Physico-statistical modeling of the hydrodynamic field of the ship on the basis of hydrodynamic calculations

Розглядаються перспективні напрямки розвитку систем автоматизованого проектування кораблів. Показані переваги математичного гідродинамічного моделювання кораблів у порівнянні з фізичним моделюванням гідродинаміки його моделі. Розглянуті методичні проблеми аналізу даних математичного гідродинамічного моделювання кораблів. Представлені методичні рішення та розглянуті конкретні приклади обробки статистичними методами результатів математичного гідродинамічного моделювання кораблів. На базі цифрової моделі корабля досліджено вплив геометрії його корпусу на гідродинамічне поле, розраховане шляхом математичного моделювання глибини розповсюдження аномалій надлишкового тиску в нестискуваній рідині. Розрахунки виконані для швидкості руху корабля 30 вузлів. В результаті розрахунку повного, а також аномального тиску у водному середовищі, що був створений в процесі руху кораблем, була апробована методика фізико-математичного моделювання його гідродинамічного поля. Фізико-статистичне моделювання реалізується за допомогою застосування регресійного аналізу для формалізації емпіричних закономірностей. Отримані результати емпіричних залежностей глибини аномалій надлишкового тиску від швидкості об'єкта. Для району північно-західного шельфу Чорного моря виконано районування акваторії за фактором швидкості руху судна, що забезпечує непомітне переміщення в умовах передбачуваного розгортання донних гідростатичних комплексів моніторингу гідродинамічного поля. За результатами районування акваторії побудована карта. Розрахунки виконані для конкретної моделі корабля і не можуть бути застосовані для суден з відмінною геометрією корпусу. Кожний клас кораблів повинен бути забезпечений окремими розрахунками гідродинамічного поля, що створюється ним в процесі різних режимів руху.The perspective directions of development of systems of automated designing of ships are considered. The advantages of mathematical hydrodynamic modeling of ships in comparison with physical modeling of hydrodynamics of its model are shown. The methodical problems of data analysis of mathematical hydrodynamic modeling of ships are considered. The methodical solutions are presented and concrete examples of statistical methods processing of results of mathematical hydrodynamic modeling of ships are considered. The influence of the geometry of its body on the hydrodynamic field, calculated on the basis of the digital model of the ship, is calculated by mathematical modeling of the depth of propagation of excess pressure anomalies in unchanged fluid. Calculations are made for the speed of ships in 30 knots. As a result of the calculation of the complete, as well as abnormal, exceptional pressure in the aqueous environment created by the moving vehicle, the method of physico-mathematical modeling of its hydrodynamic field was tested. Physical-statistical modeling is implemented by applying regression analysis to formalize empirical regularities. The obtained results of the empirical dependencies of the depth of the anomaly of excess pressure from the object speed. For the area of the northwestern Black Sea shelf, the zoning of the water area was performed on the speed factor of the vessel, which provides a hidden movement in conditions of the planned deployment of bottom hydrostatic monitoring systems for the hydrodynamic field. Based on the results of zoning of the water area, a map was constructed. The calculations are made for a specific ship model and can not to be used for vessels with excellent body geometry. Each class of ships must be provided with separate calculations created by it hydrodynamic field

3D Sonar Measurements in Wakes of Ships of Opportunity

The aim of this work is to test the potential capabilities of 3D sonar technology for studying small-scale processes in the near-surface layer of the ocean, using the centerline wake of ships of opportunity as the object of study. The first tests conducted in Tampa Bay, Florida, with the 3D sonar have demonstrated the ability of this technology to observe the shape of the centerlinewake in great detail starting from centimeter scale, using air bubbles as a proxy. An advantage of the 3Dsonar technology is that it allows quantitative estimates of the ship wake geometry, which presents new opportunities for validation of hydrodynamic models of the ship wake. Three-dimensional sonar is also a potentially useful tool for studies of air-bubble dynamics and turbulence in breaking surface waves

DNA Analysis of Surfactant-Associated Bacteria in a Natural Sea Slick Observed by TerraSAR-X and RADARSAT-2 Over the Gulf of Mexico

The damping of short gravity-capillary waves (Bragg waves) due to surfactant accumulation under low wind speed conditions results in the formation of natural sea slicks. These slicks are detectable visually and in synthetic aperture radar (SAR) imagery. Surfactants are produced by natural life processes of many organisms, such as bacteria, phytoplankton, seaweed, and zooplankton. By using DNA analysis, we are able to determine the relative abundance of surfactant-associated bacteria in the sea surface microlayer and the subsurface water column. A method to reduce contamination of samples during collection, storage, and analysis (Kurata et al., 2016; Hamilton et al., 2015) has been implemented and advanced by increasing the number of successive samples and changing sample storage procedures. In this work, microlayer samples have been collected in the Gulf of Mexico during a research cruise (LASER) on the R/V F.G. Walton Smith during RADARSAT-2 and TerraSAR-X overpasses. We found that in slick areas surfactant-associated bacteria mostly reside in subsurface waters, producing surfactants, which move to the surface, accumulate on and enrich the sea surface microlayer. This is consistent with previous studies (Kurata et al., 2016; Hamilton et al., 2015) and with the experimental results of Cunliffe et al. (2010)

Fine-Scale Features on the Sea Surface in SAR Satellite Imagery - Part 2: Numerical Modeling

With the advent of the new generation of synthetic aperture radar (SAR) satellites, it has become possible to resolve fine-scale features on the sea surface on the scale of meters. The proper identification of sea surface signatures in SAR imagery can be challenging, since some features may be due to atmospheric distortions (gravity waves, squall lines) or anthropogenic influences (slicks), and may not be related to dynamic processes in the upper ocean. In order to improve our understanding of the nature of fine-scale features on the sea surface and their signature in SAR, we have conducted high-resolution numerical simulations combining a three-dimensional non-hydrostatic computational fluid dynamics model with a radar imaging model. The surface velocity field from the hydrodynamic model is used as input to the radar imaging model. The combined approach reproduces the sea surface signatures in SAR of ship wakes, low-density plumes, and internal waves in a stratified environment. The numerical results are consistent with observations reported in a companion paper on in situ measurements during SAR satellite overpasses. Ocean surface and internal waves are also known to produce a measurable signal in the ocean magnetic field. This paper explores the use of computational fluid dynamics to investigate the magnetic signatures of oceanic processes. This potentially provides a link between SAR signatures of transient ocean dynamics and magnetic field fluctuations in the ocean. We suggest that combining SAR imagery with data from ocean magnetometers may be useful as an additional maritime sensing method. The new approach presented in this work can be extended to other dynamic processes in the upper ocean, including fronts and eddies, and can be a valuable tool for the interpretation of SAR images of the ocean surface

Relative Abundance of Bacillus spp., Surfactant-Associated Bacterium Present in a Natural Sea Slick Observed by Satellite SAR Imagery over the Gulf of Mexico

The damping of short gravity-capillary waves (Bragg waves) due to surfactant accumulation under low wind speed conditions results in the formation of natural sea slicks. These slicks are detectable visually and in synthetic aperture radar satellite imagery. Surfactants are produced by natural life processes of many marine organisms, including bacteria, phytoplankton, seaweed, and zooplankton. In this work, samples were collected in the Gulf of Mexico during a research cruise on the R/V F.G. Walton Smith to evaluate the relative abundance of Bacillus spp., surfactant-associated bacteria, in the sea surface microlayer compared to the subsurface water at 0.2 m depth. A method to reduce potential contamination of microlayer samples during their collection on polycarbonate filters was implemented and advanced, including increasing the number of successive samples per location and changing sample storage procedures. By using DNA analysis (real-time polymerase chain reaction) to target Bacillus spp., we found that in the slick areas, these surfactant-associated bacteria tended to reside mostly in subsurface waters, lending support to the concept that the surfactants they may produce move to the surface where they accumulate under calm conditions and enrich the sea surface microlayer

Relative Abundance of Bacillus spp., Surfactant-Associated Bacterium Present in a Natural Sea Slick Observed by Satellite SAR Imagery over the Gulf of Mexico

The damping of short gravity-capillary waves (Bragg waves) due to surfactant accumulation under low wind speed conditions results in the formation of natural sea slicks. These slicks are detectable visually and in synthetic aperture radar satellite imagery. Surfactants are produced by natural life processes of many marine organisms, including bacteria, phytoplankton, seaweed, and zooplankton. In this work, samples were collected in the Gulf of Mexico during a research cruise on the R/V F.G. Walton Smith to evaluate the relative abundance of Bacillus spp., surfactant-associated bacteria, in the sea surface microlayer compared to the subsurface water at 0.2 m depth. A method to reduce potential contamination of microlayer samples during their collection on polycarbonate filters was implemented and advanced, including increasing the number of successive samples per location and changing sample storage procedures. By using DNA analysis (real-time polymerase chain reaction) to target Bacillus spp., we found that in the slick areas, these surfactant-associated bacteria tended to reside mostly in subsurface waters, lending support to the concept that the surfactants they may produce move to the surface where they accumulate under calm conditions and enrich the sea surface microlayer

DNA Analysis of Surfactant-Associated Bacteria in a Natural Sea Slick in the Gulf of Mexico Observed by TerraSAR-X

Under low wind speed conditions, surfactants accumulate at the air-sea interface, dampen short-gravity capillary (Bragg) waves, and form natural sea slicks that are detectable visually and in synthetic aperture radar (SAR) imagery. Marine organisms, such as phytoplankton, zooplankton, seaweed, and bacteria, produce and degrade surfactants during various life processes. This study coordinates in situ sampling with TerraSAR-X satellite overpasses in order to help guide microbiological analysis of the sea surface microlayer (SML) and associated subsurface water (SSW). Samples were collected in the Gulf of Mexico during a research cruise (LASER) in February 2016 to determine abundance of surfactant associated bacteria in the sea surface microlayer and subsurface water column. By using real time polymerase chain reaction (quantitative PCR, or qPCR) to target Bacillus spp. associated with surfactant production, results indicate that more surfactant-associated bacteria reside in the subsurface water in low wind speed conditions. Sequencing results suggest that Bacillus and Pseudomonas are more abundant in the SSW in low wind speed conditions. These results indicate that these bacteria reside in the SSW, presumably producing surfactants that move to the surface via physical processes, accumulate on and enrich the sea surface microlayer

Deep learning for deep waters: An expert-in-the-loop machine learning framework for marine sciences

Driven by the unprecedented availability of data, machine learning has become a pervasive and transformative technology across industry and science. Its importance to marine science has been codified as one goal of the UN Ocean Decade. While increasing amounts of, for example, acoustic marine data are collected for research and monitoring purposes, and machine learning methods can achieve automatic processing and analysis of acoustic data, they require large training datasets annotated or labelled by experts. Consequently, addressing the relative scarcity of labelled data is, besides increasing data analysis and processing capacities, one of the main thrust areas. One approach to address label scarcity is the expert-in-the-loop approach which allows analysis of limited and unbalanced data efficiently. Its advantages are demonstrated with our novel deep learning-based expert-in-the-loop framework for automatic detection of turbulent wake signatures in echo sounder data. Using machine learning algorithms, such as the one presented in this study, greatly increases the capacity to analyse large amounts of acoustic data. It would be a first step in realising the full potential of the increasing amount of acoustic data in marine sciences