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Jack A. Puleo

Publications and source records attributed to Jack A. Puleo.

6 recordsLinked to original sources
USGS2025

Cross-shore hydrodynamics and morphodynamics modeling of an erosive event in the inner surf zone

The phase-averaged and depth-integrated coastal morphodynamic model, XBeach-Surfbeat, was investigated for its capability of predicting the cross-shore hydrodynamics and morphodynamics in the inner surf zone by simulating the storm-induced berm erosion, sediment transport, and subsequent sand bar formation. By utilizing a comprehensive hydrodynamic and morphodynamic dataset measured in a large wave flume and high-fidelity 3D large-eddy simulation (LES) data, a rigorous model validation was conducted to assess its capability in predicting inner-surf zone hydrodynamics and to explore how the improved hydrodynamic performance impacts the predicted morphodynamics. Using the default model parameters of the model, the undertow was overestimated with the peak magnitude being 30%–35% larger in the inner surf zone. Combining Monte Carlo simulation, the optimum hydrodynamic calibration for the simulated undertow was achieved when the roller energy dissipation parameter ( β ) "> β ) was maintained below 0.1, and the threshold water depth ( h min "> h min ) exceeded 0.25 m. The calibrated undertow improved the morphodynamic predictions by reducing the excessive berm erosion (Event I) and sand bar growth in the inner surf zone (Event II). Further improved morphodynamic predictions were achieved by calibrating sediment transport parameters, including the onshore sediment transport coefficient ( γ u a "> γua ) and the bore interval coefficient ( T b f a c "> T bfac ) associated with turbulence-bed interaction. A consistent set of optimized model coefficients for the model is shown to be effective in simulating the entire erosive event (combined Events I and II). This study reveals that further improvement of the model's capability may require incorporating new parameterizations and physics, such as wave-breaking-induced turbulence and wave nonlinearity associated with sediment transport in the inner surf and swash zones.

Coastal Engineering↗
USGS2024

Large eddy simulation of cross-shore hydrodynamics under random waves in the inner surf and swash zones

A 3D large eddy simulation coupled with a free surface tracking scheme was used to simulate cross-shore hydrodynamics as observed in a large wave flume experiment. The primary objective was to enhance the understanding of wave-backwash interactions and the implications for observed morphodynamics. Two simulation cases were carried out to elucidate key processes of wave-backwash interactions across two distinct stages: berm erosion and sandbar formation, during the early portion of a modeled storm. The major difference between the two cases was the bathymetry: one featuring a berm without a sandbar (Case I), and the other, featuring a sandbar without a berm (Case II) at similar water depth. Good agreement (overall Willmott's index of agreement greater than 0.8) between simulations and measured data in free surface elevation, wave spectrum, and flow velocities validated the model skill. The findings indicated that the bottom shear stress, represented by the Shields parameter, was significant in both cases, potentially contributing substantial sediment transport. Notably, the occurrence of intense wave-backwash interactions were more frequent in the absence of a sandbar. These intense wave-backwash interactions resulted in a pronounced horizontal pressure gradient, quantified by high Sleath parameters, exceeding the criteria for momentary bed failure. Additionally, a more vigorous turbulence-bed interaction, characterized by near-bed turbulent kinetic energy, was observed in the case lacking a sandbar, potentially augmenting sediment suspension. These insights are pivotal in understanding the mechanisms underlying berm erosion and how sandbar formation serves to protect further beach erosion.

JGR Oceans↗
USGS2019

Predicting surf zone injuries along the Delaware coast using a Bayesian network

Personnel at Beebe Healthcare in Lewes, Delaware, collected surf zone injury (SZI) data for eight summer seasons from 2010 through 2017. Data include, but are not limited to, time of injury, gender, age, and activity. More than 2000 SZI events, including 196 spinal injuries and 6 fatalities, occurred at the five most populated beaches along the 25 miles of Atlantic-fronting coast. SZI are predominantly wave related incidents associated with wading (50.1%), body surfing (18.4%), and body boarding (13.3%). The episodic nature of SZI indicate the importance of linking the environmental conditions and human behavior in the surf zone to predict days with high injury rates. Higher order statistics are necessary to effectively consider all associated factors related to SZI. Two Bayesian networks (BN) were constructed to model SZI and predict changes in injury rate (proportion of injuries to bathers) and injury likelihood (probability of at least one injury occurrence) on an hourly basis. The models incorporate environmental data collected by weather stations, wave gauges, and researcher personnel on the beach. The models include prior (e.g., historic) information to infer relationships between provided parameters. Sensitivity analysis determined the most influential parameters related to injury rates were significant wave height, foreshore slope, and water temperature. Exposure parameters (e.g., air temperature) influenced the number of people in the water, resulting in strong correlation between injury likelihood and the related meteorological conditions (variance reduction > 0.4%). Log likelihood ratio (LLR) scores indicate the network predicts SZI likelihood during any specified hour with more skill than prior predictions with the best performing model improving prediction 69.1% of the time (LLR = 69.1%). An alternative BN predicting injury rate performed worse with the prior probability model out predicting the injury rate network (positive LLR = 36.7%). Issues persist with predicting SZI that have an LLR ≪ -1 (< 5% of 2017 injuries) and occur in conditions different than when most other SZI occur. Better understanding of SZI will improve awareness techniques to both educate beachgoers and assist beach patrol decision making during high risk conditions.

Delaware↗
USGS2012

Quantifying riverine surface currents from time sequences of thermal infrared imagery

River surface currents are quantified from thermal and visible band imagery using two methods. One method utilizes time stacks of pixel intensity to estimate the streamwise velocity at multiple locations. The other method uses particle image velocimetry to solve for optimal two-dimensional pixel displacements between successive frames. Field validation was carried out on the Wolf River, a small coastal plain river near Landon, Mississippi, United States, on 26-27 May 2010 by collecting imagery in association with in situ velocities sampled using electromagnetic current meters deployed 0.1 m below the river surface. Comparisons are made between mean in situ velocities and image-derived velocities from 23 thermal and 6 visible-band image sequences (5 min length) during daylight and darkness conditions. The thermal signal was a small apparent temperature contrast induced by turbulent mixing of a thin layer of cooler water near the river surface with underlying warmer water. The visible-band signal was foam on the water surface. For thermal imagery, streamwise velocities derived from the pixel time stack and particle image velocimetry technique were generally highly correlated to mean streamwise current meter velocities during darkness (r 2 typically greater than 0.9) and early morning daylight (r 2 typically greater than 0.83). Streamwise velocities from the pixel time stack technique had high correlation for visible-band imagery during early morning daylight hours with respect to mean current meter velocities (r 2 > 0.86). Streamwise velocities for the particle image velocimetry technique for visible-band imagery had weaker correlations with only three out of six correlations performed having an r 2 exceeding 0.6. Copyright 2012 by the American Geophysical Union.

Water Resources Research↗
USGS2004

One-dimensional wave bottom boundary layer model comparison: Specific eddy viscosity and turbulence closure models

Six one-dimensional-vertical wave bottom boundary layer models are analyzed based on different methods for estimating the turbulent eddy viscosity: Laminar, linear, parabolic, k —one equation turbulence closure, k−ε —two equation turbulence closure, and k−ω —two equation turbulence closure. Resultant velocity profiles, bed shear stresses, and turbulent kinetic energy are compared to laboratory data of oscillatory flow over smooth and rough beds. Bed shear stress estimates for the smooth bed case were most closely predicted by the k−ω model. Normalized errors between model predictions and measurements of velocity profiles over the entire computational domain collected at 15° intervals for one-half a wave cycle show that overall the linear model was most accurate. The least accurate were the laminar and k−ε models. Normalized errors between model predictions and turbulence kinetic energy profiles showed that the k−ω model was most accurate. Based on these findings, when the smallest overall velocity profile prediction error is required, the processing requirements and error analysis suggest that the linear eddy viscosity model is adequate. However, if accurate estimates of bed shear stress and TKE are required then, of the models tested, the k−ω model should be used.

Journal of Waterway, Port, Coastal and Ocean Engin↗