MEI Internship Program
Love doing research? Want a career at the forefront of the energy transition? Why not do both?
MEI is offering 10 paid internships for talented people who want to help drive the energy transition. The 12 week MEI Internship Program pairs interns with leading researchers and industry working on exciting projects.
During your internship, you can then decide if you want to continue on to a PhD. At the very least, you'll have 12 weeks paid work experience.
To be eligible, prospective interns must be able to commence as a domestic PhD student in 2020 or 2021 and already reside in Australia. Whilst interns will not be expected to complete an application for PhD study prior to commencement, the interest in PhD study will be evaluated during intern selection.
Internships will commence between 1st December 2020 and 1st March 2021, with the formal start date to be determined between the lead supervisor and intern. The internship will be for 12 weeks full time as a Research Assistant, Grade 1.1.
How to apply
Applications are closed.
MEI Internship Projects
Using renewable hydrogen in gas turbines
The rapid transition to a zero-emission economy will very likely require massive increases in the use of hydrogen. Green hydrogen made from renewable energy can play a major part in this transition, for use in advanced gas turbines and other industrial devices. However, gas turbines need to use green hydrogen safely and efficiently, and burning hydrogen at high pressures creates unique challenges. This includes the possibility of instabilities in the combustor that can tear the engine apart. The aim of this internship is to design an experimental facility to study how hydrogen addition affects turbulent premixed flame stability as pressure increases up to 30 times atmospheric.Internship
Combustion chemistry of hydrogen-natural gas mixtures
The rise in renewable energy in Australia causes a pressing issue of energy storage: what to do with the excess electricity generated when the grid demand is low. One promising solution is to use the electricity to decompose water and store the produced hydrogen in natural gas pipelines. This approach offers enormous capacity for renewable energy storage compared with alternative means such as that used by Snowy Hydro. However, hydrogen and natural gas have very different combustion properties. For example, the flame speed of hydrogen is 7 to 8 times that of natural gas, and hydrogen is more likely to explode when pressure drops. This causes potential problems for home burners and industrial appliances, but the solutions are not well known. The aim of this internship is to carry out an experimental study of hydrogen-methane mixtures in a high-pressure flow reactor in the Thermodynamics Laboratory. The results of this work will be used to develop accurate chemical models for these mixtures for practical applications.Internship
Machine-learning for improving efficiency of power generation
Computational Fluid Dynamics (CFD) has become an integral part of the design of any energy generating device, from gas turbines to wind or tidal turbines. In industrial design, CFD relies heavily on models for the complex turbulent flows that feature in nearly all energy applications. However, the current industry-standard models are based on simplified assumptions that can lead to significant errors, and thus lack of accuracy, that reduce the impact CFD can have on technology development. In this project, a unique machine-learning framework for turbulence modelling, developed at the University of Melbourne, is used to improve the accuracy of turbulence models. The models will be developed by using large-scale data sets already available. Importantly, the newly developed models will also be tested in a full a-posteriori context on industrially relevant problems to assess their accuracy.Internship
Forecasting techniques for renewable energy sources: Performance analysis and economic impact on grid operation
The recent movement of Victoria on subsidising photovoltaic (PV) power installations on the household level is about to introduce highly intermittent and uncertain generation in the distribution system. The unpredictable nature of the high penetration of PV power may rapidly deteriorate both the reliability and economic performance of power systems. This project focuses on developing statistical learning methods appropriate for forecasting PV generation. Existing PV power data with spatial and temporal correlation will be utilised to understand the underlying statistical properties of PV generation. Using these properties, short-term time-series forecasting tools will be developed in order to provide useful forecasts and uncertainty measures. The resulted high-performance forecasting methods along with the knowledge of basic household load profiles will allow for a more accurate quantification of active power reserve requirements necessitated by the future PV-rich distribution systems. This will be achieved by analysing the impact of the uncertainty on reserve requirements given the technical constraints and control capabilities of a distribution network. This forecasting approach is a key step towards the development of a data-driven decision-making framework for power system operation.Internship
Online integrated electricity-gas-hydrogen network modelling tool with advanced input-output graphical user interface
Decarbonisation of the entire Australian energy system requires the development of new computationally efficient tools. In particular, an integrated electricity, gas, and hydrogen system (IEGHS) modelling tool is currently being developed within the Future Fuels CRC project which is capable of investigating a wide array of decarbonisation scenarios, including injection/storage of renewable hydrogen/synthetic methane into the gas network. Critical to this modelling tool is the graphical user interface (GUI), for both input and output data and information, as well as to perform data analytics and interpretation of the output results.This multi-disciplinary project will develop a GUI that works seamlessly with the IEGHS modelling tool, focusing on neat visualisation techniques of input data and results. The GUI will have the capability of calling the IEGHS tool in real-time. It will be the first of its kind in the industry, and the student will gain first-hand experience in developing tools that interact with advanced modelling.The candidate is required to have excellent knowledge of programming, some background in data analytics, and ideally experience in one or more of the following programming languages: Python, Julia, and MATLAB.Internship
Mapping methane hot-spots from space with novel retrieval data
Methane is the second-most important greenhouse gas and Australia has very high anthropogenic methane emissions per capita. The generally diffuse nature of sources of atmospheric methane makes mapping and quantifying emissions challenging. Variation in total-column methane can be seen from space, with the recently-launched TROPOMI sensor providing the highest resolution and accuracy.Initial examination of this methane data identified a potential hot-spot in north west Queensland. A series of investigations (partly supported with MEI seed funding), including running an atmospheric flux-inversion system, led to the conclusion that this anomaly is most likely a retrieval artifact. Concerns about quality control precluded the ESA TROPOMI methane data product for use in routine monitoring of methane emissions.Alternative processing by the University of Bremen of the TROPOMI methane data has become available. Initial investigations show that the putative hot-spot in north west Queensland does not appear in the Bremen methane data, but that it highlights other regions of interest (e.g., the New South Wales Hunter Valley); see Figure 1. The Bremen product is subject to ongoing validation against the state-of-the-art TCCON ground-based network, which provides excellent anchor points at Darwin and Wollongong but cannot validate regional hot-spots elsewhere.The flux-inversion system has a steep learning curve, and is only suitable for MSc and PhD projects. Thus we propose an exploratory data analysis project for this 12-week internship, aiming to:identify any hot-spots;characterise their behaviour contingent on wind flow; andestimate emission rates using the above results.Internship
Organisational determinants of the uptake and performance of energy efficiency initiatives
This project will investigate the degree to which the pursuit and success of energy efficiency initiatives depends on (a) where responsibility for such initiatives sits within an organisation; and (b) the background of the responsible individual(s). It is motivated by two observations:Informants in sectors as diverse as infrastructure, banking and healthcare that energy efficiency initiatives are often either not pursued or not successful despite relatively clear economic payoffs, established technologies and clear environmental benefit.Preliminary research has revealed significant variation across firms in the level (functional, business unit, corporate) and area (e.g., procurement, finance, corporate affairs, people and culture) at which responsibility for such initiatives resides. There is also variation in the backgrounds (business, policy, scientific/ technical) of the responsible individuals.We believe these organisational factors may play an important and under-appreciated role in determining the number, type and success of energy efficiency initiatives a firm pursues. The initial 12 week project will include (a) reviewing the relevant scholarly literature, (b) systematically mapping where responsibility for energy efficiency sits in companies across multiple sectors in Australia, and; (c) beginning to develop a specific research question to be pursued during doctoral study.Internship
Design of advanced input-output and data analytics graphical user interface for a low-carbon power system operation and planning tool
A tool is being developed by a multi-disciplinary team at the University of Melbourne to perform optimal planning and simulate the operation and performance of future low-carbon power systems with large shares of renewables and distributed energy resources. The model is currently being used to study planning options for the National Electricity Market in Australia and for the UK transmission system. Through advanced data analytics, the tool will include a graphical user interface (GUI) to facilitate clarity in the large amount of required input data and in the interpretation and analysis of the results.This internship will support the development of the GUI and will provide the student with a unique opportunity to gain an in-depth understanding of state-of-the-art power system operation, planning and relevant tools, while also improving their skills in coding, database management, and data analytics.The candidate should have excellent knowledge of programming, some background in data analytics, and ideally experience in one or more of the following programming languages: Python, Julia, and MATLAB.Internship
Improving earthquake detection and location using novel signal processing techniques to support carbon sequestration (CS) applications in the offshore Gippsland Basin
The UoM seismology group has been awarded $424,370 by ANLEC R&D to optimise seismic monitoring and establish a natural baseline for seismicity in southeast Australia in support of the commercial-scale carbon sequestration (CS) offshore Gippsland Basin (The CarbonNet Project), for which a demonstration project is currently under development. The outcomes of our research work provide means to assess the security of CS sites before, during, and after injections. This is necessary for managing reservoirs, fulfilling regulatory requirements, and providing assurance to the public. A key element in our research work is developing capabilities to detect and locate earthquakes accurately, and when possible, in near-real time. At present, our workflows are performed manually, which we intend to optimise by automatising detection and location workflows. The intern will test new algorithms that enable us to detect events and measure seismic phase arrival times automatically. With these new algorithms, we intend to lower the minimum magnitude detection threshold significantly, whereby the number of detected events can be increased. For instance, with template matching techniques, we could potentially increase the number of event detections by as much as ten times. The internship opportunity is likely to commence in January 2021.Internship
Gippsland geothermal mapping and cost analysis tool — information gathering and geothermal economic algorithms
In June 2020, the Latrobe Valley Authority awarded a $180,000 grant to UoM to conduct a 12 month project (July 2020 – June 2021) in collaboration with the Geological Survey of Victoria to undertake preliminary work towards developing mapping and economic assessment tools for geothermal energy in Gippsland. One stream of UoM’s role in the project is to construct thermal energy demand curves for a series of ‘standard’ geothermal energy end uses (e.g. greenhouses, barramundi farms, spa resorts, district heating) in consultation with existing and potential operators. The intern will assist the Lead Researcher on this task. Specific duties might include helping to define the ‘standard’ cases, working with commercial operators to understand their requirements for process heat as a function of time (diurnal and annual), gathering ancillary data (e.g. weather records), and integrating all information into a series of heat demand projections. The 12-week internship is most likely to run from October to December 2020.Internship
Decarbonising the transport sector with renewable hydrogen
The transport sector has about 25% contribution to the total produced CO2 emissions globally. Reciprocating engines are currently the major driver of this sector and will remain so in the foreseeable future. One pathway to decarbonise the transport sector is using clean fuels such as hydrogen. This zero-CO2 emission fuel is even more attractive when it is produced using renewable energy. One issue with burning hydrogen in reciprocating engines is that hydrogen has very different properties to conventional fuels, so could severely impact engine performance. The aim of this internship is to address this challenge through an experimental study on hydrogen behaviour in the Cooperative Fuels Research (CFR) engine.Internship
Quantum information for fusion power
Quantum information science is poised to revolutionise information technology, with significant impacts in many areas of science and technology. Quantum information science includes general-purpose quantum computation, quantum simulation, quantum sensing and quantum communications. A fifth element is quantum materials, which can enable quantum computing (through physical embodiment of qubits) and which are a frontier of fundamental science in themselves. The USA Department of Energy is exploring quantum information initiatives, and the fusion research community has developed a roadmap for a role for quantum information science applied to fusion power research. Specifically: thermonuclear fusion is a process that involves quantum mechanical tunneling of sub-atomic particles through Coulomb barriers to initiate the fusion process itself mediated by the short-range strong nuclear force. Despite close to a century of research, no new routes to overcome the Coulomb barrier have been invented apart from the brute force method of surmounting the barrier with kinetic energy.This project aims to investigate opportunities provided by the emerging field of quantum information science to find new routes to solve this problem, which is fundamentally quantum mechanical in origin. We seek to address opportunities identified in the roadmap which is for “Reconceptualising Classical Plasma Physics Problems for Quantum Computation and to develop concepts and then algorithms to solve important problems in fusion and plasma physics with emerging quantum computers—in the long-term with error correction.” At this early stage we propose to review the literature and to better define the problems that could be addressed with the quantum technologies. It is possible the scope of possible algorithms for simple simulations could also be developed.Internship