UW RTG IPDE Summer School 2011 Finite Volume Methods and the - - PowerPoint PPT Presentation

▶
uw rtg ipde summer school 2011 finite volume methods and
SMART_READER_LITE
LIVE PREVIEW

UW RTG IPDE Summer School 2011 Finite Volume Methods and the - - PowerPoint PPT Presentation

Dec 18, 2022 347 likes •1k views

UW RTG IPDE Summer School 2011 Finite Volume Methods and the Clawpack Software Randall J. LeVeque Applied Mathematics University of Washington Donna A. Calhoun Mathematics Boise State University R.J. LeVeque, University of Washington IPDE

slide-1
SLIDE 1

UW RTG IPDE Summer School 2011 Finite Volume Methods and the Clawpack Software

Randall J. LeVeque Applied Mathematics University of Washington Donna A. Calhoun Mathematics Boise State University

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-2
SLIDE 2

Course outline

Main goals:

  • Theory of hyperbolic conservation laws in one dimension
  • Finite volume methods in 1 and 2 dimensions
  • Some applications: advection, acoustics, Burgers’, shallow

water equations, traffic flow

  • Use of the Clawpack software:

www.clawpack.org Slides will be posted and green links can be clicked. $IPDE refers to the webpage

http://www.amath.washington.edu/~rjl/ipde

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-3
SLIDE 3

Outline

This lecture

  • Hyperbolic equations
  • Motivating examples
  • Advection, flow in a pipe.
  • Acoustics, sound waves
  • Elasticity, seismic waves
  • Shallow water equations, tsunamis

Reading: Chapters 1 and 2 Note: Some slides have section numbers on footer.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Chap. 1, 2]

slide-4
SLIDE 4

Linear and nonlinear waves

A wave is a disturbance or displacement that propagates. Examples:

  • Water waves (disturbance of depth)
  • Sound waves (disturbance of pressure)
  • Seismic waves (displacement of elastic material)

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-5
SLIDE 5

Linear and nonlinear waves

A wave is a disturbance or displacement that propagates. Examples:

  • Water waves (disturbance of depth)
  • Sound waves (disturbance of pressure)
  • Seismic waves (displacement of elastic material)

Very small disturbances can be modeled by linear partial differential equations Solutions are often continuous, smooth functions

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-6
SLIDE 6

Linear and nonlinear waves

A wave is a disturbance or displacement that propagates. Examples:

  • Water waves (disturbance of depth)
  • Sound waves (disturbance of pressure)
  • Seismic waves (displacement of elastic material)

Very small disturbances can be modeled by linear partial differential equations Solutions are often continuous, smooth functions Larger displacements require nonlinear equations Solutions may be discontinous: shock waves

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-7
SLIDE 7

First order hyperbolic PDE in 1 space dimension

Linear: qt + Aqx = 0, q(x, t) ∈ l Rm, A ∈ l Rm×m Conservation law: qt + f(q)x = 0, f : l Rm → l Rm (flux) Quasilinear form: qt + f′(q)qx = 0 Hyperbolic if A or f′(q) is diagonalizable with real eigenvalues. Models wave motion or advective transport. Eigenvalues are wave speeds. Note: Second order wave equation ptt = c2pxx can be written as a first-order system (acoustics).

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Sec. 1.1]

slide-8
SLIDE 8

Derivation of Conservation Laws

q(x, t) = density function for some conserved quantity, so x2

x1

q(x, t) dx = total mass in interval changes only because of fluxes at left or right of interval.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Chap. 2]

slide-9
SLIDE 9

Derivation of Conservation Laws

q(x, t) = density function for some conserved quantity. Integral form: d dt x2

x1

q(x, t) dx = F1(t) − F2(t) where Fj = f(q(xj, t)), f(q) = flux function.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Chap. 2]

slide-10
SLIDE 10

Derivation of Conservation Laws

If q is smooth enough, we can rewrite d dt x2

x1

q(x, t) dx = f(q(x1, t)) − f(q(x2, t)) as x2

x1

qt dx = − x2

x1

f(q)x dx

  • r

x2

x1

(qt + f(q)x) dx = 0 True for all x1, x2 = ⇒ differential form: qt + f(q)x = 0.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Chap. 2]

slide-11
SLIDE 11

Advection equation

Flow in a pipe at constant velocity u = constant flow velocity q(x, t) = tracer concentration, f(q) = uq = ⇒ qt + uqx = 0. True solution: q(x, t) = q(x − ut, 0)

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Sec. 2.1]

slide-12
SLIDE 12

Advection equation

Flow in a pipe at constant velocity u = constant flow velocity q(x, t) = tracer concentration, f(q) = uq = ⇒ qt + uqx = 0. True solution: q(x, t) = q(x − ut, 0)

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Sec. 2.1]

slide-13
SLIDE 13

Advection equation

Flow in a pipe at constant velocity u = constant flow velocity q(x, t) = tracer concentration, f(q) = uq = ⇒ qt + uqx = 0. True solution: q(x, t) = q(x − ut, 0)

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Sec. 2.1]

slide-14
SLIDE 14

Advection examples

Here and on future slides, $IPDE refers to the webpage

http://www.amath.washington.edu/~rjl/ipde

$CLAW refers to the Clawpack top directory. Examples:

  • $IPDE/claw-apps/advection-1d-1 Created for this class.
  • $CLAW/apps/advection Example applications in Clawpack.
  • $CLAW/book Examples from the book.
  • www.clawpack.org/doc/apps.html Gallery of applications.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-15
SLIDE 15

Example: Linear acoustics in a 1d tube q = p u

  • p(x, t) = pressure perturbation

u(x, t) = velocity Equations: pt + κux = 0 κ = bulk modulus ρut + px = 0 ρ = density

  • r

p u

  • t

+

  • κ

1/ρ p u

  • x

= 0. Eigenvalues: λ = ±c, where c =

  • κ/ρ = sound speed

Second order form: Can combine equations to obtain ptt = c2pxx

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-16
SLIDE 16

Riemann Problem

Special initial data: q(x, 0) = ql if x < 0 qr if x > 0 Example: Acoustics with bursting diaphram Pressure: Acoustic waves propagate with speeds ±c.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Sec. 3.9.1]

slide-17
SLIDE 17

Riemann Problem

Special initial data: q(x, 0) = ql if x < 0 qr if x > 0 Example: Acoustics with bursting diaphram Pressure: Acoustic waves propagate with speeds ±c.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Sec. 3.9.1]

slide-18
SLIDE 18

Riemann Problem

Special initial data: q(x, 0) = ql if x < 0 qr if x > 0 Example: Acoustics with bursting diaphram Pressure: Acoustic waves propagate with speeds ±c.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Sec. 3.9.1]

slide-19
SLIDE 19

Riemann Problem

Special initial data: q(x, 0) = ql if x < 0 qr if x > 0 Example: Acoustics with bursting diaphram Pressure: Acoustic waves propagate with speeds ±c.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Sec. 3.9.1]

slide-20
SLIDE 20

Riemann Problem

Special initial data: q(x, 0) = ql if x < 0 qr if x > 0 Example: Acoustics with bursting diaphram Pressure: Acoustic waves propagate with speeds ±c.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Sec. 3.9.1]

slide-21
SLIDE 21

Riemann Problem

Special initial data: q(x, 0) = ql if x < 0 qr if x > 0 Example: Acoustics with bursting diaphram Pressure: Acoustic waves propagate with speeds ±c.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Sec. 3.9.1]

slide-22
SLIDE 22

Riemann Problem for acoustics

Waves propagating in x–t space: Left-going wave W1 = qm − ql and right-going wave W2 = qr − qm are eigenvectors of A.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Sec. 3.9.1]

slide-23
SLIDE 23

Seismic wave in layered medium

Red = div(u) [P-waves], Blue = curl(u) [S-waves]

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-24
SLIDE 24

Seismic wave in layered medium

Red = div(u) [P-waves], Blue = curl(u) [S-waves]

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-25
SLIDE 25

Seismic wave in layered medium

Red = div(u) [P-waves], Blue = curl(u) [S-waves]

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-26
SLIDE 26

Seismic wave in layered medium

Red = div(u) [P-waves], Blue = curl(u) [S-waves]

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-27
SLIDE 27

Seismic wave in layered medium

Red = div(u) [P-waves], Blue = curl(u) [S-waves]

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-28
SLIDE 28

Seismic wave in layered medium

Red = div(u) [P-waves], Blue = curl(u) [S-waves]

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-29
SLIDE 29

Seismic wave in layered medium

Red = div(u) [P-waves], Blue = curl(u) [S-waves]

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-30
SLIDE 30

Seismic wave in layered medium

Red = div(u) [P-waves], Blue = curl(u) [S-waves]

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-31
SLIDE 31

Seismic wave in layered medium

Red = div(u) [P-waves], Blue = curl(u) [S-waves]

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-32
SLIDE 32

Seismic wave in layered medium

Red = div(u) [P-waves], Blue = curl(u) [S-waves]

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-33
SLIDE 33

Red = div(u) [P-waves], Blue = curl(u) [S-waves] Four levels with refinement factors 4, 4, 4

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-34
SLIDE 34

Red = div(u) [P-waves], Blue = curl(u) [S-waves] Four levels with refinement factors 4, 4, 4

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-35
SLIDE 35

Red = div(u) [P-waves], Blue = curl(u) [S-waves] Four levels with refinement factors 4, 4, 4

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-36
SLIDE 36

Red = div(u) [P-waves], Blue = curl(u) [S-waves] Four levels with refinement factors 4, 4, 4

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-37
SLIDE 37

Shock formation

For nonlinear problems wave speed generally depends on q. Waves can steepen up and form shocks = ⇒ even smooth data can lead to discontinuous solutions.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-38
SLIDE 38

Shock formation

For nonlinear problems wave speed generally depends on q. Waves can steepen up and form shocks = ⇒ even smooth data can lead to discontinuous solutions.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-39
SLIDE 39

Shock formation

For nonlinear problems wave speed generally depends on q. Waves can steepen up and form shocks = ⇒ even smooth data can lead to discontinuous solutions.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-40
SLIDE 40

Shock formation

For nonlinear problems wave speed generally depends on q. Waves can steepen up and form shocks = ⇒ even smooth data can lead to discontinuous solutions.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-41
SLIDE 41

Shock formation

For nonlinear problems wave speed generally depends on q. Waves can steepen up and form shocks = ⇒ even smooth data can lead to discontinuous solutions.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-42
SLIDE 42

Shock formation

For nonlinear problems wave speed generally depends on q. Waves can steepen up and form shocks = ⇒ even smooth data can lead to discontinuous solutions. Computational challenges! Need to capture sharp discontinuities. PDE breaks down, standard finite difference approximation to qt + f(q)x = 0 can fail badly: nonphysical oscillations, convergence to wrong weak solution.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-43
SLIDE 43

Shallow water equations

h(x, t) = depth u(x, t) = velocity (depth averaged, varies only with x) Conservation of mass and momentum hu gives system of two equations. mass flux = hu, momentum flux = (hu)u + p where p = hydrostatic pressure ht + (hu)x = 0 (hu)t +

  • hu2 + 1

2gh2

  • x

= 0 Jacobian matrix: f′(q) =

  • 1

gh − u2 2u

  • ,

λ = u ±

  • gh.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011 [FVMHP Sec. 13.1]

slide-44
SLIDE 44

Shallow water equations with bathymetry B(x, y)

ht + (hu)x + (hv)y = (hu)t +

  • hu2 + 1

2gh2

  • x

+ (huv)y = −ghBx(x, y) (hv)t + (huv)x +

  • hv2 + 1

2gh2

  • y

= −ghBy(x, y) The equations have the form qt + f(q)x + g(q)y = ψ where q(x, y, t) = h hu hv

  • ,

f(q) =

  • hu

hu2 + 1

2gh2

huv

  • ,

etc.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-45
SLIDE 45

DART buoy data

Deep-ocean Assessment and Reporting of Tsunamis NOAA’s Network of pressure gauges on the ocean floor

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-46
SLIDE 46

DART buoy data

Deep-ocean Assessment and Reporting of Tsunamis NOAA’s Network of pressure gauges on the ocean floor

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-47
SLIDE 47

DART buoy data

Deep-ocean Assessment and Reporting of Tsunamis NOAA’s Network of pressure gauges on the ocean floor

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-48
SLIDE 48

DART buoy data

Deep-ocean Assessment and Reporting of Tsunamis NOAA’s Network of pressure gauges on the ocean floor

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-49
SLIDE 49

DART buoy data

Deep-ocean Assessment and Reporting of Tsunamis NOAA’s Network of pressure gauges on the ocean floor

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-50
SLIDE 50

www.ndbc.noaa.gov/station_page.php?station=32412

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-51
SLIDE 51

DART buoy data

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-52
SLIDE 52

DART buoy data

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-53
SLIDE 53

NOAA unit sources for subduction zone

From: Tang, L., V.V. Titov, and C.D. Chamberlin (2010): A Tsunami Forecast Model for Hilo, Hawaii. NOAA OAR Special Report, PMEL Tsunami Forecast Series: Vol. 1, 94 http://nctr.pmel.noaa.gov/pubs.html

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-54
SLIDE 54

Response at DART buoy from unit earthquakes

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-55
SLIDE 55

Response at DART buoy from unit earthquakes

Propagation in deep water is essentially linear... Fit linear combination of these responses to DART data.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-56
SLIDE 56

Best fit from unit earthquakes

Best fit with constraint that all coefficients (dislocations) positive.

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-57
SLIDE 57

Response at DART 51406

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-58
SLIDE 58

27 February 2010 tsunami

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-59
SLIDE 59

27 February 2010 tsunami

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-60
SLIDE 60

27 February 2010 tsunami

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-61
SLIDE 61

27 February 2010 tsunami

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-62
SLIDE 62

27 February 2010 tsunami

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-63
SLIDE 63

27 February 2010 tsunami

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-64
SLIDE 64

Inundation of Hilo, Hawaii

Using 5 levels of refinement with ratios 8, 4, 16, 32. Resolution ≈ 160 km on Level 1 and ≈ 10m on Level 5. Total refinement factor: 214 = 16, 384 in each direction. With 15 m displacement at fault:

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011

slide-65
SLIDE 65

Inundation of Hilo, Hawaii

Using 5 levels of refinement with ratios 8, 4, 16, 32. Resolution ≈ 160 km on Level 1 and ≈ 10m on Level 5. Total refinement factor: 214 = 16, 384 in each direction. With 90 m displacement at fault:

R.J. LeVeque, University of Washington IPDE 2011, June 20, 2011