Building a digital twin navigating autonomous underwater vehicles to monitor water quality. NERC GW4+ DTP PhD studentship for 2024 Entry. Ref: 4972
About the award
Dr Jozef Skakala, Plymouth Marine Laboratory, Modelling group
Lead Institution: Plymouth Marine Laboratory
Location: Plymouth Marine Laboratory & University of Exeter
About the Partnership
This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science.
For eligible successful applicants, the studentships comprises:
- An stipend for 3.5 years (currently £18,622 p.a. for 2023-24) in line with UK Research and Innovation rates
- Payment of university tuition fee;
- A research budget of £11,000 for an international conference, lab, field and research expenses;
- A training budget of £3,250 for specialist training courses and expenses
The ocean is an essential component of the climate system, whilst simultaneously providing key resources for billions of people. However, observing the ocean, especially its biology and chemistry, is a major challenge due to its vastness and complexity. Robust observations can be obtained from satellite measurements of the ocean surface, but only a limited number of ocean variables can be derived from those measurements, such as sea surface temperature, or surface chlorophyll concentrations. Other observations are scattered and still relatively few, as well as expensive to obtain. To make the best use of available resources, and achieve the goal of net-zero carbon science, we need to make our observations much more efficient, including being adaptive to real-time ocean conditions. Autonomous underwater vehicles (AUVs), such as ocean gliders, are establishing themselves as a key source of marine observations. Excitingly, these AUVs can be navigated by ``intelligent’’, digital twin (DT), systems, optimizing the effectiveness and maximising the impact of observational science by ensuring we target observations at the most interesting/important locations and times. These DT systems consist of two-way communicating physical-biogeochemical ocean models, machine learning (ML)/artificial intelligence (AI) components, and adaptive autonomous platforms (see Fig.1), with the ML/AI providing path-planning and sample control for the AUV. A successful example of such a system was demonstrated in a recent phytoplankton bloom-tracking study by Ford et al (2022), demonstrating that DTs including AUVs can (i) help us understand the onset of events potentially impacting marine water-quality, and (ii) improve forecasts of such events. The ideas from Ford et al (2022) need to be pushed further, e.g. by (i) extending them to new applications, (ii) exploring more effective DT designs, or (iii) including multiple AUVs.
One important new application is focusing the AUVs on tracking the onset of marine hypoxia. This is of great value, since dissolved oxygen (DO) concentrations act as an important indicator of ecosystem health and water quality. The global DO inventory has declined by approximately 2% within the last 50 years and the depletion of DO is a known problem in parts of the North Sea. Hypoxia events can lead to mass fish mortality and, worryingly, are predicted to accelerate globally in response to climate change, expanding marine dead zones. Consequently, there are ongoing (e.g. OSPAR) oxygen monitoring programmes in the North Sea, to which future DT-AUV missions could contribute
Project Aims and Methods
We propose for the student to develop a proof-of-concept DT to demonstrate that multiple AUVs could be successfully navigated to investigate oxygen depletion/minima zones using their own measurements (of temperature, salinity, chlorophyll and oxygen itself) as well as Earth Observation (EO) and model data. The initial plan, which we are happy to later adapt to the student’s initiatives, needs and interests, is that the student will run virtual DT experiments using a state-of-the-art physics-biogeochemistry ocean model (NEMO-FABM-ERSEM), acting as the ``real-world ocean’’ in which AUVs take ``samples’’. Virtual EO data will be similarly extracted from the model outputs and perturbed by noise to represent observational uncertainty, providing an idealised framework in which the true ocean state and all model and observational errors are known, allowing the effectiveness of the developed ML/AI algorithms to be accurately assessed. Building on a recent work of Skakala et al (2023), the student will develop ML model to forecast DO values from the virtual glider measurements and EO data. The ML model will then be used together with path-planning algorithms, designed by the student, to optimize the trajectory and sampling strategy of multiple gliders. The work will demonstrate the feasibility of the DT AUVs control for tracking hypoxia and provide framework for real-world applications. The work will further investigate/advise what variables need to be measured, as well as their locations and sampling resolution, for a successful future hypoxia tracking mission.
1. high level numerical/technical skills, e.g. background in one of the following: computer science, mathematics, physics, engineering., 2. enthusiasm for marine/climate science.
This is a highly successful partnership between PML, University of Exeter and the Met Office (here as a collaborative partner), resulting in the first ever example of a fully autonomous marine biogeochemistry observing mission, published in Ford et al (2022). PML provides a vibrant environment for all aspects of world-class marine science, from observations and autonomy (e.g. DIMA group, EOSA/NEODAAS, or WCO/SmartSound Plymouth) to modelling and data assimilation (MSM group). University of Exeter is a world-leading institution in environmental and climate research, as well as in applied AI/ML, including applying AI/ML solutions to AUV path-planning and navigation. Met Office is a world-leader in weather and climate prediction (including the ocean). The student will be based at PML, but will spend a major part of their PhD at the University of Exeter, as well as visiting the Met Office, benefiting from the expertise of scientists at the UK’s national meteorological service.
PML will provide many opportunities to interact and be mentored by leading experts in many aspects of marine science (especially in biogeochemistry/ecology) and together with the Met Office will provide the student with access and introduction to the state-of-the-art marine models and HPC. Uni of Exeter will offer many useful courses, e.g. on robotics and automation, statistical modelling in space and time, high performance computing as well as general training programmes, e.g. on research methods.
Background reading and references
1. Ford, D.A., Grossberg, S., Rinaldi, G., Menon, P.P., Palmer, M.R., Skákala, J., Smyth, T., Williams, C.A., Lorenzo Lopez, A. and Ciavatta, S., 2022. A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts. Frontiers in Marine Science, 9, p.1067174. 2. Skakala, J., Awty-Carroll, K., Menon, P.P., Wang, K. and Lessin, G., 2023. Future digital twins: emulating a highly complex marine biogeochemical model with machine learning to predict hypoxia. Frontiers in Marine Science, 10, p.1058837.
NERC GW4+ DTP studentships are open to UK and Irish nationals who, if successful in their applications, will receive a full studentship including payment of university tuition fees at the home fees rate.
A limited number of full studentships are also available to international students which are defined as EU (excluding Irish nationals), EEA, Swiss and all other non-UK nationals. For further details please see the NERC GW4+ website.
Those not meeting the nationality and residency requirements to be treated as a ‘home’ student may apply for a limited number of full studentships for international students. Although international students are usually charged a higher tuition fee rate than ‘home’ students, those international students offered a NERC GW4+ Doctoral Training Partnership full studentship starting in 2023 will only be charged the ‘home’ tuition fee rate (which will be covered by the studentship).
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Equality, Diversity and Inclusion
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The closing date for applications is 2359 hours GMT Tuesday 9 January 2024. Interviews will be held between 22 February and 8 March 2024. For more information about the NERC GW4+ DPT please visit https://nercgw4plus.ac.uk
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|Application deadline:||9th January 2024|
|Value:||£18,622 per annum for 2023-24|
|Duration of award:||per year|
|Contact: PGR Admissions Officefirstname.lastname@example.org|