Due to increased numbers of offshore structures and subsea cables, there is a high demand for underwater maintenance and monitoring. Common options to meet this demand are sonar mapping and imaging. Sonar mapping provides a reliable way for object detection with a high penetration depth, but it is not suitable for tasks that require a detailed insight into the material composition and color of the object. Imaging can provide in-depth, comprehensive information on material properties and external features. This makes it reasonable to investigate its use for object segmentation. Hyperspectral imaging is a subset of imaging which proved to be more effective for airborne object segmentation compared to RGB imaging. This stems from the fact that hyperspectral imaging contains a higher number of spectral bands, justifying the investigation of its applicability in underwater environments. However, underwater imaging faces major challenges such as a variable data quality which is strongly affected by water turbidity, color distortion and a narrow wavelength transmission window. Most of the prior studies conducted on underwater object segmentation relied on RGB images, such as the work carried out by AAU Energy on object segmentation relying on synthetic data [1]. The applicability of hyperspectral reliant object segmentation underwater is yet to be conclusively defined, however, the promising results obtained in airborne conditions are an encouraging prospect. The contribution of this paper is to investigate the applicability of hyperspectral data for underwater object segmentation. In particular, a segmentation algorithm, evaluated in an artificial environment, was researched.
The integration of offshore wind assets with green hydrogen production and storage units can offer a much-needed solution for intermittency and curtailment issues of the offshore energy industry. To gain confidence that such novel integrated assets will be fit for purpose, the present study presents a comprehensive risk assessment followed by an action plan to mitigate the identified risks to help facilitate their technology qualification. The new methodology introduced here involves all the life-cycle phases of an offshore green hydrogen production system. Following, prevailing failure modes, their effects, and their causes are identified through an extensive review of relevant literature. Subsequently, risk prioritization is performed by ranking the criticality scores obtained from a multidisciplinary group of experts to the questionnaire designed to reveal the chosen subsystems' technology readiness, degree of change, concern in manufacturing and operation, and potential consequences regarding occupational health, safety, environment, economic and regulatory.
For design validation of offshore structures and conceptualisation of wave energy converters, physical model testing performed in wave basin laboratories is often applied. In such cases, knowledge about the wave conditions is of great significance. For validation of the wave condition in such tests, different methods for estimation of the directional wave spectra may be applied. However, different assumptions are imposed in the methods and deviations here from providing uncertainties in the results. The following paper quantifies the influence of nonlinear effects on the accuracy of the estimated directional wave spectra. This is done by analysis of idealised, synthetically generated waves based on second order wave theory and secondly with simplified amplitude dispersion included. The present analyzes show that the uncertainties of the directional wave spectra are proportional to the level of nonlinearity present in the wave field.