The purpose of this project is therefore to develop a software tool that can implement an automated intelligent registration (artificial intelligence) of the catch of cod on board the vessel. The project can both support the ongoing camera projects, but also functions as a forward-looking method where the concept of this approach is that the camera focuses on the catch and can be implemented without human supervision. This has a number of potential advantages, including that human supervision is avoided, the number of cameras can probably be reduced to just one (although possibly a stereo camera), labor resources are saved by automated monitoring, it will be possible to reduce the amount of data, fishermen can target selective fishing based on the information obtained, increased precision in relation to possible legal
use of the observations and overall it will reduce costs. The project supports the monitoring that has been initiated in the Kattegat, but should also be seen as a future development, including internationally, where the focus is on building monitoring/surveillance around the use of images as documentation of the catch. An extremely important element of the project is to create a high-quality dataset that can be used internationally to improve algorithms and intensify research.
A major challenge in next-generation industrial applications is to improve numerical analysis by quantifying uncertainties in predictions. In this work we present a formulation of a fully nonlinear and dispersive potential flow water wave model with random inputs for the probabilistic description of the evolution of waves. The model is analyzed using random sampling techniques and nonintrusive methods based on generalized polynomial chaos (PC). These methods allow us to accurately and efficiently estimate the probability distribution of the solution and require only the computation of the solution at different points in the parameter space, allowing for the reuse of existing simulation software. The choice of the applied methods is driven by the number of uncertain input parameters and by the fact that finding the solution of the considered model is computationally intensive. We revisit experimental benchmarks often used for validation of deterministic water wave models. Based on numerical experiments and assumed uncertainties in boundary data, our analysis reveals that some of the known discrepancies from deterministic simulation in comparison with experimental measurements could be partially explained by the variability in the model input. Finally, we present a synthetic experiment studying the variance-based sensitivity of the wave load on an offshore structure to a number of input uncertainties. In the numerical examples presented the PC methods exhibit fast convergence, suggesting that the problem is amenable to analysis using such methods.
An increasing water to oil ration in the North Sea oil and gas production motivates for an optimization of the current deoiling facilities. Current facilities are operated on matured methodologies, which in most cases fulfill the government regulations. However, it has also observed that these solutions could be further improved. In order to more precisely monitor the deoiling operations, this study investigated the real-time monitoring of the deoiling efficiency of the hydrocyclone facilities which are commonly used in offshore oil and gas production. Fluorescence based monitors were applied to measure hydrocyclone inlet's and underflow's Oil-in-Water (OiW) concentrations in real-time. Image-based microscopy was used to analyze the oil droplet size distribution at inlet and underflow to investigate the droplets' influence on hydrocyclone's efficiency. Performance experiments were carried out to identify how pressure difference ratio (PDR) and the droplet's sizes affect the deoiling efficiency. The performance of the deoiling hydrocyclone was significantly influenced by the inlet flow rate, while less or marginally dependent on the PDR. The droplet size distribution experiment proved that large droplets have a high probability to be separated by the hydrocyclone. The findings suggest that the coupled separator tank and hydrocyclone system can be further improved upon by deploying coordinated control as the two systems are strongly interdependent.
Results from Blind Test Series 1, part of the Collaborative Computational Project in Wave Structure Interaction (CCP-WSI), are presented. Participants, with a range of numerical methods, blindly simulate the interaction between a fixed structure and focused waves ranging in steepness and direction. Numerical results are compared against corresponding physical data. The predictive capability of each method is assessed based on pressure and run-up measurements. In general, all methods perform well in the cases considered, however, there is notable variation in the results (even between similar methods). Recommendations are made for appropriate considerations and analysis in future comparative studies.
The design of wave energy converters should rely on numerical models that are able to estimate accurately the dynamics and loads in extreme wave conditions. A high-fidelity CFD model of a 1:30 scale point-absorber is developed and validated on experimental data. This work constitutes beyond the state-of-the-art validation study as the system is subjected to 50-year return period waves. Additionally, a new methodology that addresses the well-known challenge in CFD codes of mesh deformation is successfully applied and validated. The CFD model is evaluated in different conditions: wave-only, free decay, and wave–structure interaction. The results show that the extreme waves and the experimental setup of the wave energy converter are simulated within an accuracy of 2%. The developed high-fidelity model is able to capture the motion of the system and the force in the mooring line under extreme waves with satisfactory accuracy. The deviation between the numerical and corresponding experimental RAOs is lower than 7% for waves with smaller steepness. In higher waves, the deviation increases up to 10% due to the inevitable wave reflections and complex dynamics. The pitch motion presents a larger deviation, however, the pitch is of secondary importance for a point-absorber wave energy converter.
The International Energy Agency Technology Collaboration Program for Ocean Energy Systems (OES) initiated the OES Wave Energy Conversion Modeling Task, which focused on the verification and validation of numerical models for simulating wave energy converters (WECs). The long-term goal is to assess the accuracy of and establish confidence in the use of numerical models used in design as well as power performance assessment of WECs. To establish this confidence, the authors used different existing computational modeling tools to simulate given tasks to identify uncertainties related to simulation methodologies: (i) linear potential flow methods; (ii) weakly nonlinear Froude–Krylov methods; and (iii) fully nonlinear methods (fully nonlinear potential flow and Navier–Stokes models). This article summarizes the code-to-code task and code-to-experiment task that have been performed so far in this project, with a focus on investigating the impact of different levels of nonlinearities in the numerical models. Two different WECs were studied and simulated. The first was a heaving semi-submerged sphere, where free-decay tests and both regular and irregular wave cases were investigated in a code-to-code comparison. The second case was a heaving float corresponding to a physical model tested in a wave tank. We considered radiation, diffraction, and regular wave cases and compared quantities, such as the WEC motion, power output and hydrodynamic loading.
In this article, we develop a deep neural network model to estimate the wave added resistance. The required data to train the model is generated using strip theory calculations over a wide range of hull geometries and operational conditions. The model is efficient as it only requires the ship’s main particulars: length, beam, draft, block coefficient, and slenderness ratio. In addition, we present an application of this model in a vessel performance framework. This will be used for predicting propulsion power and analyzing the degree of biofouling on ships from the company Ultrabulk2. The study shows that the developed deep neural network model produces reliable results in predicting the added wave resistance coefficient in comparison to strip theory calculations. Also, the developed ship propulsion and biofouling analysis display satisfactory output for monitoring hull performance under actual ship operational conditions.
Savonius hydrokinetic turbines (SHTs), categorized as emerging cyclic-type wave energy converters (WECs), have demonstrated notable potential in achieving elevated energy conversion efficiency and consistent power output. This performance is particularly observed when operating under the initial phase-locked strategy (IPLS), marking a significant advancement in the realm of wave energy harvesting. However, a thorough exploration of the influences stemming from wave conditions and turbine design remains an area that warrants further investigation for advancing the performance of SHT-WECs under the proper operational strategy. This study undertakes an exhaustive analysis of geometric parameters, encompassing turbine diameter, blade number, and thickness. An experiment-validated numerical model based on the unsteady two-phase Reynolds-averaged Navier-Stokes equations is adopted in the research. Comprehensive investigations include analyzes of flow fields around the turbine, pressure distributions on blade surfaces, and dynamic torque variations. These analyzes serve to elucidate the variation rules of hydrodynamic characteristics and their influential mechanisms. The results highlight the notable impact of the proposed "relative-short wavelength impact" on the performance of SHT-WECs operating under IPLS conditions. Notably, no significant impact is observed when the relative wavelength exceeds 17. Optimal performance is achieved with the thinnest and two-bladed turbine configuration. Moreover, optimizing the turbine diameter significantly enhances SHT-WEC conversion efficiency, with the attained maximum value reaching approximately 18.6%. This study offers a concise guideline for designing turbine diameters in alignment with specific wave conditions.
Autonomous unmanned underwater vehicles (UUVs) play a vital role in diverse underwater operations; localization is of great interest for UUVs mirroring the trend seen in self-driving surface and aerial vehicles. Unlike their land and aerial counterparts, underwater environments lack reliable Global Navigation Satellite Systems (GNSS) due to radio wave attenuation in water. Hence, alternative localization methods are imperative for both navigation and operational purposes. This study thoroughly reviews sensor technologies for underwater localization, including sonar, Doppler velocity log, cameras, and more. Different operations necessitate distinct localization accuracies and vehicle and sensor choices. Environmental factors, such as turbidity, waves, and sound disturbances, impact sensor performance. Conclusions are given on the coincidence between operational requirements and sensor specifications, with special attention to the open concerns. These considerations include aspects such as the line of sight for acoustic positioning systems and the requirement for a feature-rich environment for visual sensors. Lastly, a prediction for the future of underwater localization is given, where the tendencies indicate lower costs for sensors, making operation-specific vehicles more attractive, which aligns with an increased demand for cost-efficient autonomous offshore operations.
The maritime industry is a dangerous and highly technologicallysaturated sector. Unfortunately, advancement in automation and technologyhave not minimised human error as intended. Interaction between humansand technology in the industry is also overtly pre-scripted. The main reasonfor this is to reduce human error by ensuring predictability in interaction.Ultimately, investigations of non-routine interaction are often based on a hind-sight view of what went wrong in a given situation. This article analyses acollection of non-routine interactions that derive from a larger data corpus,using Discursive Psychology and Conversation Analysis. It argues that such astudy can capture what is missing from some investigations, namely, whatmakes sense for crews in the context of a given non-routine situation. Despitethe constraints and the challenges of technological complexity, this articleargues that reframing psychological matters in non-routine technologicallymediated interaction can be a new way of showing how such matters aredynamic, visible and manageable. This can inform the general debate of howto minimise human error, and more specifically, provide insight into the increas-ing inclusion of technology and as a consequence, the equally increasingamount of technologically mediated interaction that we will see in the future.