che 555 numerical differentiation

9
by Lale Yurttas, Texas A&M University Chapter 5 1 Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. Chapter 5 NUMERICAL INTEGRATION & DIFFERENTIATION : NUMERICAL DIFFERENTIATION

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Page 1: CHE 555 Numerical Differentiation

by Lale Yurttas, Texas A&M University

Chapter 5 1

Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display.

Chapter 5NUMERICAL

INTEGRATION & DIFFERENTIATION :

NUMERICAL DIFFERENTIATION

Page 2: CHE 555 Numerical Differentiation

2

HIGH ACCURACY DIFFERENTIATION

FORMULAS• High-accuracy divided-difference

formulas can be generated by including additional terms from the Taylor series expansion.

)(2

)(3)(4)()(

)(2

)()(2)()()()(

)()()(2)()(

)(2)()()()(

2)()()()(

212

22

121

212

21

21

hOh

xfxfxfxf

hOhh

xfxfxfh

xfxfxf

hOh

xfxfxfxf

hOhxfh

xfxfxf

hxfhxfxfxf

iiii

iiiiii

iiii

iiii

iiii

Page 3: CHE 555 Numerical Differentiation

First Derivative • Forward finite divided difference

• Backward finite divided difference

• Centered finite divided difference

by Lale Yurttas, Texas A&M University

Chapter 5 3

)(:2

)(3)(4)()(' 212 hOErrorh

xfxfxfxf iiii

)(:12

)()(8)(8)()(' 42112 hOErrorh

xfxfxfxfxf iiiii

)(:2

)()(4)(3)(' 221 hOErrorh

xfxfxfxf iiii

Page 4: CHE 555 Numerical Differentiation

Example 1Estimated the derivative of

at x=0.5 using high-accuracy formulas of finite divided differences and step size of h = 0.25, the true value of -0.9125.

by Lale Yurttas, Texas A&M University

Chapter 5 4

2.125.05.015.01.0)( 234 xxxxxf

Page 5: CHE 555 Numerical Differentiation

by Lale Yurttas, Texas A&M University

Chapter 5 5

RICHARDSON EXTRAPOLATION

• There are two ways to improve derivative estimates when employing finite divided differences:– Decrease the step size, or– Use a higher-order formula that employs

more points.• A third approach, based on

Richardson extrapolation, uses two derivative estimates t compute a third, more accurate approximation.

Page 6: CHE 555 Numerical Differentiation

by Lale Yurttas, Texas A&M University

Chapter 5 6

)(31)(

34

)(31)(

34

2/

)]()([1)/(

1)(

12

12

12

12221

2

hDhDD

hIhII

hh

hIhIhh

hII

For centered difference approximations with O(h2).

hxfxfxfhD ii

i 2)()()(')( 11

Page 7: CHE 555 Numerical Differentiation

Example 2Estimated the first derivative of

at x=0.5 using Richardson Extrapolation and step sizes of h1 = 0.5 and h2 = 0.25. The true value of -0.9125.

by Lale Yurttas, Texas A&M University

Chapter 5 7

2.125.05.015.01.0)( 234 xxxxxf

Page 8: CHE 555 Numerical Differentiation

by Lale Yurttas, Texas A&M University

Chapter 5 8

DERIVATIVES OF UNEQUALLY SPACED DATA

• Data from experiments or field studies are often collected at unequal intervals. One way to handle such data is to fit a second-order Lagrange interpolating polynomial.

x is the value at which you want to estimate the derivative.

iiii

iii

iiii

iii

iiii

iii

xxxxxxxxf

xxxxxxxxf

xxxxxxxxfxf

111

11

11

11

111

11

2)(

2)(2)()(

Page 9: CHE 555 Numerical Differentiation

Example 3The heat flux at the soil-air interface can be computed with Fourier’s law,

Where q = heat flux (W/m2), k = coefficient of thermal diffusivity in soil (=3.5 x 10-7 m2/s), ρ = soil density (=1800 kg/m3), and C = soil specific heat (=840 J/(kg.oC)). Use numerical differentiation to evaluate the gradient at the soil-air interface and employ this estimate to determine the heat flux into the ground.

by Lale Yurttas, Texas A&M University

Chapter 23 9

0

)0(

zdz

dTCkzq