Variable buoyancy anchor deployment analysis for floating wind applications using a Marine Simulator

The research presented in this paper has been primarily sponsored by EPSRC’s Supergen ORE Hub & ORE Catapult Floating Offshore Wind Centre of Excellence (grant number FF2021-1040). The authors acknowledge funding received from Energy Technology Partnership Knowledge Exchange Network scheme (grant number PR057-ME) that provided additional funding to support this work. The authors wish to thank Oceanetics Inc. and Aubin Group for their support towards this project. This work has benefited from the support and funding received from Net Zero Technology Centre and The University of Aberdeen through their partnership in The National Decommissioning Centre (NDC) and The Scottish Government’s Decommissioning Challenge Fund in part-funding the establishment of the Marine Simulator research facility at the NDC.Peer reviewedPublisher PD

Ocean Wave Rendering with Whitecap in the Visual System of a Maritime Simulator

The whitecap is an important oceanographic phenomenon. However, existing whitecap rendering methods do not successfully generate realistic whitecaps. To solve this problem, this paper presents a real-time whitecap rendering method applied to the visual system of a maritime simulator. The method takes the vertical acceleration on the wave crest as the criterion of whitecap generation. The Fourier coefficient of the vertical acceleration is provided, and a continuous mathematical model computing the whitecap coverage is built. The vertical acceleration is the variable of the model. The life time of the whitecap’s existence can be controlled by the parameter of the model, and the parameter is computed with the genetic algorithm. The average of the computed whitecap coverage is equal to the whitecap coverage computed by the stochastic method and is close to the whitecap coverage computed by the empirical formula. The whitecap coverage is used as the blending factor to blend the pixel color of the whitecap texture and that of the sea surface. The presented method has sound theoretical support, with small computational complexity. The rendered whitecap is closer to the description of the Beaufort wind force scale than before

Using piloted simulation to measure pilot workload of landing a helicopter on a small ship

When conducting landings to a ship's deck in strong winds, helicopter pilot workload is often dominated by the turbulence within the ship's airwake. Previous studies have shown that larger ships create more aggressive airwakes and simulated flight trials had shown that it can be easier to land to a smaller ship than a large one. However, there are helicopter-enabled ships that are less than 100m in length and these will have significantly greater ship motion in rough seas than a large ship. The study reported in this paper has used a motion-base flight simulator to evaluate the pilot workload when landing to three geometrically similar ships of lengths 100m, 150m and 200m. Ship motion software has been used to create realistic deck displacements for sea states 4, 5 and 6, which are consistent with the increasing wind speed over the deck. It has been shown that the 100m ship was the most difficult to land to, with deck motion being the limiting factor. The next most difficult ship to land to was the 200m ship, with airwake turbulence being the limiting factor. The 150m ship generated the lowest pilot workload. The study has demonstrated that when ship motion is excessive, as it will be with small ships in rough seas, pilot workload will be dominated by deck motion during a landing task, but as the ship gets larger and more stable, airwake disturbances will dominate. It is clear from this study that realistic ship motion is essential when using piloted flight simulation to conduct simulated ship-helicopter operations

Amplitude Malformation in the IFFT Ocean Wave Rendering under the Influence of the Fourier Coefficient

Although Tessendorf’s IFFT Gerstner wave model has been widely used, the value of A, a constant of the Fourier coefficient, is not given. A will strongly influence the shape of the rendered ocean wave and even cause amplitude malformation. We study the algorithm of the IFFT Gerstner wave, and give the method of A calculating. The method of the paper can guarantee there is no amplitude malformation in rendered ocean waves. The expression of the IFFT Gerstner wave with the amplitude of the cosine wave is derived again. The definite integral of the wave number spectrum is discretized. Further, another expression of the IFFT Gerstner wave is gotten. The Fourier coefficient of the expression contains the wave number spectrum and the area of the discrete integral domain. The method makes the shape of the generated wave stable. Comparing Tessdendorf’s method with the method of the paper, we find that the expression of A should contain the area of the discrete integral domain and the spectral constant of the wave number spectrum. If A contains only the spectral constant, the amplitude malformation may occur. By reading some well known open source codes, we find that the code authors adopted some factitious methods to suppress the malformed amplitude Obviously, the code authors have already noticed the phenomenon of the malformation, but not probed the cause. The rendering results of the codes are close to that of the method of the paper. Furthermore, the wave potential is computed using the Gerstner wave model directly, the author find it is quite close to that of the paper. The experimental results and comparisons show that the method of the paper correctly computes the wave potential and effectively solves the problem of amplitude malformation

Electromagnetic backscatter modelling of icebergs at c-band in an ocean environment

This thesis outlines the development of an electromagnetic (EM) backscatter model of icebergs. It is a necessary first step for the generation of in-house synthetic aperture radar (SAR) data of icebergs to support optimum iceberg/ship classifier design. The EM modelling was developed in three stages. At first, an EM backscatter model was developed to generate simulated SAR data chips of iceberg targets at small incidence angles. The model parameters were set to mimic a dual polarized dataset collected at C-Band with the Sentinel-1A satellite. The simulated SAR data chips were compared with signatures and radiometric properties of the satellite data, including total radar cross section (TRCS). A second EM model was developed to mimic the parameters of a second SAR data collection with RADARSAT-2; this second data collection was at larger incidence angles and was fully polarimetric (four channels and interchannel phase). The full polarimetric SAR data allowed for a comparison of modelled TRCS and polarimetric decompositions. Finally, the EM backscatter models were tested in the context of iceberg/ship classification by comparing the performance of various computer vision classifiers using both simulated and real SAR image data of iceberg and vessel targets. This step is critical to check the compatibility of simulated data with the real data, and the ability to mix real and simulated SAR imagery for the generation of skilled classifiers. An EM backscatter modelling tool called GRECOSAR was used for the modelling work. GRECOSAR includes the ability to generate small scenes of the ocean using Pierson-Moskowitz spectral parameters. It also allows the placement of a 3D target shape into that ocean scene. Therefore, GRECOSAR is very useful for simulating SAR targets, however it can only model single layer scattering from the targets. This was found to be limiting in that EM scattering throughout volume of the iceberg could not be generated. This resulted in EM models that included only surface scattering of the iceberg. In order to generate realistic SAR scenes of icebergs on the ocean, 3D models of icebergs were captured in a series of field programs off the coast of Newfoundland and Labrador, Canada. The 3D models of the icebergs were obtained using a light detection and ranging (LiDAR) and multi-beam sonar data from a specially equipped vessel by a team of C-CORE. While profiling the iceberg targets, SAR images from satellites were captured for comparison with the simulated SAR images. The analysis of the real and simulated SAR imagery included comparisons of TRCS, SAR signature morphology and polarimetric decompositions of the targets. In general, these comparisons showed a good consistency between the simulated and real SAR scene. Simulations were also performed with varying target orientation and sea conditions (i.e., wind speed and direction). A wide variability of the TRCS and SAR signature morphology was observed with varying scene parameters. Icebergs were modelled using a high dielectric constant to mimic melting iceberg surfaces as seen during field work. Given that GRECOSAR could only generate surface backscatter, a mathematical model was developed to quantify the effect of melt water on the amount of surface and volume backscatter that might be expected from the icebergs. It was found that the icebergs in a high state of melt should produce predominantly surface scatter, thus validating the use of GRECOSAR for icebergs in this condition. Once the simulated SAR targets were validated against the real SAR data collections, a large dataset of simulated SAR chips of ships and icebergs were created specifically for the purpose of target classification. SAR chips were generated at varying imaging parameters and target sizes and passed on to an iceberg/ship classifier. Real and simulated SAR chips were combined in varying quantities (or targets) resulting in a series of different classifiers of varying skill. A good agreement between the classifier’s performance was found. This indicates the compatibility of the simulated SAR imagery with this application and provides an indication that the simulated data set captures all the necessary physical properties of icebergs for ship and iceberg classification

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Earth Resources: A continuing bibliography with indexes, issue 16, January 1978

This bibliography lists 543 reports, articles, and other documents introduced onto the NASA scientific and technical information system between October 1 and December 31, 1977. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Navigation in the Arctic. How can simulator training be used for assessment and reduction of risk?

Over the recent years, the ship traffic in the polar areas has increased. There is reason to believe that this traffic, and especially the cruise traffic, will increase further as the ice retracts towards the poles. There is also reason to believe that with the continued focus and exposure of the Polar Region, the cruise tourism to the region will grow.The increased presence in the polar areas will create positive repercussions for several actors, both on sea and land. There will however also be negative consequences associated with the growing presence in the polar areas. Vessels will be operating with long distance to other vessels and land infrastructures. These vessels will also be operating in climate and conditions that will put extra pressure on both vessel and crew. These challenges need to be solved in order for the ship industry to operate safely in the Polar Region. The thesis is focused on navigation in the Arctic, and especially how the use of simulator exercises can be used for assessment and reduction of risk. The first part of the thesis is related to study of literature as a method for collecting theory and background information for the thesis. The theoretical basis is then used for performing a preliminary hazard analysis for navigation in the Arctic. Based on the results from the analysis it is described how simulator training can be used as a risk-reducing measure for operation in the Arctic. It is also described for which hazards simulator training is an effective measure and for which hazards other techniques will be more useful