h=0.5, repeat using h=0.25. The true value is h=0.5 xi-1=0 - f(xi-1)=1.2 xi=0.5 - f(xi)=0.925 Xi+1=1 - f(xi+1)=0.2

Forward Difference Approximation
Backward Difference Approximation

Centered Difference Approximation

h=0.25 xi-1=0.25 - f(xi-1)= xi=0.5 - f(xi)=0.925 Xi+1=0.75 - f(xi+1)= Forward Difference Approximation

Backward Difference Approximation
Centered Difference Approximation

FINITE DIFFERENCE APPROXIMATION OF HIGHER DERIVATIVE
The forward Taylor series expansion for f(xi+2) in terms of f(xi) is Combine equations: 29

Solve for f ''(xi): This formula is called the second forward finite divided difference and the error of order O(h). The second backward finite divided difference which has an error of order O(h) is 30

The second centered finite divided difference which has an error of order O(h2) is
31

High-Accuracy Differentiation Formulas
High accurate estimates can be obtained by retaining more terms of the Taylor series. High-Accuracy Differentiation Formulas The forward Taylor series expansion is: From this, we can write 32

Substitute the second derivative approximation into the formula to yield:
By collecting terms: Inclusion of the 2nd derivative term has improved the accuracy to O(h2). This is the forward divided difference formula for the first derivative. 33

Forward Formulas 34

Backward Formulas 35

Centered Formulas 36

Example Estimate f '(1) for f(x) = ex + x using the centered formula of O(h4) with h = 0.25. Solution From Tables 37

In substituting the values:
38

Error Truncation Error: introduced in the solution by the approximation of the derivative Local Error: from each term of the equation Global Error: from the accumulation of local error Roundoff Error: introduced in the computation by the finite number of digits used by the computer 39

Introduction to Finite Difference
Numerical solutions can give answers at only discrete points in the domain, called grid points. If the PDEs are totally replaced by a system of algebraic equations which can be solved for the values of the flow-field variables at the discrete points only, in this sense, the original PDEs have been discretized. Moreover, this method of discretization is called the method of finite differences. (i,j)

y y u y x x x Discretization: PDE FDE Explicit Methods
Simple No stable Implicit Methods More complex Stables ∆x y n+1 ∆y y n u m,n y n-1 x x x m-1 m m+1

Summary of nodal finite-difference relations for various configurations:
Case 1: Interior Node

Case 2: Node at an Internal Corner with Convection

Case 3: Node at Plane Surface with Convection

Case 4: Node at an External Corner with Convection

Case 5: Node at Plane Surface with Uniform Heat Flux

SOLVING THE Finite difference EQUATIONS
Heat Transfer Solved Problems

The Matrix Inversion Method

jacobi ITERATION method

GAUSS-SEIDEL ITERATION

Pre-specified % tolerance based on the knowledge of your solution
Error Definitions Use absolute value. Computations are repeated until stopping criterion is satisfied. If the following Scarborough criterion is met Pre-specified % tolerance based on the knowledge of your solution

Matrix Inversion Method
USIG EXCEL Matrix Inversion Method =MMULT(A7:C9,E2:E4) =MINVERSE(A2:C4)

Jacobi Iteration Method using Excel

Gauss-Seidel Iteration Method using Excel

A large industrial furnace is supported on a long column of fireclay brick, which is 1 m by 1 m on a side. During steady-state operation is such that three surfaces of the column are maintained at 500 K while the remaining surface is exposed to 300 K. Using a grid of ∆x=∆y=0.25 m, determine the two-dimensional temperature distribution in the column. (1,3) (2,3) (3,3) (1,2) (2,2) (3,2) (1,1) (2,1) (3,1) Ts=300 K

System of Linear Equations
-4 1 -800 -500 -1000 -300 = System of Linear Equations

Matrix Inversion Method

Iteration Method using Excel

Jacobi Iteration Method using Excel

Error Iteration Method using Excel

Gauss-Seidel Iteration Method using Excel

Error Iteration Method using Excel

Iteration Method using Excel