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•Infragravity energy is dependent on beach slope and incident wave periods [Mase, 1988]
•Edge waves are difficult to detect
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LOCALIZED GENERATION OF LOW FREQUENCY SWASH MOTION THROUGH CHAOTIC SWASH
FRONT INTERACTIONS
Zachary Williams
UNC Wilmington
Department of Physics and Physical Oceanography
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Sketch of the talk
1. Local Nonlinearity
2. Detecting Nonlinearity
3. Swash Flow Model
4. Obtaining Data
5. Analysis of Data
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Cuspate Beach
Arcing pattern consisting of:
Bays – Lower slopeHorns‐ Higher slope
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Linear w/ NoiseNonlinear
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Making a prediction(autoregressive model)
PredictionRegression coefficients
Component pieces of phase space
Embedding Dimension
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‐Solve for a’s using best fit
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‐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
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•Analysis of chaotic bouncing ball system
1 Peak
3 Peaks
2 Peaks
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Modeling Swash
Swash has parabolic in shape
Parameterize Drag
Shock Bore
Assume nonbreaking waves
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Modeling Swash
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Modeling Swash
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Each model iteration
1. Shoot particles
2. Update position and velocity (kinematic eqn’s)
3. Collide swash particles
4. Record maximum positions
Modeling Swash
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Modeling Swash
Obtaining Data
•Every iteration, record furthest particle within a width L
•Time series given in terms of runup excursion
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Model Time Series
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Duck, NC
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•Local model has best correlation•Prediction accuracy goes down
1 Peak
2 Peaks
3 Peaks
Analysis of Model Bay
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•Local model has best correlation•Prediction accuracy doesn’t decrease
1 Peak
2 Peaks
3 Peaks
Analysis of Real Bay
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•Global model performs best•prediction accuracy slightly increases
2 Peaks
3 Peaks
1 PeakAnalysis of Real Horn
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Conclusions
•Evidence nonlinearity in model bay
•Model horns were linear
•Natural bay has evidence of nonlinearity
•Natural horn appears linear
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Thank you to….
Dr. Dylan McNamara
Dr. Brian Davis
Dr. Daniel Guo
Dr. John Morrison
Dr. Bill Atwill
Dr. Russell Herman