LESSON 5 • Geology: Shaping Landscapes
82 pages
English

LESSON 5 • Geology: Shaping Landscapes

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82 pages
English
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SCIENCE Ecology; Earth Science; Geology Page 375 LESSON 5 • Geology: Shaping Landscapes Objectives: Students will: • identify different landforms and explain how they are formed and changed with time. • understand the role climate plays in shaping landforms. • understand and identify how climatic conditions affect soils, vegetation, and wildlife. • locate and map landforms found within state or regional Wilderness. • determine how human actions affect local or regional geography.
  • north atlantic ocean giant
  • science ecology
  • relief map
  • wilderness
  • landforms
  • earth-science
  • earth science
  • rock
  • erosion
  • geologic processes
  • students

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Informations

Publié par
Nombre de lectures 16
Langue English

Exrait

Finite Difference, Finite Element and Finite
Volume Methods for the Numerical Solution of
PDEs
Vrushali A. Bokil
bokilv@math.oregonstate.edu
and
Nathan L. Gibson
gibsonn@math.oregonstate.edu
Department of Mathematics
Oregon State University
Corvallis, OR
DOE Multiscale Summer School
June 30, 2007
Multiscale Summer School – p. 1/82Math Modeling and Simulation of Physical Processes

De ne the physical problem

Create a mathematical model

Systems of PDEs, ODEs, algebraic equations

De ne initial and or boundary conditions to get a
well-posed problem

Create a discrete (numerical) model

Discretize the domain! generate the grid! obtain
discrete model

Solve the discrete system

Analyze errors in the discrete system

Consistency, stability and convergence analysis
Multiscale Summer School – p. 2/82Contents

Partial Differential Equations (PDEs)

Conservation Laws: Integral and Differential
Forms

Classi cation of PDEs: Elliptic, Parabolic and
Hyperbolic

Finite Difference Methods

Analysis of Numerical Schemes: Consistency,
Stability, Convergence

Finite Volume and Finite Element Methods

Iterative Methods for Large Sparse Linear
Systems
Multiscale Summer School – p. 3/82Partial Differential Equations

PDEs: Mathematical models of continuous physical
phenomenon in which a dependent variable, say u, is a
function of more than one independent variable, say t (time),
and x (eg., spatial position).

Conservation Laws: PDEs derived by applying a physical
principle such as conservation of mass, momentum or
energy. These equations, govern the kinematic and
mechanical behavior of general bodies. These laws can be
written in either the strong or differential form, or an integral
form.

Well Posed Problem: Solution exits, is unique and depends
continuously on the data. Boundary conditions on the
( nite) boundary of the domain and/or initial conditions are
required to obtain a well posed problem.
Multiscale Summer School – p. 4/82PDEs (continued)

For simplicity, we will deal only with single
PDEs (as opposed to systems of several PDEs)
with only two independent variables,

Either two space variables, denoted by x and
y, or

One space variable denoted by x and one time
variable denoted by t

Partial derivatives with respect to independent
variables are denoted by subscripts, for example
@u

u =
t
@t
2
@ u

u =
xy
@x@y
Multiscale Summer School – p. 5/82Well Posed Problems

Boundary conditions, i.e., conditions on the
( nite) boundary of the domain and/or initial
conditions (for transient problems) are required to
obtain a well posed problem.

Properties of a well posed problem:

Solution exists

Solution is unique

Solution depends continuously on the data
Multiscale Summer School – p. 6/82Classifications of PDEs

The Order of a PDE = the highest-order partial
derivative appearing in it. For example,

The advection equation u + u = 0 is a rst
t x
order PDE.

The Heat equation u = u is a second order
t xx
PDE.

A PDE is linear if the coef cients of the partial
derivatives are not functions of u. For example

The advection equation u + u = 0 is a linear
t x
PDE.

The Burgers equation u + uu = 0 is a
t x
nonlinear PDE.
Multiscale Summer School – p. 7/82Classifications of PDEs (continued)
Second-order linear PDEs in general form
au + bu + cu + du + eu + fu + g = 0
xx xy yy x y
2
are classi ed based on the value of the discriminant b 4ac
2

b 4ac > 0: hyperbolic

e.g., wave equation : u u = 0
tt xx

Hyperbolic PDEs describe time-dependent, conservative physical
processes, such as convection, that are not evolving toward steady
state.
2

b 4ac = 0: parabolic

e.g., heat equation u u = 0
tt xx

Parabolic PDEs describe time-dependent dissipative physical
processes, such as diffusion, that are evolving toward steady state.
2

b 4ac < 0: elliptic

e.g., Laplace equation: u + u = 0
xx yy

Elliptic PDEs describe processes that have already reached steady
states, and hence are time-independent.
Multiscale Summer School – p. 8/82Parabolic PDEs: Initial-Boundary value problems

Example: One dimensional (in space) Heat Equation for u = u(t; x)
u = u ; 0 x A; t 0
t xx
The diffusion coef cient > 0 is a constant.

with

Boundary conditions: u(t; 0) = u ; u(t; A) = u , and
0 A

Initial conditions: u(0; x) = g(x)
t
domain of influence
t
q
q

domain of dependence
0
x
0
x A
q
Multiscale Summer School – p. 9/82Elliptic PDEs: Boundary value problems

Example: Model of steady heat conduction in a two dimensional (in
space) domain, governed by the Laplace equation for the temperature
T = T(x; y)
T + T = 0; 0 x W; 0 y H
xx yy

with boundary conditions

T(x; 0) = T ; T(x; H) = T
1 2

T(0; y) = T ; T(W; y) = T
3 4
y
T
2
H
T
3
y
T
q
4
q
0
x
0
T
x W
1
q
Multiscale Summer School – p. 10/82

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