Laboratory 3D Velocity Fields of Shallow Island Wakes in Oscillatory Flow

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This dataset comprises high-resolution, time-resolved, three-dimensional velocity fields generated during laboratory experiments investigating shallow water island wakes under oscillatory tidal forcing. The data was collected using a custom-built flume and a novel volumetric measurement system based on Synthetic Aperture Particle Imaging Velocimetry (SAPIV). Key Features: Measurement Technique: Synthetic Aperture PIV (SAPIV) with 9-camera array. Spatial Resolution: Voxel size of 0.022 cm x 0.022 cm × 0.05 cm; up to 293 million voxels per time step. Temporal Resolution: Burst sampling at 20 Hz, 11 frames per second over 8 tidal cycles per experiment. Flow Conditions: Sinusoidal tidal forcing with varied amplitude, period, and depth. Island Geometry: Circular cylinder (D = 10 cm and 15 cm). Parameter Space: Keulegan–Carpenter number (KC = U₀·T/D) Relative boundary layer thickness (δ⁺ = √(ν·T/h)) Aspect ratio (h/D) Wake stability parameter (S = 2√π·δ⁺/KC) Wake Forms Captured: Symmetric Asymmetric Unsteady Bubble Vortex Shedding Purpose Characterize wake dynamics in shallow tidal flows, including vortex formation, wake stability, and vertical mixing. Validate scaling laws for upwelling and lateral advection based on Ekman pumping theory. Support ecological modelling by quantifying vertical transport relevant to nutrient fluxes and biological productivity. Benchmark numerical models of coastal and shelf-scale island wakes. Enable reuse in fluid dynamics research, particularly in studies of vortex dynamics and environmental mixing. Potential Reuse Develop or validate 3D numerical simulations of island wakes. Explore the influence of tidal parameters on wake morphology. Investigate secondary vortex structures and their role in vertical transport. Apply machine learning techniques to classify wake regimes and training of Physics Informed Neural Networks (PINNs) Support environmental impact assessments in coastal engineering.