Closing Date

14^th May 2021

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

The Research Project

The sustainable offshore development of seafood and renewable energy production systems in Australia is crucial for the Blue Economy development. Included in these prospects for blue economy growth are multi-purpose offshore platforms/facilities (MPOP) which can be realised through co-location and/or integration of seafood and renewable energy production systems. Such co-location/integration would have several synergistic benefits, including shared resources, efficient use of ocean space, less competition amongst other user groups of marine space, reduced operational and maintenance (O&M) costs from possible shared activities and reduced impact on coastal environments and ecosystems. All these benefits can be achieved if an integrated/co-located MPOP can operate safely and reliably. However, because of different structural and operational specifications/limitations of the devices used for aquaculture and offshore energy production, the reliability assessment of the integrated/co-located systems is a challenging task.

An emerging field of research in reliability assessment of novel systems involves the use of the Digital Twins (DT) concept. The pairing of the virtual and physical worlds allows analysis of data and monitoring of systems to identify the problems before they even occur, preventing downtime. The DT utilises sensory data and physical models for replicating the structure and evaluating its reliability real-time. The concept has been adopted by the aerospace industry for years, for service-life management and optimisation of airplanes and space shuttles.

Implementing the DT framework however is not straight forward because of the different structural and operational limitations of the aquaculture and renewable energy devices integrated within an MPOP and the lack of structural monitoring guidelines. Solving this issue requires utilisation and coupling of different environmental, structural and failure models, optimal utilisation of different sensors etc. The physical models used within DT frameworks are not certain and there are different degrees of uncertainty associated with each one of them. Improper use of sensors for structural and operational monitoring also increases the uncertainty. Such uncertainties increase the risk of predictions obtained by a DT. This research intends to address the uncertainty within the DT components by means of risk-based reliability frameworks. The research project answers the following questions:

1. What are the capabilities of the DT concept for reliability assessment of MPOPs?
2. What are the sources of uncertainty within the models forming an MPOP DT?
3. How to quantify the risk associated with the identified uncertainties?
4. What is the cumulative impact of these risks on the predictions obtained by a DT framework?
5. How is it possible to minimise and mitigate these risks?

The project contributes towards a safer and more reliable operation of MPOPs in both exposed high-energy offshore and nearshore environments.

Eligibility

Essential Skills

• Completion of Australian Honours Degree with a First Class or 2A Honours (or internationally equivalent degree) in mechanical, civil or maritime engineering
• Masters by research degree
• Masters by coursework degree with at least 25% research
• The degree must be undertaken on a full time basis

Desirable Skills

• In-depth understanding of hydrodynamics and maritime engineering; solid knowledge of materials science
• Proficiency in a programming language (eg MATLAB); previous experience in statistical/probabilistic modelling

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Dr Vikram Garaniya for further information.

Closing Date

14^th May 2021

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

The Research Project

Global demand for efficient sea transportation has led to the evolution of large vessels worldwide. To capitalise in this efficiency increase, these larger vessels must manoeuvre in ever more confined waterways. Operating in such confined waterways creates unsafe transit for channel shipping. Additionally, extreme drought condition has led to the low water level in rivers and consequently has interrupted transportation not only by larger vessels but also by the current vessels which could safely passed through the rivers in the past. To provide safe transits, all possible vertical motions and draught changes should be considered. One of the sources of the draught change is the dynamic heel due to rudder deflection. Operating in the rivers requires to remain in the provided safe course and vessels' rudders are continuously in motion for course keeping purposes.

When a body moves in a circular path, there is a force towards the centre called centripetal force. In the case of a ship turning in a circle, the centripetal force is produced by the water acting on the side of the ship away from the centre of the turn. For equilibrium, there must be an equal and opposite force, which acts at the centre of mass (G). When a ship's rudder turns to port, the forces on the rudder itself causes a small angle of heel initially to port, however, the underwater form of the ship and centrifugal force on it cause a final static heel to starboard, and vice versa.

These two forces are coupled which tends to heel the ship away from the centre of the turn. Although a dynamic heel is developed during this coupling, the final static heel is usually taken into consideration. Neglecting dynamic heel is acceptable in waves due to the small ratio of this motion to the roll motion. However, in the case of calm water, particularly in rivers, canals and ports where there is restriction in terms of width and depth, the dynamic heel plays an important role.

This research project aims to investigate the effect of rudder deflection on the vessel's dynamic heel by three different methods of experimental, numerical and analytically solving equations of motion.

Eligibility

Essential:

• The degree must be undertaken on a full time basis
• Applicants must have already been awarded a first class honours degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector
• Strong research and analytical skills, and be first author of at least two published (or accepted) high-ranked journal papers

Desirable:

• Computational Fluid Dynamics (CFD), hands-on experimental work especially in towing tank and model test basin, computer programming skills
• Experience in Data Acquisition, Signal Processing and Machine Learning

Applicants from the following disciplines are eligible to apply:

• Naval architecture
• Maritime engineering
• Mechanical engineering

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Dr Javad Mehr for further information.

Closing Date

14^th May 2021

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

The Research Project

Development of infrastructures to harvest renewable energy in offshore locations has gained increasing attention. To exploit renewable energy from offshore locations, different types of offshore structures are being proposed and built. Fixed platforms such as jacket structures are examples of platform employed in shallow water due to the lower cost of installation, operation, and maintenance. Fixed structures take advantage of supporting large deck loads, reliable stability, and little effect from seafloor scour. The continental shelf around Australia is relatively shallow, up of 200 meters deep. Fixed structures in the shelf can be used to support renewable energy production systems.

Offshore structures are usually subjected to more severe load conditions than those on the land. Among all loads, earthquake-induced loading has more severe consequences. Optimizing the remaining useful life of offshore renewable energy assets requires significant challenges incorporated with geotechnical perspectives such as cyclic loading of the foundation, the stiffness of the foundation, and accurate assessments of structural fatigue. Therefore, earthquake-based design criteria are essential to mitigate the risk of operational loss of renewable systems during seismic events.

This project aims to develop a holistic framework by integrating dynamic analysis and advanced machine learning methods to predict the performance of the structure exposed to progressive excitation loads including hydrodynamic effects. This research project will address the following objectives:

• To develop a comprehensive methodology to analyse the dynamic strength level of the structure under seismic load,
• To develop a well-defined methodology for accidental limit state function for progressive collapse of structures,
• To develop a Machine Learning package for predicting resilience of given structures, and
• To develop a risk mitigation strategy for the operational capacity of fixed offshore structures encountering earthquake loads.

The project will formulate the resilience of offshore fixed structures encountered during seismic loads using artificial intelligence. This mathematical based framework can contribute to solve the real-world problem related to the Blue Economy projects.

Eligibility

Essential Skills

• The degree must be undertaken on a full time basis and applicants must have a First Class Honours degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector
• Proficiency with Machine Learning tool, Bayesian inference and CFD software
• Be able to demonstrate strong research and analytical skills

Desirable Skills

• Priority will be given to those who received first class honours for their bachelor degree and master by coursework course with research components and/or publications
• In-depth knowledge of offshore structures, hydrodynamics and geotechnical engineering

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Til Baalisampang for further information.

Closing Date

14^th May 2021

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

The Research Project

There is a growing need for implementation of artificial intelligence (AI) in procurement functions as it allows procurement departments to solve complex problems by using smart computer algorithms, generating effective innovation strategies. Digital technologies such as machine learning, deep learning, and neural networks can all be considered as AI which provides a wide range of opportunities for firms to bring innovations faster to the market.

The ability of the firms to drive innovations as new value-adding products and services can significantly affect their competitive advantages which cannot be successfully achieved without collaborations with their suppliers. Supply networks are potentially rich sources of ideas and innovation, and suppliers have been considered as the most important partners in the firms' innovation efforts. This emphasises the importance of procurement in assisting firms to access suppliers' innovation capabilities.

Best practices have indicated that AI applications in procurement functions can increasingly facilitate the innovation development process in different ways. For example, through automation and improvement of firms' search capabilities, AI applications provide procurement professionals with additional insights into identifying the highly innovative suppliers and analysing their role in developing more innovative products and services. However, research contributions in exploring the impact of AI applications on supporting procurement decisions for collaborative innovations are extremely limited. Therefore, there are huge potentials for focusing on procurement functions to stimulate supplier innovations in the era of digital transformation. In this regard, this project investigates the applications of AI in enabling procurement functions to leverage supplier capabilities for the innovation development process.

Eligibility

• Deep understanding of supply chain management and procurement functions
• Understanding of AI and related technologies
• Evidence of high level written and communication skills expected for Doctoral study
• Good analytical skills
• Work or research experience in supply chain management

See the following web page for entry requirements: www.utas.edu.au/research/degrees/what-is-a-research-degree

Application Process

Applicants who require more information or are interested in this specific project should first contact the listed Supervisor. Information and guidance on the application process can be found on the Apply Now website.

Information about scholarships is available on the Scholarships webpage.

More Information

Please contact, Dr Hadi Rezaei Vandchali for further information.