Field-scale Experiments of Oil Spill Dispersion Using Integrated UAV-LSPIV and Lightweight U-Net.

Xiao Zhang; Qin Li; Tong Chen; Qiang Wei

doi:10.62051/ijepes.v4n2.10

Authors

Xiao Zhang
Qin Li
Tong Chen
Qiang Wei

DOI:

https://doi.org/10.62051/ijepes.v4n2.10

Keywords:

Oil Spill Dispersion, UAV-LSPIV, Field Experiments, Deep Learning, Emergency Response

Abstract

Obtaining quantitative relationships for oil spill spreading is crucial for emergency decision-making, but existing laboratory studies have limitations in replicating natural turbulence and acquiring field data. To address this deficiency, this study integrates unmanned aerial vehicle large-scale particle image velocimetry (UAV-LSPIV), a lightweight U-Net network, and morphological segmentation algorithms (contour accuracy of 85%) to establish a dynamic on-site monitoring system for oil spills. Lake experiments quantified three phases static diffusion (800 mL expanded rapidly to 9.72 ± 0.24 m² within 30 s, grew by +5.6 m² during 30–60 s, and stabilized near 18 m² after 70 s.), while river experiments revealed shear-driven shuttle-shaped evolution across three flow regimes (0.68–1.50 m·s-1). Small spills (200 mL) exhibit 0.001–0.005 m·s-1 faster equilibrium velocities compared to larger spills. The methodology provides a paradigm for field oil spill experiments and a field-validated dataset, which is of significant importance for improving oil spill models and emergency response.

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