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Knowledge of the droplet size distribution (DSD) of spilled oil is essential for the accurate prediction of oil transport, dissolution, and biodegradation. Breaking waves play important roles in oil droplet formation in oceanic environments. To understand the effects of breaking waves on oil DSD, oil spill experiments were designed and performed in a large-scale wave tank. A plunging breaker with a height of about 0.4 m was produced using the dispersive focusing method within the tank. Oil placed within the breaker resulted in a DSD that was measured using a shadowgraph camera and found to fit a Gaussian distribution (µ = 1.2 mm, σ2 = 0.29 mm2). For droplets smaller than 1500 µm, the number-based DSD matched the DS1988 correlation [1], which gives N(d) ~ d-2.3, but this was N(d) ~ d-9.7 for droplets larger than 1500 µm. An order of magnitude investigation revealed that a Gaussian volume-based DSD results in a number-based DSD that may be approximated by d-b (with b ≈ 2) for small diameters (relative to the mean), which explains the occurrence of the DS1988 correlation. With the measured wave hydrodynamics, the VDROP model was adopted to simulate the DSD, which closely matched the observed DSD. The present results reduce the empiricism of the DS1988 correlation.

Uncrewed surface vehicles (USVs) have gained significant attention in the past decade due to their potential commercial applications and a vast global market. An essential capability of USVs is autonomous obstacle avoidance in oceanic environments to prevent collisions. This study focuses on investigating and benchmarking deep learning-based real-time object detection algorithms for collision avoidance in USVs. The candidate algorithms were trained and evaluated using a maritime dataset provided by Marine Thinking Inc. Among the algorithms tested, You Only Look Once (YOLO) v5m demonstrated outstanding performance (mAP50 > 0.84 and mAP50:95 > 0.69) and high frame per second (FPS > 60) in realtime scenarios. Subsequently, the pre-trained YOLO v5m algorithm was converted into a TensorRT engine and deployed within a DeepStream pipeline on an onboard Nvidia Jetson for real-time collision avoidance tests. The results indicate that the YOLO v5m algorithm successfully detected obstacles and provided feedback to the ArduPilot autonomous suite. The ArduPilot, in turn, adjusted the control signal of the USV motor based on the Bendy Ruler obstacle avoidance algorithm, resulting in the successful avoidance of detected obstacles, and preventing collisions. This study presents an efficient, production-ready, and cost-effective autonomous collision avoidance methodology for USVs. [ABSTRACT FROM AUTHOR]

Understanding the droplet size distribution of subsea oil releasing is important to predict the subsequent transport and degradation of the spilled oil. Single- and multi-phase oil jet experiments were conducted in the Ohmsett facility, including pure oil jet and oil-air jet through a 10.7-mm pipe and a 4.7-mm pipe. Measurements of the vertical jet hydrodynamics and the oil size distribution were obtained. The reported results help to extend subsea oil spills experiment scale into a meso-scale, and the measurement range of the oil size is widened up to 2 cm. Moreover, the application of dispersant and involvement of air phase provided valuable scientific evidence for the usage of dispersants to treat oil spills. The data confirmed the effectiveness of the dispersant that reduces oil droplet size and also suggested that the participation of the gas phase facilitated the decreasing of oil droplet size. The results were compared to the numerical simulation tool VDROP-J. While the hydrodynamics showed good consistency, the systemdependent coefficient needed a slight revision to coincide with presented data. This also confirmed the significance of experimental materials for further validation and development of oil spill models, where we also provided guidance and discussion on conducting and post-processing the oil spill experiment. [ABSTRACT FROM AUTHOR]

A numerical study, based on a variably saturated groundwater flow model within a Monto Carlo framework, was conducted to investigate flow and solute transport in a heterogeneous beach aquifer subjected to tides. The numerical simulations were conducted based on our previous tracer experiments performed in a laboratory beach. Heterogeneity is assumed to be multifractal generated using the Universal Multifractal model. Our results show that heterogeneity greatly alters temporal and spatial evolution of the tracer plume migrating in the beach. The spreading coefficient of the plume shows very dynamic response to tides; it increases as the tidally driven recirculation cell overlaps with the plume and decreases as the recirculation cell moves far from the plume with tides. Descriptive statistics suggests that heterogeneity enhances spreading of the plume in the beach in an ensemble sense along with significant spatial and temporal variation. Due to heterogeneity, high spots of the pore water velocity are formed within the recirculation cell, creating transient preferential flow paths in the beach in response to tides. Contours of the Okubo-Weiss (OW) parameter show that coupling with tides, heterogeneity creates vorticity-dominated flow regions and also expands strain-dominated flow regions more downward, indicating complex local-scale mixing in the beach, compared to corresponding homogeneous case. Geologic heterogeneity also alters the spatial extent of the recirculating cell and induces highly variable transit time along the recirculating flow paths. The results provide insights into effects of geologic heterogeneity on seawater-groundwater mixing and associated solute transport processes in tidally influenced coastal aquifers. [ABSTRACT FROM AUTHOR]