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Understanding and optimisation of hydrodynamic properties of micro Autonomous Underwater Vehicle (AUV) configurations resulting from the installation of different payload profiles


Project Description

Project Rationale:
The study will investigate how minimum data sampling requirements can be met in a robust manner using a shoal of heterogeneously instrumented micro-AUVs. Oceanographic observations often require a large number different parameters to be measured simultaneously. One method in which this type of problem specific sampling can be achieved, using a general observational framework, is through the use of low-cost shoals of micro AUVs to build up a distributed networks of sensors. The main advantage of this approach compared to more conventional methods, e.g. buoys, moorings or conventional AUVs, lies in the relative ease with which the systems can be re-configured, for example the extent and resolution of the measurements can be controlled by the location and number of micro AUVs used to form the network.
However, even within the same network, the various nodes will often need to be instrumented differently to capture the different aspects of the environment, and there is a need to ensure the basic performance of the platforms, such as control, maneuverability and endurance, are relatively uniform for the network to properly function. This is particularly challenging for compact platforms since the relatively large size of the commercial sensor packages used can have a significant effect on these performance parameters

Methodology:
The compact nature of micro-AUVs means that their performance is influenced dramatically by any alteration of their hydrodynamic profile due to fitting of external sensors, antennas, or other ancillary equipment. The implications on platform endurance also need to be considered. In order to facilitate this, this project will develop the necessary framework to evaluate the performance of distributed sensor networks for environmental sampling. In the first part, the study would seek to characterise this for different ecoSUB vehicle shapes, and provide simple to use tools for determining the effects of adding protuberances to the basic body shapes on speed and endurance. This will be carried out through the use of computational fluid dynamics and wind tunnel testing where a suite of common sensor payloads will be considered. Next, methods will be developed to optimize the control, manouvrability and endurance of the platforms through the use of advanced control methods so that a uniform performance can be achieved for the different payload setups. Finally, the performance of the shoals will be evaluated using realistic environment simulators that take into account environmental parameters such as underwater and surface currents, to verify the robustness of observation missions to the possible changes in the environment.


Training:
The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and hosted in the School of Engineering and Planet Ocean. Specific training will
include:
Operation of Planet Ocean”s micro-ecoSub (owned by UoS) and mini-ecoSub (owned by Planet Ocean) platforms
Use of numerical simulators, supercomputers and advanced control methodologies
Use of wind tunnels and experimental design

Funding Notes

You can apply for fully-funded studentships (stipend and fees) from INSPIRE if you:
Are a UK or EU national.
Have no restrictions on how long you can stay in the UK.
Have been 'ordinarily resident' in the UK for 3 years prior to the start of the project.

Please click View Website for more information on eligibilty and how to apply

References

[1] https://planet-ocean.co.uk/wp/?page_id=4509
[2] http://www.bridges-h2020.eu/

How good is research at University of Southampton in Earth Systems and Environmental Sciences?

FTE Category A staff submitted: 68.62

Research output data provided by the Research Excellence Framework (REF)

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