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Numerical Methods Applied to Mechatronics Lecture No 2 Escuela de Ingeniería Mecatrónica Universidad Nacional de Trujillo MATHEMATICAL MODELING, NUMERICAL METHODS, AND PROBLEM SOLVING Dr. Jorge A. Olortegui Yume, Ph.D.

Roots Non Linear Eqns

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Page 1: Roots Non Linear Eqns

Numerical Methods Applied to Mechatronics

Lecture No 2

Escuela de Ingeniería Mecatrónica Universidad Nacional de Trujillo

MATHEMATICAL MODELING, NUMERICAL METHODS, AND PROBLEM SOLVING

Dr. Jorge A. Olortegui Yume, Ph.D.

Page 2: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 2

THE ENGINEERING PROBLEM SOLVING PROCESS

Page 3: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 3

SOME DEFINITIONS

• Mathematical model

Formulation or equation expressing essential features of a physical system or process mathematically involving:

• Dependent variables

• Independent variables

• Parameters

• Forcing functions.

Page 4: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 4

SOME DEFINITIONS • Function model

Dependentvariable

findependent

variables, parameters,

forcingfunctions

•Dependent variable

Characteristic reflecting system´s behavior/state

•Independent variables

Dimensions determining system´s behavior (e.g., t,x).

•Parameters

Constants reflecting system’s properties/composition (e.g., m,Cp, k).

•Forcing functions

External influences acting upon the system(e.g., Forces, Moments, )

Page 5: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 5

SOME DEFINITIONS

• Function model example

Analytical model for the jumper’s velocity, accounting for drag, is

Dependentvariable

findependent

variables, parameters,

forcingfunctions

v t gm

cdtanh

gcd

mt

“g” “m” “cd” “t” “v”

Page 6: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 6

SOME DEFINITIONS • Analytical Modeling

Process of expressing a model in terms of mathematical expressions

based on natural laws and principles.

- They end up in: Algebraic equations

Implicit functions

Diff. equations

Example: Bungee jumper

LawsNewtonndmaF '2

mavcmg

maFF

d

dg

2

dt

dvmvcmg d 2 Diff. equation

Page 7: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 7

SOME DEFINITIONS • Analytical Modeling

Example: Bungee jumper (Analytical solution)

v t gm

cdtanh

gcd

mt

Page 8: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 8

SOME DEFINITIONS • Model Results

For the bungee jumper example for m = 68.1 kg y cd =0.25 kg/s

can obtain a graphical representation

v t gm

cdtanh

gcd

mt

Page 9: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 9

SOME DEFINITIONS • Numerical Modeling

Process of expressing a model in terms of simple arithmetic and

logic expressions.

- easy to implement in a computer

Example: Bungee jumper

dv

dtv

tv ti1 v ti ti1 ti

Time rate change of velocity can be approximated as

Page 10: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 10

SOME DEFINITIONS • Numerical Modeling

Example: Bungee jumper (cont´d)

ii

iiid

tt

tvtvmtvcmg

1

12

The diff. equation: could be approximated as and solved as

dt

dvmvcmg d 2

iiidii tttvcmgm

tvtv 1

2

1

1

Page 11: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 11

SOME DEFINITIONS • Numerical Modeling

Example: Bungee jumper (cont´d)

NOTES: • Apparently, NO NEED for numerical development BUT,…

• Some mathematical models of physical phenomena may be much more complex: CAN´T SOLVE ANALYTICALLY BUT NUMERICALLY

• Complex models may not be solved exactly or require more sophisticated mathematical techniques than simple algebra for their solution.

Page 12: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 12

Bases for Numerical Models

• Conservation laws provide the foundation for many model functions.

• Different fields of engineering and science apply these laws to different paradigms within the field.

• Among these laws are:

– Conservation of mass

– Conservation of momentum

– Conservation of charge

– Conservation of energy

Page 13: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 13

Summary of Numerical Methods 5 categories of numerical methods:

Roots of Nonlinear equations

Page 14: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 14

Summary of Numerical Methods 5 categories of numerical methods:

Linear algebraic equations

Page 15: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 15

Summary of Numerical Methods 5 categories of numerical methods:

Function aprroximation or Curve fitting

Page 16: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 16

Summary of Numerical Methods 5 categories of numerical methods:

Numerical Integration/ Differentiation

Page 17: Roots Non Linear Eqns

Math. Modeling, Num. Methods, Prob. Solving Dr. Jorge A. Olortegui Yume, Ph.D. 17

Summary of Numerical Methods 5 categories of numerical methods:

Numerical methods For Differential Equations