Quantification of Near-bed Swash-zone Velocities Using Particle Image Velocimetry Douglas Krafft (1), Jack A. Puleo (1), José Carlos Pintado Patiño (2) (1) Center for Applied Coastal Research, Department of Civil and Environmental Engineering, University of Delaware, Newark DE 19716, (2) Laboratorio de Ingeniería y Procesos Costeros, Instituto de Ingeniería, Universidad Nacional Autónoma de México, Sisal, Yucatán, México, Introduction Foreshore morphology is controlled by swash-zone processes. Predicting morphodynamics requires knowledge of sediment concentration and fluid velocities, but individual swash events contain spatial and temporal regions in which current meters are incapable of measuring velocity. One of these regions is sheet flow near and within the mobile bed. Our interest is to use particle image velocimetry (PIV) techniques for quantifying near bed fluid velocities to increase spatial and temporal sampling capability. Experiments Two swash-zone experiments involving dam-break events were undertaken to investigate the near-bed flow structure and flows within the sheet layer. Experiments were conducted over a planar sloping mobile bed (1:7) with different grain sizes (D 50 = 0.59 mm and 1.03 mm) at the University of Delaware Center for Applied Coastal Research (CACR). Swash forcing was generated using a dam-break mechanism (Figure 1) over a flat section of a wave flume. Sensors (Figure 2) consisting of an ultrasonic distance meter (UDM) measured the water level, a Vectrino II measured the near-bed flow velocity vertical profile (1mm bins) at 100 Hz, and a high speed camera collected imagery at 301 Hz of a vertical plane in the lower few centimeters of the water column. Sensor cross-shore location was 1 m onshore from the toe of the slope and 5.3 m from the dam-break. Image size was 87.6 by 65.7 mm and extended below the initial bed level. Image resolution was mm per pixel. For the 1.03 mm sediment, 9 runs were collected. For the 0.59 mm sediment, 12 runs were collected. Center for Applied Coastal Research Results A column of velocity vectors at the same cross-shore location as the Vectrino II was estimated from each image pair using error correlation-based PIV. Vector post-processing (removal) using poor correlation and fluid acceleration criteria was applied. PIV velocity estimates are generally present earlier in uprush and later in backwash than Vectrino II measurements (Figure 3). PIV estimates begin in the sheet layer. Over time, the velocity profile extends higher into the water column as sediment acting as a tracer is present. In the 1.03 mm case (Figure 3A,B), estimates begin early in uprush and improve until concentrations decrease. PIV estimates improve again in the backwash as sediment mobilization increases. In the 0.59 mm case (Figure 3C,D), estimates are initially poor with reliable data beginning later in the swash event. More velocities are present, and agreement with Vectrino II data improved near flow reversal. PIV estimates worsen through backwash when sediment concentration is high. Velocity Time Series Example near-bed PIV and Vectrino II velocity estimates at several elevations and over the event duration are shown in Figures (4,5). In the 1.03 mm case, PIV-estimated velocities occur for the duration of the swash event, with high data retention and provide data for a larger portion of the event duration than the in-situ measurements. Backwash deceleration, which has not been measured with in-situ instrumentation, is also observed. Data from the 0.59 mm case are more sparse likely due to excessive sediment concentration causing difficulty in the correlation algorithm. PIV data retention increases near the bed for the 1.03 mm case but not the 0.59 mm case. Figure 1 (left) and 2 (right). Figure 1: The 10.5 m long 0.6 by 0.8 m section of the flume showing the dam break mechanism and beach slope. Sensors (A) were located in the region marked by the dashed box. Figure 2: Cross-section of the flume at the sensor location, with the UDM (B), Vectrino II (C), and high speed camera(D). Error Root-mean-square errors in velocity between PIV and Vectrino II are calculated for each elevation bin for all ensembles (Figure 6). Errors near the initial bed level are small for the 1.03 mm case but increase with elevation. Larger errors are related to the general lack of sediment higher in the water column. However, velocities in the direct vicinity of the initial bed are critical for sheet flow sediment transport studies and for quantifying flow throughout the event duration. Errors are small for the 0.59 mm case but less data were retained after quality control. Often the sediment concentration was sufficiently large that the correlation algorithm had difficulty identifying a robust velocity estimate. Figure 3: A,C) Water depth in 1.03 mm and 0.59 mm case (respectively) plotted against time. Vertical lines mark the instants of frames. B,D) 1.03 mm and 0.59 mm (respectively) sediment imagery with PIV (blue) and Vectrino II (red) velocity vectors. A 1 m/s scale vector is shown by the yellow arrow. Figure 4: A) Water depth. Subsequent panels show velocity time series from PIV (black) and Vectrino II (magenta). Approximate elevations are indicated in each panel. Figure 6: RMSE plotted against elevation for all runs mm runs are shown in magenta mm runs are shown in black. Summary and Future PIV can be used to quantify near-bed swash-zone velocities under mobile bed conditions. Additional PIV algorithms and perhaps less restrictive quality control algorithms will be tested. Data will be ensemble-averaged and robust techniques for identifying the bed level and sheet layer thickness will be developed. Submerged/buried camera applications need to be tested to make this approach field- capable. Figure 7: Example time stacks for the 1.03 mm (A) and 0.59 mm (B) cases. Inset (B,D) show an image frame and the column of pixels extracted (same cross-shore location as the Vectrino II) denoted by the yellow line. Magenta curves mark the instantaneous bed level. Blue curves mark the top of the sheet layer. Time Stack An added benefit of the imaging is the ability to create time stacks for free surface slope, bed level and sheet layer thickness. A column of pixels (Figure 7B,D) was extracted from each frame and displayed in a time-space plot (Figure 7A,C). The bed level varied over 10 mm for both sediment types but there was slight net accretion for the 1.03 mm case compared to almost 10 mm net erosion for the 0.59 mm case. Sheet layer thickness is more difficult to identify during uprush. The sheet layer thickness increase during backwash and reaches the free surface for the 0.59 mm case. B C D A A B C D EC14C-1002 z’=0 mm z’=2 mm z’=4 mm z’=6 mm D 50 = 1.03 mm z’=0 mm z’=4 mm z’=8 mm z’=12 mm D 50 = 0.59 mm D 50 = 1.03 mm D 50 = 0.59 mm Figure 5: A) Water depth. Subsequent panels show velocity time series from PIV (black) and Vectrino II (magenta). Approximate elevations are indicated in each panel. D 50 = 0.59 mm D 50 = 1.03 mm ABCDE F G HIJKL A B C D