Project

Understanding the marine environment is a key component to a more sustainable Earth. Technologies to automate data collection and analysis of the marine environment are necessary. Underwater cameras and AI (here in the form of computer vision algorithms) are predicted to play major roles in this regard. This research project takes its starting point in a recently established underwater camera setup that captures video in various conditions. The project aim is an underwater computer vision system that can estimate the visibility, prune the massive amounts of video so only images containing marine organism remains, and finally classify the marine organisms.

Floating offshore wind turbines (FOWT) is a new technology, which is still in its developing stage. FOWT could be the solution in order to increase the possible construction areas, as they are more suitable for deeper waters. But the downside is that a floating foundation introduces additional dynamics to the system, which could lead to complex constructions and thereby decrease their cost/effectiveness. If the FOWT control systems take these dynamics into account it could minimize the impact of these and thereby increase the advancement

of FOWTs. Therefore in this project it is sought to develop a physically scaled model of a real wind turbine, which is able to be controlled similar to real wind turbine systems, this includes generator torque control and blade pitching control. The physical model must be constructed in order to test and verify these controlling methods. In this project the scaled nacelle of a wind turbine is designed and constructed, together with the power electronics. It is a 1:35 scaled model of the NREL 5 MW reference wind turbine. Furthermore, blades are designed and constructed in order to match the scaled thrust force of the reference wind turbine. The dynamic models of the subsystems of the wind turbine are developed and controllers for them are designed. The controller's impact is simulated in simulink models of the subsystems.

The ocean covers over 70% surface of the earth, however, we have to say that so far human being knows still very little under these waters, although we believe there should be plenty of resources we could adopt if we could find out some safe and cost-effective technology to do so. Subsea robotics has been helping human beings to extend their capabilities in recent decades, thanks to the rapid technology development. Subsea robots can commit difficult and/or dangerous tasks beyond human's natural capability, such as deepwater sea floor scanning, oil & gas exploitation and exploration, subsea pipeline installation and inspection, as well as handling some catastrophic disasters.

The proposed equipment can certainly provide us with a solid and professional subsea robotic platform, not only to verify our so-far obtained results, but also to inspire new thinking and ideas, as well as to provide relevant industries a lab-sized testing robot protocol.

A common challenge for structures submerged in water, such as offshore oil and gas platforms and wind turbine foundations, is marine fouling. The fouling consists of, for example, mussels and sea grass, which settle permanently on the structure and thereby increase both the volume and roughness of the material. This causes increased stress and fatigue of the structure, primarily due to increased wave loads and the weight of the fouling. Furthermore, the fouling complicates inspections of the structure, which are important for documenting the durability of the material. These disadvantages are reduced by cleaning the fouling off at regular intervals. Alternatively, the structure is oversized in the design phase to overcome the loads from marine fouling. Both methods are expensive for the production and/or operation of the structures and thus for energy production. In this project, two major players within Denmark's strengths, oil and gas (Total E&P) and wind (Siemens Gamesa), have joined forces to support the development of an improved concept for inspecting and combating marine fouling. The concept is based on improved robotic technology, which will raise the level of automation, as well as a compact setup that makes the operation independent of large environmentally polluting vessels, which the clean-up campaigns today depend on. The solution will finally be tested in the North Sea and will raise the technology from TRL 4 to TRL 7.

The purpose of the project is to mature the idea of ​​a novel approach for establishing reliable digital twins of offshore wind turbines, which can be employed for improved operation and maintenance of these systems. Upon successful completion of this, the intention is to apply for an Innovation Fund project or EUDP project. The aim is to develop digital twins based on closed-loop model updating and incorporate them in a systematic procedure for structural health monitoring of wind turbines, and (2) aim To develop data-driven control strategies for vibration damping.

The recent focus on monitoring of underwater energy and information infrastructure in and near Danish waters has increased the debate on the use of unmanned underwater vehicles (UUVs). While general monitoring can be advantageously carried out with sailing vessels, detailed inspection will necessarily require underwater vehicles with optical and acoustic sensors.

Industrially, UUVs have long been used for inspection and maintenance tasks with varying degrees of automation. Common to the automation of UUVs is the localization problem below the water surface. Today, acoustic solutions (LBL/SBL/USBL) mounted at the water surface are used for triangulation and thereby localization. Such solutions contribute a significant time delay, which makes automatic and precise navigation near underwater structures and objects impossible. At the same time, the localization solution is also inflexible due to the necessity of the sensors mounted at the sea surface or bottom. There has therefore been increased focus on using localization sensors that are mounted exclusively on UUVs, such as high-frequency short-range sonar and camera solutions. Sonar is extremely robust in environments where visibility is low, while the camera solution in good visibility provides the most information about objects and structures. A combination solution seems obvious to solve both the navigation problem and automated object detection and classification of the surrounding environment.

Machine learning methods have long been used for navigation and object detection for flying drones, but have not yet gained traction for UUVs. The biggest challenge is that machine learning requires a relatively large amount of data with great diversity to ensure reliable results. There are several ways to create such datasets, and for flying drones it has been shown that data augmentation with a mixture of real and virtual photorealistic images provides a good basis. Virtual images have the great advantage of enabling a simulation of conditions that can be difficult or costly to test in. For conditions above water, there are several software solutions, including from the gaming industry, which can create such realistic virtual environments. There is no equivalent solution for underwater environments where, among other things, water turbidity, light attenuation and sunlight refraction with the water surface have been studied. The tools allow these effects to be included, but there is no evidence that this gives realistic results.

In this project we want to investigate the possibility of generating and using virtual underwater environments for data augmentation in connection with training and validation of navigation, object and classification methods. We will limit the study to one case with a smaller environment with few objects, so that we can verify or falsify the working method during the project period. Results will obviously also reveal the potential for applying for a larger and more comprehensive project.

The project follows the ACOMAR project, where the main focus for AAU is to make the control and algorithm part of ACOMAR ready for TRL8. Based on offshore tests in ACOMAR, it is expected that several algorithms and their implementation will need to be adjusted to achieve TRL8. It is expected that more tests of the navigation, control and error handling algorithms will need to be carried out at local onshore test facilities, with the aim of adapting and maturing the final product.
In conjunction with these tests, it is expected that documentation of the algorithms will be made for possible transfer. The documentation is intended to promote user-friendliness, so that the algorithms can be operated by the operators.

In continuation of the previous project “Virtual photorealistic underwater environments for data augmentation in training machine learning methods for classification and navigation with UUVs”, it will be beneficial to include a sonar sensor in the selected UUV scenario and simulate it, as visual data can be limited by blurring at high turbidity, e.g. in port environments, at higher distances to the inspection object, or under poor lighting. The choice of sonar system must take into account specific needs and conditions in the selected underwater environment. This will allow for the collection and merging of acoustic data alongside the optical, which can contribute to a more comprehensive and versatile representation of the underwater environment. From a defense perspective, it is particularly interesting to achieve robust detection of objects in an extended working area. This can be, for example, in conditions where objects are hidden by marine fouling, lightly buried or by other masking that can be penetrated by acoustic signals.

In addition to the previous optical simulations, a sonar simulation model must therefore be developed and used. This involves a complex understanding of acoustic signal processing, as well as the unique properties of sound propagation under water, which is why it is intended to use an existing ultrasound simulator (Field-ii, developed by DTU) for the simulation itself. This step will drastically improve the possibility of a holistic simulation of the underwater environment in which the UUVs will operate.

The inclusion of sonar data provides the opportunity to train more robust and versatile machine learning models. Sonar data can be used to strengthen the models' ability for object detection and classification, especially (as mentioned) in scenarios where optical data is insufficient or unreliable, such as under high turbidity. Furthermore, the integration of different sensor data types could result in the development of a multisensor data fusion algorithm, which can improve the precision and reliability of the trained models.

Including sonar data will undoubtedly lead to technical challenges, such as the need to synchronize data from different sensors and the challenges of developing a realistic sonar simulation model. A further technical challenge will be ensuring that the machine learning algorithms can effectively merge the optical and sonar-based data to produce reliable results.

The purpose of the project is to develop a gyroelectric energy conversion unit for wave energy. In order to demonstrate the technology under realistic conditions, a series of experimental tests will be carried out at the Nissum Bredning Test Station on a 5 kW unit.
The following main activities will be held:
Continuation of wave basin tests on an existing prototype at AAU. Including determination of the absorbed power at different standard sea conditions. Tests with irregular waves to optimize energy absorption under realistic conditions.
Design and manufacture of a 5 kW PTO unit. In the design and in the choice of manufacturing methods, emphasis will be placed on using standard components and manufacturing methods that can also be used in a possible production of a full-scale PTO unit (15, 30 and 50 kW).
Testing and demonstration of a 5 kW PTO unit at the Nissum Bredning Test Station. Over a period of approx. 10 months from August 2015 to June 2016, a series of tests will be carried out with the PTO unit mounted to the test station platform approx. 140 m from shore.
Preparation of a measurement program data processing for the tests at AAU, as well as the testing at Nissum Bredning.
Contact with wave power developers. In the final part of the project, a number of Danish and foreign wave power developers will be contacted with a view to starting an end-user dialogue with 2-3 wave power developers.

The purpose of this proof of concept project is to further investigate the WaveSpring technology and how it can benefit wave energy plants. The results from the project will increase the efficiency of wave energy plants and reduce the price of the energy produced from the plants.