Offshore wind energy is a sustainable renewable energy source that is acquired by harnessing the force of the wind offshore, where the absence of obstructions allows the wind to travel at higher and more steady speeds. Offshore wind has recently grown in popularity because wind energy is more powerful offshore than on land. Prior to the development of floating structures, wind turbines could not be deployed in particularly deep or complicated seabed locations since they were dependent on fixed structures. With the advent of floating structures, which are moored to the seabed using flexible anchors, chains, or steel cables, wind turbines can now be placed far offshore. The deployment of floating wind turbines in deep waters is encouraged by several benefits, including steadier winds, less visual impact, and flexible acoustic noise requirements. A thorough understanding of the physics underlying the dynamic response of the floating offshore wind turbines, as well as various design principles and analysis methods, is necessary to fully compete with traditional energy sources such as fossil fuels. The present work offers a comprehensive review of the most recent state-of-the-art developments in the offshore wind turbine technology, including aerodynamics, hydromechanics, mooring, ice, and inertial loads. The existing design concepts and numerical models used to simulate the complex wind turbine dynamics are also presented, and their capabilities and limitations are discussed in detail.

Over the last few decades, a range of instruments and vehicles have been used to monitor the oceans. One example is the use of autonomous underwater vehicles to perform ocean surveys, and within this group, autonomous underwater gliders have made their mark. Gliders enable the scientist to make extended complex studies on topics such as the effect of metals, pesticides, and nutrients on fish abundance, reproductive success, and ability to feed or on contaminants such as chemicals or biological toxins that are transported in particulate form and may become incorporated into living organisms (plankton, bivalves, and fish) or become deposited in bottom sediments. With these vehicles, the scientist or environmentalist can detect hazardous substances in the ocean such as chemicals from an oil spill or toxic algae such as red tide.

A new generation of autonomous underwater gliders is currently being developed for the fields of marine biology, physical oceanography, and marine environmental studies, assessment and management at the Florida Institute of Technology, Melbourne Florida, USA. The end-product of the project will be an autonomous powered glider that collects video, acoustic, and physical data, simultaneously obtains water samples for chemical analysis through mass-spectrum analysis, and is able to gather biological specimens to improve and automate inspection and sampling of the marine environment. The powered glider will be programmed for surveying and analyzing specific phenomenons. During the project several scientific surveys will be performed to test the abilities of the new autonomous underwater vehicle (AUV). For example, the assessment of marine invertebrate larvae and zooplankton (e.g. Krill - Euphausia superba) will be investigated from samples that are collected or photographed. All operations will be completely autonomous with the exception of the high level communication between the AUV and the support vessel. The AUV will contain a specially developed modular measurement unit containing various sensors to determine conductivity, temperature, depth, turbidity, and other sensors to detect various chemicals such as chlorophyll.

The detection, classification, and recognition of underwater acoustic features have always been of the highest importance for scientific, fisheries, and defense interests. Recent efforts in improved passive sonar techniques have only emphasized this interest. In this paper, the authors describe the use of novel, hybrid neural approaches using both unsupervised and supervised network topologies. Results are presented which demonstrate the ability of the network to classify biological, man made, and geological sources. Also included are capabilities of the networks to attack the more difficult problems of identifying the complex vocalizations of several fish and marine mammalian species. Basic structure, processor requirements, training and operational methodologies are described as well as application to autonomous observation and vehicle platforms.

A new type of surface vehicle/buoy is needed for marine biological studies, physical oceanography, environmental impact assessment and integrated marine environment management. It is proposed within this document to present the development of an autonomous self-mooring vehicle (ASMV) that collects meteorological, acoustic, and physical data, while simultaneously obtaining water samples for chemical analysis through mass-spectrum analysis from littoral and estuary waters. The ASMV is programmed to use and analyze the oceanographic and meteorological data in its guidance system to track and analyze a specific phenomenon. During the project several scientific surveys will be performed to test the abilities of the vehicle. For example, the assessment of the biodiversity of organisms (e.g., phytoplankton) that live at a specific region will be conducted. All operations will be completely autonomous with the exception of the high level communication between the vehicle and the operations center. The vehicle houses commercial and student designed measurement units containing instruments such as multi-chemistry analyzer, conductivity, temperature, depth gages, and a fluorimeter that detects various chemicals such as chlorophyll, rhodamine, and fluorescein.