•Infragravity energy is dependent on beach slope and incident wave periods [Mase, 1988]
•Edge waves are difficult to detect
LOCALIZED GENERATION OF LOW FREQUENCY SWASH MOTION THROUGH CHAOTIC SWASH
FRONT INTERACTIONS
Zachary Williams
UNC Wilmington
Department of Physics and Physical Oceanography
Sketch of the talk
1. Local Nonlinearity
2. Detecting Nonlinearity
3. Swash Flow Model
4. Obtaining Data
5. Analysis of Data
Cuspate Beach
Arcing pattern consisting of:
Bays – Lower slopeHorns‐ Higher slope
Linear w/ NoiseNonlinear
Making a prediction(autoregressive model)
PredictionRegression coefficients
Component pieces of phase space
Embedding Dimension
‐Solve for a’s using best fit
‐Best prediction uses all nearest neighbors
‐No fall off w/ prediction
Linear w/ NoiseNonlinear
‐Best prediction uses intermediate number of nearest neighbors
‐Fall off w/ prediction distance
•Analysis of chaotic bouncing ball system
1 Peak
3 Peaks
2 Peaks
Modeling Swash
Swash has parabolic in shape
Parameterize Drag
Shock Bore
Assume nonbreaking waves
Modeling Swash
Modeling Swash
Each model iteration
1. Shoot particles
2. Update position and velocity (kinematic eqn’s)
3. Collide swash particles
4. Record maximum positions
Modeling Swash
Modeling Swash
Obtaining Data
•Every iteration, record furthest particle within a width L
•Time series given in terms of runup excursion
Model Time Series
Duck, NC
•Local model has best correlation•Prediction accuracy goes down
1 Peak
2 Peaks
3 Peaks
Analysis of Model Bay
•Local model has best correlation•Prediction accuracy doesn’t decrease
1 Peak
2 Peaks
3 Peaks
Analysis of Real Bay
•Global model performs best•prediction accuracy slightly increases
2 Peaks
3 Peaks
1 PeakAnalysis of Real Horn
Conclusions
•Evidence nonlinearity in model bay
•Model horns were linear
•Natural bay has evidence of nonlinearity
•Natural horn appears linear
Thank you to….
Dr. Dylan McNamara
Dr. Brian Davis
Dr. Daniel Guo
Dr. John Morrison
Dr. Bill Atwill
Dr. Russell Herman