Wave Analysis for Offshore Aquaculture Projects: A Case Study for the Eastern Mediterranean Sea

The investigation of wave climate is of primary concern for the successful implementation of offshore aquaculture systems as waves can cause significant loads on them. Up until now, site selection and design (or selection) of offshore cage system structures on extended sea areas do not seem to follow any specific guidelines. This paper presents a novel methodology for the identification of favorable sites for offshore aquaculture development in an extended sea area based on two important technical factors: (i) the detailed characterization of the wave climate, and (ii) the water depth. Long-term statistics of the significant wave height, peak wave period, and wave steepness are estimated on an annual and monthly temporal scale, along with variability measures. Extreme value analysis is applied to estimate the design values and associated return periods of the significant wave height; structures should be designed based on this data, to avoid partial or total failure. The Eastern Mediterranean Sea is selected as a case study, and long-term time series of wave spectral parameters from the ERA5 dataset are utilized. Based on the obtained results, the most favorable areas for offshore aquaculture installations have been identified.

A Laser Vision System for Relative 3-D Posture Estimation of an Underwater Vehicle with Hemispherical Optics

This paper describes the development and experimental validation of algorithms for a novel laser vision system (LVS), suitable for measuring the relative posture from both solid and mesh-like targets in underwater environments. The system was developed in the framework of the AQUABOT project, a research project dedicated to the development of an underwater robotic system for inspection of offshore aquaculture installations. In particular, an analytical model for three-medium refraction that takes into account the nonlinear hemispherical optics for image rectification has been developed. The analytical nature of the model allows the online estimation of the refractive index of the external medium. The proposed LVS consists of three line-lasers within the field of view of the underwater robot camera. The algorithms that have been developed in this work provide appropriately filtered point-cloud datasets from each laser, as well as high-level information such as distance and relative orientation of the target with respect to the ROV. In addition, an automatic calibration procedure, along with the accompanying hardware for the underwater laser vision system has been developed to reduce the calibration overhead required by regular maintenance operations for underwater robots operating in seawater. Furthermore, a spatial image filter was developed for discriminating between mesh and non-mesh-like targets in the LVS measurements. Finally, a set of experiments was carried out in a controlled laboratory environment, as well as in real conditions at offshore aquaculture installations demonstrating the performance of the system.

Extraction of Offshore Aquaculture Areas from Medium-Resolution Remote Sensing Images Based on Deep Learning

It is important for aquaculture monitoring, scientific planning, and management to extract offshore aquaculture areas from medium-resolution remote sensing images. However, in medium-resolution images, the spectral characteristics of offshore aquaculture areas are complex, and the offshore land and seawater seriously interfere with the extraction of offshore aquaculture areas. On the other hand, in medium-resolution images, due to the relatively low image resolution, the boundaries between breeding areas are relatively fuzzy and are more likely to ‘adhere’ to each other. An improved U-Net model, including, in particular, an atrous spatial pyramid pooling (ASPP) structure and an up-sampling structure, is proposed for offshore aquaculture area extraction in this paper. The improved ASPP structure and up-sampling structure can better mine semantic information and location information, overcome the interference of other information in the image, and reduce ‘adhesion’. Based on the northeast coast of Fujian Province Sentinel-2 Multispectral Scan Imaging (MSI) image data, the offshore aquaculture area extraction was studied. Based on the improved U-Net model, the F1 score and Mean Intersection over Union (MIoU) of the classification results were 83.75% and 73.75%, respectively. The results show that, compared with several common classification methods, the improved U-Net model has a better performance. This also shows that the improved U-Net model can significantly overcome the interference of irrelevant information, identify aquaculture areas, and significantly reduce edge adhesion of aquaculture areas.

A CFD Approach for Modelling the Fluid-Structure Interaction of Offshore Aquaculture Cages and Waves

Open ocean aquaculture cages became recently a promising alternative to traditional fish cage designs. The offshore environment implies larger loads on the structures and higher risk of fish loss. Floating rigid aquaculture cages with stiff nets are considered as a possible solution to cope with these new challenges. Their design process requires more advanced tools to account for the non-linear fluid-structure interaction. This paper presents a suitable numerical approach for analysing the interaction of offshore aquaculture cages and waves using Computational Fluid Dynamics. Here, a numerical wave tank accounts for the accurate propagation of the waves, and structural dynamics solutions are utilised for the cage system. Two-way coupling is enabled by accounting for the influence of the net on the fluid. The numerical model is validated against measurements for the loads on and the responses of a mobile floating fish farm in waves and current.

A CFD Approach for Modelling the Fluid-Structure Interaction of Offshore Aquaculture Cages and Waves

Open ocean aquaculture cages became recently a promising alternative to traditional fish cage designs. The offshore environment implies larger loads on the structures and higher risk of fish loss. Floating rigid aquaculture cages with stiff nets are considered as a possible solution to cope with these new challenges. Their design process requires more advanced tools to account for the non-linear fluid-structure interaction. This paper presents a suitable numerical approach for analysing the interaction of offshore aquaculture cages and waves using Computational Fluid Dynamics. Here, a numerical wave tank accounts for the accurate propagation of the waves, and structural dynamics solutions are utilised for the cage system. Two-way coupling is enabled by accounting for the influence of the net on the fluid. The numerical model is validated against measurements for the loads on and the responses of a mobile floating fish farm in waves and current.