PhD Opportunities

PhD Opportunities

Possible research topics to be undertaken in the Systems, Power & Energy Division of the School of Engineering are given below. If you are interested in any of these projects, you should email the prospective supervisor for discussing your intentions.

The School of Engineering has a limited number of scholarships to offer to excellent candidates, application shall be discussed with the potential supervisor. For potential sources of funding, see details on Scholarships on our Postgraduate Research page.

Alternatively, you are welcome to identify a different project topic within any relevant research areas by emailing your project proposal to the Head of Division, Prof. Margaret Lucas (Margaret.Lucas@glasgow.ac.uk), who will direct you towards a prospective supervisor with expertise in that area.

Themes

Medical and Industrial Ultrasonics
Space
Energy
Materials, Design and Manufacturing
Communications, Sensing and Imaging

Medical and Industrial Ultrasonics

Miniaturised ultrasonic sensors for smart vascular stents

Supervisors

Dr Steven L Neale
Dr John Mercer (MVLS)

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

This PhD project will develop miniaturised ultrasonic sensing devices for vascular stents. The goal is to produce a device that can continually monitor the state of the stent, detecting if a blockage is reforming.

Despite significant improvements in healthcare provision, cardiovascular disease (CVD) remains the number one cause of death in the World. Atherosclerosis is the pathological condition that underlies two thirds of heart attacks and strokes, which in turn contribute to more than 4.3 million premature deaths in Europe per annum. The economic burden to the European Union for cardiovascular disease is estimated at over €196 Billion. The current clinical approaches of stenting coronary artery plaques with a small metal stent device or heart by- pass surgery have significant risk and associated costs. This proposal attempts to build towards a smart stent device that can be deployed using existing NHS catheterisation facilities, yet provides advanced technological properties that are predicted to reduce patient morbidity and mortality. A stent which can interact and report on its own vessel status, such as when the vessel re-blocks or clots would be transformative. In this project we will investigate how placing a miniature ultrasonic transducer within the stent before it is deployed could help to monitor the health of the vessel and the stent within it. Current research on smart stents include using the electrical impedance change associated with the build up of tissue around the stent to detect if it is becoming re-blocked however the ultrasonic approach may provide greater information about the amount and position of material building up giving key clinical data informing on whether an intervention is necessary.

The project will build on an existing interdisciplinary collaboration between Dr Steven L Neale who is in Medical and Industrial Ultrasonics (MIU) in the School of Engineering and Dr John Mercer in the Institute of Cardiovascular and Medical Sciences. The work will involve building the miniaturised ultrasonic transducer and testing it using a variety of vascular stents and tissue phantoms and will be based both within the school of Engineering and the British Heart Foundation building and as such it is suitable for an enthusiastic and diligent Engineering/Physics student with interests in developing technology for biomedical applications.


Artificial intelligence for swarm robotics tested with optoelectronic tweezers

Supervisors

Dr Steven L Neale
Professor Colin Mcinnes
Dr Euan W McGookin

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The PhD project will develop an Artificial Intelligence (AI) to control a swarm of small robots. The AI will be demonstrated and tested through the creation of an Optoelectronic Tweezers (OET) test-bed which will allow the physical robot swarm to attempt specific assembly tasks, evaluating how the coordination of the individuals helps to attain the goal set.

The OET system allows the control of microscopic particles through light patterned electrical fields. Highly polarisable metallic particles are attracted to areas of high electrical field which appear when the OET device is illuminated thus the position of the particles can be controlled by changing the light pattern applied. This “touch free” control is ideal for testing control strategies as the individual elements can be rearranged simply by applying the desired pattern in light. In this project we will be considering each discrete particle as a microscopic actuator able to push a small electronic component or its neighbouring actuators. We will think of these actuators as “microbots” which can be programmed to perform assembly tasks by creating an AI which control the light pattern applied to the device. The assembly goals that we will be targeting will include pushing individual electronic Surface Mount Technology (SMT) components into their positions in a circuit so that the swarm of microscopic robots will be able to achieve a macroscopic assembly goal. We will also test out small scale versions of larger world control scenarios such as simulating search and rescue tasks by creating microscopic mases for the swarm to explore. This will give us the ability to test out large swarms of particles in a real-world environment and see how they cope with the inevitable problems that are associated with large swarms such as some individual robots not performing in the manner expected.

The project will be in Dr Steven L Neale’s micromanipulation research group where we have experience with the Optoelectronic Tweezers system (https://www.gla.ac.uk/schools/engineering/staff/stevenneale/) and all necessary training in microfabrication and micromanipulation will be provided. This project will develop two different control strategies for the microbot swarm and test the against each other which will be developed with the help of Prof Colin McInnes and Dr Euan McGookin who both have experience of different robotics control strategies. The project will involve both developing the AI algorithms and also physical experimentation to demonstrate them and so will require an enthusiastic and dedicated student with an interest in robotics.


High-speed imaging and emission characterisation of acoustically activated drug delivery particles

Supervisors

Dr Paul Prentice
Dr Helen Mulvana

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Acoustically activated microbubbles (and advanced particles based on microbubbles) for localised and non-invasive drug delivery to a range of organs (including brain and pancreas), continue to attract significant and world-wide research attention, including numerous in-vivo and preclinical trials. In publications generated by this research, it is common for therapeutic bioeffect to be reported in parallel with some representation (often the noise-spectrum) of the acoustic emissions detected from the driven microbubble population, such that the bubble activity can be classified or quantified for correlation to the drug delivery, or degree of tissue damage.

Somewhat remarkably, however, the behaviour of microbubble populations within blood vessels, exposed to therapeutic ultrasound – and in particular, the acoustic emissions generated – are still poorly understood. Clearly, this deficit in knowledge hinders refinement and optimisation of exposure and detection protocols, but is also critical to understanding the mechanisms underpinning microbubble mediated therapies.

This project is dedicated to addressing this deficit, using two state-of-the-art ultra-high speed cameras, a range of hydrophone detectors (including with complex calibration for magnitude and phase response), and research-enabled diagnostic imaging arrays, to interrogate and characterise microbubble activity in capillary models. Dual high-speed imaging will allow unprecedented investigations of phenomena at varying timescales – for example, clustering dynamics under the action of secondary radiation forces during the initial acoustic exposure, and temporally resolved cluster oscillations (at image acquisition rates of up to 10 million frames per second) for direct correlation to the detected acoustic emission.

Therapeutic ultrasound parameters including frequency, pressure amplitude and pulsing duty cycle, as well as microbubble concentration, and flow rate (to mimic blood circulation) will all be systematically studied. The work will make use of anatomically accurate flow phantoms, already in use to study the physiological effects of blood vessel geometry and flow on microbubble dynamics and in vivo delivery data arising from a separate study to use microbubbles for delivery to the rat placenta.

Advanced drug delivery vehicles will also be investigated in the latter stages of this project, including

  1. Acoustic Cluster Therapy (ACTTM; combining commercial GE healthcare microbubble agent, Sonazoid, with drug-loaded vapourisation droplets), via an existing industrial collaboration with manufacturers; Phoenix Solutions AS (Oslo, Norway).
  2. SPION (superparamagnetic iron oxide nanoparticles) microbubbles as novel multimodal contrast agent for magnetomotive ultrasound imaging

Ultrasonically-assisted penetration of granular materials

Supervisors

Dr Patrick Harkness

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Granular materials are difficult to explore, but yet they make up an enormous proportion of the Earth’s seabeds and deserts, as well as almost the entire surface of several planets, moons, and asteroids. To explore these regions, we will need to penetrate the material using very low forces because the terrain (and gravity) does not always support the development of larger loads.

Previous work at the University of Glasgow has demonstrated that ultrasonic vibration has the potential to fluidise granular materials such that they will flow more easily. This can reduce the forces needed to penetrate.

A doctoral project is therefore proposed to investigate how this effect might be magnified and exploited. For example, it is known that hammering probes may perform better when they are ultrasonically assisted, and there have been attempts to produce biomimetic versions of razor clams which can dig at extreme speed by liquefying the sand around their bodies. Applications of this work could include space exploration, anchoring systems, and construction tools.

The project will require the development of ultrasonically-resonant devices, coupled with skills in robotics and experiment design. The objectives are primarily scientific, and seek firstly to understand the liquefaction process around an ultrasonic tip, leading on to design optimisation in terms of minimising both ‘profile’ and ‘frictional’ drag on the penetrator. The student will be encouraged to apply for variable-gravity opportunities (parabolic flights and centrifuge time) to extend their parameter space as they explore the effects of ultrasonic amplitude, duration, and mode-shape.

It is envisaged that the final output of the project will be a tethered subsurface explorer.

The student will work in an environment with an ongoing interest in subsurface exploration (including ice, soil, and rock drilling) and will be expected to collaborate with colleagues in the group.


Flexible ultrasonic surgical devices

Supervisors

Prof Margaret Lucas

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Ultrasonic devices for surgery applications rely on resonant structures. This results in the surgical tips being simple geometries, usually straight or with a single bend. Many surgical procedures require devices to reach locations in the body that are difficult to access and therefore a flexible device, able to move along a tortuous path to the site of surgery would have very significant advantages.

This project investigates a completely new approach to the design of ultrasonic surgical devices. The surgical tips will be driven by new, innovative transducers that can enable the device itself to be flexible. The project will research the capabilities of innovative transducers to deliver sufficient ultrasonic excitation and the optimal vibrational motion to the surgical tip to perform precision cutting of tissue. A key focus of the research will be in miniaturisation of devices for minimal access surgeries.


Smart ultrasonic transducers for surgical devices

Supervisors

Prof Margaret Lucas

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

A significant research effort in the Ultrasonic Group is developing new ultrasonic surgical devices, particularly for miniaturised minimal access surgeries involving bone cutting procedures. Recent research has been successful in incorporating a shape memory alloy (SMA), Nitinol, in a novel ultrasonic cymbal transducer. The transducer is capable of being operated in the same mode of vibration at two distinct resonant frequencies through a phase change in the material as a result of a small change in temperature.

This project will investigate how the temperature change required for the phase change can be controlled and minimised through the choice of material and will also investigate other phase change materials. Research will also focus on how the phase change could be achieved through small changes to the loading of the transducer.  Methods of driving the transducer to deliver the required phase change will also be researched. The project will develop alternative transducer designs that can incorporate SMAs for dual or multiple frequency operation. The overall aim will be to deliver small surgical devices that can cut both soft and hard tissues with a single ultrasonic surgical device.


Superparamagnetic microbubbles for ultrasound mediated therapy and molecular imaging

Supervisors

Dr Helen Mulvana
Dr Paul Prentice

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Gas filled microbubbles are used in ultrasound imaging to improve the echogenicity of perfused tissue. When driven to oscillate using ultrasound they are also capable of generating localised bioeffects, which can increase cellular porosity to drugs. As a result, microbubbles have generated significant interest as a tool for improving the diagnostic value of ultrasound imaging and as a platform for the development of non-invasive therapies.

The addition of compounds to the shells of these agents using either ligands or through direct incorporation has been explored as a means of increasing their utility. Antibodies can be used to improve microbubble affinity to specific biological markers of disease for molecular imaging. Nanoparticles can improve transport of drug payloads for therapy or be added to produce multi-modal microbubbles for use with imaging modalities such as MRI or PET in addition to ultrasound. Each of these manipulations also present opportunities to influence local microbubble concentrations in the body.

The focus of this project will be to study the effects of adding super paragmagnetic iron oxide nanoparticles (SPIONs) to microbubble shells (SPION-MBs). SPION-MBs will be investigated in vitro cell systems and using vessel flow phantoms. Hydrophone detectors (fibre optic; needle) and state of the art high speed imaging will be used to investigate the influence of SPIONs and the means by which they are incorporated within the MB on their resulting acoustic behaviour across a range of acoustic driving conditions. Close examination of SPION-MB dynamic behaviour and their biological interactions within and without a magnetic field will also be undertaken to build a comprehensive understanding of how SPION-MBs might be exploited for diagnostic ultrasound imaging and therapy.


Acoustic interactions of microbubbles and cells

Supervisors

Dr Helen Mulvana
Dr Paul Prentice

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Gas filled microbubbles are used in ultrasound imaging to improve the echogenicity of perfused tissue. When driven to oscillate using ultrasound they are also capable of generating localised bioeffects, which can be used to drive the transport of particles and to increase cellular porosity to drugs. As a result, microbubbles have generated significant interest as a platform for the development of non-invasive therapies.

Considerable work has been undertaken to attempt to understand how ultrasound driven microbubbles can act to influence cellular porosity. For example it is known that at low acoustic intensities microbubble oscillation will be predominantly linear and can be used to initiate endocytosis in targeted cells, while higher acoustic intensities can lead to micro-jet formation capable of directly piercing the cell membrane. Despite this many questions remain unanswered regarding how microbubbles might influence cellular behaviour, how they might traverse vascular boundaries and behaviour differs in vitro as compared to in vivo.

In this project the response and behaviour of cultured cells in response to oscillating microbubbles will be investigated. Using a range of available techniques including physiologically accurate flow phantoms, fluorescence microscopy, transmembrane resistance measurement and detailed acoustic characterisation the influence of oscillating microbubbles on cellular behaviour will be assessed under a range of both ultrasound and microbubble conditions. The resulting information will provide essential insight to the development of more effective therapeutic applications of ultrasound driven microbubbles.


Space

Development of throttleable solid-rocket motors

Supervisors

Dr Patrick Harkness

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Liquid-fuelled rockets motors are heavy and complex, but they have the advantage that they can be throttled in real-time. Solid rockets, on the other hand, are of a simpler construction but their thrust profile is ‘baked-in’ at the time of their manufacture.

This project seeks to combine the advantages of the two engine types by producing a throttleable solid rocket motor. The University of Glasgow, in collaboration with Dnipro National University in Ukraine, has achieved a test-firing of a solid-fuelled rocket motor which has demonstrated several throttle cycles, but there are some outstanding difficulties. In particular, the force required to push the solid-fuel rod into the combustion chamber is significant because it must overcome the pressure in the combustion chamber itself. The objective is to build a resonant chamber in the style of a pulse-jet, such that the fuel can be added during periods of relatively low local pressure.

The project will require modelling of combustion-chamber gas dynamics, using ground-truth from previous experiments in Ukraine. The intention is that, when these results are satisfactory, the model will be used to drive the design of a new combustion chamber that would be manufactured and test-fired in Ukraine.

In addition to tuning a resonant combustor to operate, for the first time, on the basis of vaporised solid propellant, effort will be expended to maximise heat transfer into the propellant itself. This effect is important because heat transfer from gases to solid impingement plates is affected by flow pulsations, and there may well be a design driver that seeks to balance the effects of pulse frequency (essentially a function of chamber size) and heating efficiency (a function of Reynolds and Strouhal numbers). This process must be optimised, because it is the heat of the combustion chamber, transmitted back into the propellant rod, which vaporises the fuel and allows the safe operation of the engine concept.

The student, in this project, will work in a team dedicated to the exploration of space and hazardous environments, but will also be expected to develop strong links with our Ukrainian partners.


Design and optimisation of orbits in proximity of asteroids using solar sails

Supervisors

Dr Matteo Ceriotti
Prof Colin McInnes

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Close-up observations of asteroids (particularly near-Earth asteroids, or NEAs) are important both for scientific reasons (many characteristics are largely unknown today, and asteroids are very different one another), for planetary protection and for future exploitation of their resources (metals, water). A possible way to visit multiple asteroids is with a solar sail spacecraft. A solar sail is a large, reflective and lightweight membrane that is deployed from a spacecraft and provides a thrust by reflecting the photons from the sun. This is appealing because it generates a small, but continuous, acceleration over time without propellant expenditure, enabling high-Δv missions.

Research in this group has shown the potential of solar sails to enable missions that can visit up to five near-Earth asteroids within ten years. In addition, trajectories were found for a variety of combinations of NEAs, as well as launch dates, demonstrating the flexibility offered by this type of propulsion. While the interplanetary journey is possible, a further research challenge is how to orbit around (or in proximity of) the asteroids: this PhD will investigate the dynamics and trajectories in proximity of the asteroids, together with the transfer to/from the interplanetary legs.

Asteroid orbits are challenging for several reasons: asteroids are highly irregular bodies, and their shape and density are not known in advance; in addition, most are tumbling, generating an irregular and time-varying gravity field. An additional challenge comes from the use of the solar sail, whose acceleration can only be controlled, through attitude manoeuvres, within certain limits and constraints.

Multi- and irregular body dynamics will be used. It is likely that an initial approach will be based on the energy levels and zero-velocity curves, to ensure bounded trajectories near the asteroid. Further research will involve numerical optimisation to target specific trajectories that maximise scientific return, reliability, and/or other merit figures. The ultimate goal is to design and optimise complete trajectories that inject into asteroid orbit starting from the interplanetary phase, orbit around the asteroid for a desired amount of time, and eventually depart into the next interplanetary transfer.

The PhD will involve both analytical and computer-based (numerical) research, preferably in the MATLAB and/or Mathematica environments. The ideal PhD candidate will have a degree in aerospace engineering or applied mathematics, and an excellent track record, preferably including evidence of outstanding research, such as previous awards and/or publications.


Nanosatellites beyond Earth orbit: CubeSats for deep space

Supervisors

Dr Matteo Ceriotti
Dr Patrick Harkness

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

A CubeSat is a nanosatellite of 10x10x10 cm of size and up to 1 kg of mass. Its frame and components are highly standardised, allowing a high degree of modularity and use of off-the-shelf parts. In addition, due to its small size and mass, it can be launched economically as secondary payloads by means of launchers carrying other conventional spacecraft. Because of these advantages, CubeSat platforms have been widely used for educational purposes, to test new technologies in space inexpensively, and lately also for science.

However, the platform also has significant limits with respect to conventional spacecraft, in terms of thrusting capabilities, attitude control, electrical and computational power available, telecommunication data rate, and radiation shielding. Most importantly, there is a strict limit for the maximum total mass. For these and other reasons, so far most CubeSat missions have flown in low Earth orbit (below 1000 km), and naturally deorbited and burned into the atmosphere at end of life.

This PhD will investigate the trajectory and propulsion system design for future interplanetary CubeSat-like nanosatellites, for high Earth orbits, deep space (e.g. Lagrangian points), Moon missions, near-Earth asteroids and beyond. Starting from an analysis of the limitations of current nanosatellite technology, this study is an exciting opportunity to explore new very-low-Delta-V trajectories and key propulsion technologies which will enable this platform to overcome the limitations mentioned before, and to fly to distant targets. For example, the limited thrusting capabilities of CubeSats require the use of advanced, highly-efficient engines (e.g. pulsed plasma thrusters), but reflective deployables (solar or wind sails) could also be used for photonic propulsion; the two systems could also be combined to maximise the benefit of both. Together with the system design, the study will identify potential mission scenarios that will benefit most from the deep-space nanosatellites, either in terms of cost reduction or increased return. The potential of using nanosatellites beyond low Earth orbit in the near future is huge. First and most importantly, it will enable low-cost deep space exploration. In addition, the modularity of the platform opens the way for mass production of similar spacecraft at relatively low cost, thus envisaging the use of multiple spacecraft in swarms or formations to achieve a common mission goal. Examples are real-time global remote imaging of the Earth or simultaneous local sensing of the space environment at different locations.

In addition, the candidate will take the lead of the development of a CubeSat engineering kit, which can be used for proof-of-concept implementation and testing of the solutions being investigated. In addition, this will open collaborations with the Physics & Astronomy (payloads, interaction with the space environment), Computing Science (on-board computer) and Electronics & Electrical Engineering (power system, electronics).

The ideal PhD candidate will have enthusiasm, a degree in aerospace engineering or applied mathematics, and an excellent track record, preferably including evidence of outstanding research, such as previous awards and/or publications.


Coupled trajectory and economic analysis of near Earth asteroid resources

Supervisors

Prof Colin McInnes
Dr Matteo Ceriotti

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Near Earth Asteroids (NEA) represent an important resource for future commercial and scientific space ventures through the in situ provision of water and other material resources. This has been recognised by growing space agency and venture capital interest in such resources.

Our prior work has focused on minimising capture energy requirements to lower costs. However, aside from minimising capture energy, trajectory design which maximises economic return on investment will be investigated as a key consideration for future commercial ventures. Here the total time taken for NEA resources to be returned to near Earth space is also key, since interest will accumulate on debt financing. Trajectory design will therefore be a function of capture energy requirements and roundtrip mission duration, leading to rich new families of trajectories which have been little explored.

This integration of trajectory optimisation and economic modelling is an exciting research challenge. Open questions to be addressed include the optimum selection of propulsion technology. For example high specific impulse, low thrust propulsion (including solar sails) will minimise propellant mass requirements, while low specific impulse propulsion may lead to shorter round-trip transfer times. This will lead to a tight coupling between engineering requirements and economic Net Present Value (NPV) which will be explored.

In particular, solar sailing offers an efficient means of returning NEA resources by leveraging light pressure rather than using propellant. A key research challenge will be to understand the optimum solar sail payload mass fraction to maximise the long-term rate of return of resources; a large mass per unit area will deliver a significant payload but with a long total trip time. Moreover, a number of large solar sails may be considered cycling between near Earth space and a set of suitable target objects for resource extraction.

Candidates should have a strong interest in mathematical modelling. Prior experience of orbital dynamics and MATLAB would be welcome.


Energy

Harvesting energy from engine exhaust gases with an Organic Rankine Cycle (ORC) system

Supervisors

Dr Zhibin Yu

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The thermal efficiency of modern internal combustion engines is limited to 20-40%. For a typical medium-size passenger vehicle in urban traffic conditions, about 33% of the thermal energy from fuel combustion within the engine is carried away by exhaust gases and about 29% is carried away by cooling water and heat radiation. The temperature of the exhaust gases from internal combustion engines usually vary from 200 to 600 C. This makes the exhaust gases very attractive for energy harvesting applications.

Organic Rankine Cycle (ORC) is a thermodynamic process similar to the conventional steam power cycle, but uses organic fluids (e.g. refrigerants) with much lower boiling points than water as working media, which makes such systems capable of utilising low temperature heat sources. Organic Rankine Cycle system is believed to be one of the most promising technologies for energy recovery from engine exhaust gases. However, a key challenge of such an application is that the temperature and flow rate of vehicle engine’s exhaust gases strongly depend on its operation under real road conditions. As a result, the energy contained in the exhaust gases fluctuates all the time. Hence, the design of ORC system for such application is far more complicated than other applications with a steady heat sources.

In response to this this challenge, this project will develop a dynamic ORC system which can adjust the key parameters of the power cycle to match the changing heat source parameters including both temperature and flow rate. As such, both the reliability and thermal efficiency of the ORC systems can be improved. This project will be carried out alongside with and benefit from two recently funded projects in this area. The project will start with a dynamic modelling of such ORC systems. Experimental work will then be conducted using an experimental prototype to gather the required data to validate the developed dynamic model. The verified model will then be used to simulate and improve the performance of the prototype in the future.


Modelling of heat pump combined subsurface seasonal thermal energy storage for district heating and cooling

Supervisors

Dr Zhibin Yu
Dr Neil Burnside

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

For areas with continental climate, cooling is needed for summer, and heating is required in winter. It is reported that at least one trillion kilowatt-hours of electricity have been consumed by air conditioning systems during summer every year in the world. In the meantime, huge amount fossil fuel (i.e., coal and natural gas) are consumed to provide heating in winter. Ground sourced heat pump is an attractive solution to provide renewable heat and cooling to reduce the consumption of fossil fuel. Furthermore, earth’s shallow subsurface provides a huge and natural potential for heat storage, which can be utilized to for seasonal thermal energy storage so that the waste heat generated by air conditioners during hot summer can be stored underground and to recover it later during periods of high demand for space heating in winter.

This project will investigate the concept of ground sourced heat pump combined with subsurface seasonal thermal energy storage for district heating and cooling. An integrated dynamic modelling tool needs to be developed to analyse the heating and cooling demand, various heat pumps and their performance at changing weather conditions, the charging and discharging processes of subsurface energy storage system, as well as the losses of stored thermal energy over time. The optimisation needs to be achieved at the whole system level over the year.  Combined with the weather data, the outcome of this project would lead to a computational package for designing and analysing such integrated energy systems.


Harvesting energy from engine exhaust gas with a thermoacoustic generator

Supervisors

Dr Zhibin Yu

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The thermal efficiency of modern internal combustion engines is limited to 20-40%. For a typical medium-size passenger vehicle in urban traffic conditions, 33% of the thermal energy from fuel combustion within the engine is carried away by exhaust gases and 29% is carried away by cooling water and heat radiation. The temperature of the exhaust gases from internal combustion engines usually vary from 500 to 900 ◦C. This makes the exhaust gases very attractive for energy harvesting applications. A thermoacoustic engine is essentially the acoustic equivalent of the Stirling engine. It employs a delicately designed acoustic network to force the gas parcels within the regenerator to experience a thermodynamic process similar to the Stirling cycle. In this way, it can convert thermal energy to mechanical power. The acoustic power can then be utilised to drive linear alternators to produce electricity. Thermoacoustic engines have several advantages over conventional Stirling engines such as simplicity, reliability and low cost. This project firstly will develop an improved model to optimise whole system, and will then focus on the optimisation of the heat exchanger which extracts heat from the exhaust gases. Novel designs need to be explored and examined, both numerically and experimentally, to minimize the additional backpressure applied to the engine and to ensure that a high degree of heat transfer efficiency is maintained. A prototype of such a thermoacoustic generator will be built and tested and the experimental results will be compared to the simulations in order to further improve the modelling.


Mapping the techno-economic and environmental sustainability of decentralized bioenergy systems for food waste disposal in megacities

Supervisors

Dr Siming You
Dr Zhibin Yu

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Food waste management is a great challenge for megacities. The improper disposal of food waste could potentially lead to the concerns of public health and hygiene. Traditional centralized disposal methods such as landfill and incineration generally have the shortcomings of significant greenhouse gas (GHG) and toxic by-product emissions. Bioenergy technologies such as gasification and anaerobic digestion (AD) serve as alternative solutions for food waste disposal and could convert food waste into valuable products such as syngas, biochar, and biogas. Both gasification and AD are suitable for small-scale, decentralized deployment which offers some advantages including the reduction of transportation expenses and minimization of pathophoresis possibility. What makes decentralized systems outstanding is the potential to foster a culture of energy and environmental conservation and sustainability by bringing residents and communities closer to the notion of sustainability. Traditional centralized systems are both psychologically and physically distant away from the residents and communities, which imperceptibly dilute their awareness of handling wastes responsibly. This is one of the reasons why some of the waste segregation initiatives faced great difficulties in achieving expected outcomes.

In this project, we will propose and optimize gasification and AD-based food waste disposal systems as an alternative to traditional disposal practices (e.g., landfill and incineration) in Glasgow, from an economic and environmental point of view. The systems will consist of waste pre-treatment units, reactors, product treatment units, and combined heat and power generation units and are used to achieve localized waste energy and resource recovery. Expected outcomes will include:

  • We will develop an integrated model by combining innovative township concepts with the effective and sustainable management of bioenergy systems. 
  • We will map the economic and environmental (carbon footprint) feasibility of the systems using Monte Carlo simulation-based cost-benefit analysis (CBA) and life cycle assessment (LCA), which will facilitate optimum system designing. 
  • The types of valuable products from the bioenergy systems that are most beneficial to Glasgow will be decided. The proposed systems will be compared to the existing practices in terms of economics and environmental sustainability. 
  • We will also identify the feasibility boundary conditions of the bioenergy systems for formulating effective policy and subsidy incentives.

Overall, this project will enhance Glasgow's technological freedom in disposing of food waste and project insights into the commercialization potential of decentralized bioenergy systems in Glasgow.


A feasibility study on integrating electric buses with biomass waste gasification for a greener public transport system

Supervisors

Dr Siming You
Dr Manosh Paul

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The current public transport buses are mainly powered by diesel in Glasgow. These buses generally use diesel based internal combustion engine (ICE) and, are noisy and a major contributor to urban air pollutants such as hydrocarbons, NOx and particulate matters (PM). Additionally, the diesel consumption by buses leads to a high greenhouse gas (GHG) footprint for the public transport system. Fuel cell electric buses are excellent alternatives with the advantages of being less noisy and air polluting. However, the carbon footprint of fuel cell electric buses is largely dependent on the sources of hydrogen and will be carbon friendly if the hydrogen production process has a lower carbon footprint.

This project is a feasibility study on a hybrid system that combines fuel cell electric bus systems with decentralized biomass waste gasification-derived hydrogen generation systems in Glasgow. The hydrogen is derived from the shift reaction of syngas from the gasification of biomass waste which is overall carbon negative. Hence, deploying electric buses with the support from gasification stations will serve to improve the urban air quality of Glasgow and reduce the carbon footprint of Glasgow’s public transport system. We will map the economic feasibility, and carbon and PM emission saving potential of the hybrid system using Monte Carlo simulation-based cost-benefit analysis (CBA) and life cycle assessment (LCA). Optimum system configurations (e.g., scale, operating conditions of gasification, and selection of hydrogen storage techniques) will be decided. We will also compare the proposed system with the existing diesel-based system regarding economics and environmental sustainability. The economic and environmental impacts of the hybrid system on the overall public transport system and waste management system of Glasgow will be evaluated. Finally, relevant policy and subsidy incentives will be suggested based on the feasibility boundary conditions of the analysis.


Enabling design of future smart grids with renewable energy and plug-in electric vehicles

Supervisors

Dr Shengrong Bu

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The smart grid can optimize electricity generation, transmission, and distribution; reduce peaks in power usage; and sense and prevent power blackouts. Grid modernization will bring significant direct savings and other benefits.

One of the key challenges faced by the smart grid is to integrate large-scale renewable energy generation while maintaining grid reliability. Unlike conventional generators, renewable energy generators are highly intermittent and uncontrollable, making them difficult to widely integrate. Potential solutions include improved forecasts, demand shaping, electricity storage, and optimal grid operation. Addressing renewable energy integration into the smart grid alone is a compelling problem for a PhD project to consider. The project can first investigate the fundamental limits of grid reliability in the face of generation uncertainty, analyze existing options, and then design novel mechanisms.

Plug-in electric vehicles (PEVs) are becoming a promising alternative to conventional automobiles. However, PEVs not only significantly increase the average electricity consumption, but also generate very bursty demand patterns. It is critical to address challenges caused by the rapid growth of PEVs to the power grid. A potential interesting focus for a PhD candidate is to design charging strategies for PEVs to maximize customer benefit while satisfying system constraints. The work could be extended to consider PEV scheduling as a mechanism to partly absorb renewable energy variability.

In the smart grid, large amount of data with various communication requirements need to be transmitted, therefore, communication infrastructure is critical. However, many issues in the field of smart grid communication are still not well addressed. One potential research topic for the PhD candidate is to study the interactions and tradeoffs of scalability and reliability of networks, and then propose novel network design strategies and also design novel algorithms to achieve scalable and reliable network communication infrastructure in the smart grid.

The aim of this project is to propose solutions that will help increase utilization of renewable energy and reduce greenhouse emissions for both the power and transportation industries at minimum cost and maximum reliability.


Investigation of solar fuels production for energy storage and negative CO2 generation

Supervisors

Dr Nader Karimi
Dr Manosh C Paul

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Despite the existence of substantial concerns about global warming, combustion of fossil fuels continues to dominate the global energy production. As a result, every year millions of tons of CO2 is discharged to the atmosphere, accelerating the rate of climate change. Significant effort has been put into the development of renewable energy technologies but they still suffer from intermittency and storage problems. In particular, solar energy technology has gone through major advancements from economic and technical viewpoints. Yet, the energy storage issue remains as the main barrier against the large scale utilisation of this widely available source of renewable energy.

Solar fuel technology can offer an effective solution to the problems of solar energy storage and carbon dioxide capturing. This technology essentially uses the energy of the sun to turn CO2 and water back to fuels and hence it can be seen as the reverse combustion process. By doing this, solar fuel technology mimics what plants naturally do by photosynthesis. Through solar fuel technology CO2 generated by the combustion of renewable or fossil fuels can be turned back into conventional fuels usable by automotive, aviation and energy sectors. The energy of these fuels is supplied by the sun and no extra CO2 is released to atmosphere. Solar fuels can be alternatively consumed by petrochemical industry resulting in negative CO2 production.

The basic idea of solar fuels is well demonstrated at laboratory scale. Nonetheless, to turn this into a viable technology a number of engineering problems have to be resolved. These mainly include finding the optimal design of the production system (or solar reactor) for maximising the production rate and minimising the cost. This, in turn, calls for gaining a deep understanding of the complex interactions amongst the solar radiation, fluid dynamics and heat transfer processes and the chemistry of the conversion reactions. This PhD project concentrates on understanding such interactions with the aim of improving the design of industrial solar reactors. Computational Fluid Dynamics (CFD) investigations complimented with experimental measurements are employed to develop representative models of the essential processes involved in solar fuel production.

Applicants with strong academic background in mechanical, aeronautical or chemical engineering are encouraged to apply. For further information please contact Dr Nader Karimi (Nader.Karimi@glasgow.ac.uk) or Dr Manosh Paul (Manosh.Paul@glasgow.ac.uk).


Investigation of the dynamics of combustion in energy and aero-propulsion systems

Supervisors

Dr Nader Karimi
Dr Andrea Cammarano

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Decarbonisation of energy and transport systems is a massive task before industry, which mainly requires replacement of combustion of fossil fuel with a carbon neutral or low carbon alternative. Although significant effort has been put on finding substitutive technologies to combustion, such technologies still face major unresolved issues such as high cost and low energy efficiency. This keeps combustion amongst the main energy production technologies in the foreseeable future. Yet, to comply with decarbonisation policies, the fossil fuels are to be replaced by renewable and carbon free fuels. The latter includes bio-synthesis fuels, solar fuels and hydrogen. There is also a trend towards introducing blends of hydrogen and fossil fuels as an attempt to reduce carbon dioxide generation. Despite their environmental benefits, application of these new generation of fuels to gas turbines and aeroengines can lead to a range of instability issues, collectively called combustion dynamics. These include thermoacoustic instabilities, flame flashback and flame blow off. The first problem involves development of high amplitude pressure waves inside the combustor that can damage the structure of the combustor severely. The other two refer to the situations that the flame does not remain stabilised in the designed location and transiently propagates upstream (flashback) and downstream (blow-off). Occurrence of each of these instabilities interrupts the operation and often causes significant hardware damage. Thus, they should be detected and suppressed at the design stage. Yet, the essential physics of combustion dynamics are still largely unknown and hence their prediction is currently unpractical. It follows that understanding and modelling of combustion dynamics are central to the development of future low carbon combustion systems.

The current PhD project aims to address these issues through analysing the fundamental fluid dynamics and combustion of thermoacoustic instabilities or flame flashback. High fidelity numerical simulations (LES/DNS) supported by experimental measurements will be employed as the first task. This will be followed by a ‘low order modelling’ to provide a predictive tool for industrial use. The low order model takes significantly less computational resources compared to the high order numerical simulations, planned in the first task. Nonetheless, it still represents the main physics of the problem and can therefore predict the instabilities with a reasonable accuracy.

Applicants with strong academic background in mechanical and/or aeronautical engineering and also Applied mathematics and physics are encouraged to apply. For further information please contact Dr Nader Karimi (Nader.Karimi@glasgow.ac.uk) or Dr Andrea Cammarano (Andrea.Cammarano@glasgow.ac.uk). 


Optimisation of thermal energy storage in abandoned flooded mine workings

Supervisors

Dr Neil Burnside
Dr Zhibin Yu

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Thermal energy efficiency and sustainability is a major challenge, particularly for countries such as Scotland where heating represents nearly two thirds of overall energy demand. Abandoned flooded mines are present over vast areas of the UK and typically represent high-level environmental and financial liabilities. However, these remnants of our carbon-intensive industrial past also provide an exciting opportunity to develop sustainable low-carbon energy resources through geothermal energy production and thermal energy storage. This project will develop innovative strategies for evaluating the geothermal potential of flooded mine workings.

Thanks to Glasgow’s extraordinary industrial heritage, many parts of the city, including the University, are positioned over a large network of abandoned, flooded coal mine workings. This project will use the University of Glasgow (UoG) as its major case study. The UoG has a campus wide Combined Heat and Power (CHP) district heating scheme which heats buildings, generates a large percentage of campus electricity and makes a significant contribution to the UoG’s carbon footprint reduction target (20% per year). Efforts are underway to achieve further carbon emission reductions for the current scheme and to ensure low-carbon energy for a £1 billion plus campus expansion.

As to be expected with the local climate, the campus has a seasonally high heat demand in the winter months. Excess ‘waste’ heat generated by the existing CHP system could potentially be turned into a valuable resource to help meet campus winter heat demand if it could be successfully captured and stored.

A major challenge for heat storage is the capacity required to store enough useable heat for a long enough duration. Due to the warm embrace of the surrounding geology, the flooded mine workings insulate groundwater from seasonal variations in surface temperature. Add to that the enormous volumes of water involved (several million m3), and these flooded mine workings represent a fantastic opportunity for inter-seasonal thermal energy storage if their hydrological nature can be robustly characterised.


Opportunities in gasification and combustion engineering

Supervisors

Dr Manosh C Paul
Dr Nader Karimi
Dr Siming You

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Production of carbon neutral fuels is an essential step in reducing carbon emissions to atmosphere and mitigation of the global warming. Gasification is a key technology in realising this ambition and is therefore under intensive development worldwide. The School of Engineering at the University of Glasgow has a strong track record in the field of gasification and combustion engineering, with a number of ongoing collaborative projects focusing on biomass, waste and underground coal gasification. Gasification, which is a partial oxidation process, usually takes place at temperature 450-1350°C with very little air or oxygen, by which carbonaceous sources of energy are converted to synthesis gas (syngas) which ideally should comprise a well combination of hydrogen (H2) and carbon monoxide (CO). However, currently there is a lack of clear understanding of the gasification thermochemical processes (such as drying, pyrolysis, combustion and reduction) which lead to the production of impurities and emissions. A key research question that will be addressed in this PhD project is how to get the gasification process robust enough, thus enabling to produce sufficiently clean syngas from various feedstocks. How to reduce/remove the tar formation? Also how to make it CO2 neutral/negative? These are the challenging questions to be addressed through the project. The student will contribute to the development of advanced thermochemical as well as computational fluid dynamics (CFD) based techniques to first understand the gasification processes and then investigate how to improve the processes through systematic parametric optimisations. Gasification experiment will be performed to validate the modelling results, and combustion performance of produced syngas will also be investigated for potential downstream applications leading to the efficient generation of combined heat and power.

The candidate should have a strong academic background in mechanical, chemical or aeronautical engineering, or applied physics and mathematics. For further information please contact Dr Manosh Paul (Manosh.Paul@glasgow.ac.uk).


Performance improvement of thermal energy systems

Supervisors

Dr Manosh C Paul
Dr Nader Karimi

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Flow transition with thermal energy transport is a common phenomenon which occurs in almost every energy engineering system e.g. in cooling electronic devices, heat exchangers, solar thermal energy, nuclear reactor, thermal energy storage, and so on. This may occur in different flow conditions e.g. laminar, turbulent, with/without influence of any external effects such as the gravitational force. Understanding the transportation process of heat and mass energy is thus crucially important for improving the performance of any thermal engineering systems. This proposed PhD project aims to study the various flow phenomena which may be complex at some conditions/applications due to the interaction between the system’s operation and energy transportation. The research will therefore initially focus on the development of highly advanced computational fluid dynamics (CFD) based numerical methods with the aim to investigate those complex phenomena. Most recently in-house developed advanced large eddy simulation (LES) and direct numerical simulation (DNS) codes will be extended further, thus allowing investigation of the fundamental aspects of the problem associated with the fluid mechanics and heat transfer. The research may also be extended further by utilising an alternative fluid such as nanofluid to investigate the performance against a base fluid (e.g. air, water). This will further involve the study of multi-phase flow with an effect of a combination of the various fluid conditions such as particle size and concentration of nanofluid. A possible extension of the study will be the investigation of phase change martial/heat pipe technology for efficient heat energy storage application – one of the key energy strategies for the UK Government.

The candidate should have a strong academic background in mechanical, chemical or aeronautical engineering, or applied physics and applied mathematics. For further information please contact Dr Manosh Paul (Manosh.Paul@glasgow.ac.uk).


General PID control and applications to power conversion

Supervisors

Dr Keliang Zhou
Prof Yun Li

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Advanced power electronic converters, which can precisely and efficiently convert, control, and condition electricity, play a key role in the successful grid integration of different distributed generators, loads, and transmission devices. The global electricity processed by power electronic converters would be up to 80% in the very near future. The power quality and even the stability of electrical power systems would be affected and even determined by massive interfacing power converters. As a consequence, power converters highly demand optimal control strategies for periodic voltages/currents compensation to assure good power quality and stable power system operation. Simple but very effective periodic controllers offer attractive control solutions to power converters. The proportional–integral–derivative (PID) controller is the commonly used periodic controller in industrial applications.

The project is dedicated to comprehensively investigate the control, compensation, and filtering of periodic signals in power electronic power processing, aiming to provide a general proportional-integral-derivative control solution to periodic signal compensation in extensive engineering applications, such as ultrahigh accuracy nano-positioning, grid integration of renewable generation via power converters, power quality systems, and so on.


Electric vehicle control for smarter and greener grid support

Supervisors

Dr Keliang Zhou
Dr Shufan Yang

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Addressing challenges from greenhouse emission and energy security, more and more distributed renewable energy generators such as solar photovoltaics and wind turbines are integrated into the grid. However, due to their intermittent nature, high penetration of those alternative sources will cause a number of problems to the grids such as supply and demand mismatches, voltage and frequency violations, which results in equipment damages and network instability.

Electric vehicles provide a feasible solution to tackle the problems of high penetration of renewable energy generation in the smarter grids. Bidirectional Grid-to-Vehicle and Vehicle-to-Grid services could mitigate the variation due to renewables.


Thermochemical extraction of high value products from biomass

Supervisors

Dr Ian Watson
Dr Julian Dow

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Biomass and waste feedstocks represents a significant opportunity to produce sustainable sources of energy whilst extracting valuable platform chemicals using biorefinery concepts.  The work will undertake novel approaches to extract liquid biooils using a range of thermochemical based processes and investigate applications that optimise the end energy and product yield.  Thermochemical treatments include: torrefaction, pyrolysis and gasification.  Process modelling will be done to determine the impact of feedstock and process treatment on the end product and will be supported by experimental work to identify novel applications of extracts.


Materials, Design & Manufacturing

Multi-objective optimisation methods for minimising tardiness, electricity consumption and cost in dynamic job shops

Supervisors

Dr Ying Liu
Prof Yun Li

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Manufacturing enterprises nowadays face the challenge of increasing energy prices and requirements to reduce their emissions. Most reported work on reducing manufacturing energy consumption focuses on the need to improve the efficiency of resources (machines).The potential for energy reducing at the system-level has been largely ignored. At this level, operational research methods can be employed as the energy saving approach.

Job shops are widely used in the manufacturing industry, especially in small and medium enterprises. In the future, the uncertainty within the system will be increased as the result of mass customisation and personalisation. Optimisation techniques to solve the uncertainties and maintain the robustness of the manufacturing system will become increasingly important.

Reducing the electricity consumption in a dynamic job shop will be studied in this research. Existing dynamic scheduling algorithms will be extended to reduce the electricity consumption and improve productivity for job shops where the components arrive at the production system at randomly distributed times. This will extend the applicable range of the developed multi-objective optimisation methodology to include stochastic manufacturing systems which are widely used in the real manufacturing world.

Thus, in this project, meta-heuristics based optimisation approaches which include electricity consumption as an objective to minimise when uncertainties such as machine breakdown occur in the production system at randomly distributed times will be developed. Reinforcement learning will be used to identify the pattern of uncertainties in the manufacturing system.


Artificial Intelligence based multi-objective optimisation dispatching rules for energy management in flexible manufacturing systems

Supervisors

Dr Ying Liu
Prof Yun Li

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Energy is one of the most vital resources for manufacturing. In the last 50 years, the consumption of energy by the industrial sector has more than doubled and industry currently consumes about half of the world’s energy.

Flexible job shops are widely used in the manufacturing industry, especially in small and medium enterprises. For instance, original equipment manufacturers in the aerospace industry usually employ the flexible job shop manufacturing system for their capability to satisfy the increasingly diversified customer demands. In the future, the requirement on the system flexibility will be increased to adapt mass customisation and personalisation. On-line decision making for the flexible manufacturing system will become increasingly important.

The main goal of this project is to address the multi-objective flexible job shop scheduling problems with reducing energy consumption and its related cost as part of the objectives. Electricity consumption and electricity cost reduction have not been well investigated in the multi-objective scheduling approaches for a typical flexible job shop manufacturing system. The lack of a more fundamental energy saving oriented flexible job-shop model and its related scheduling techniques is a significant gap in the current research which needs to be addressed.

A dispatching rule is a rule that prioritises all the jobs that are waiting for processing on a machine, which is widely used in the manufacturing system for decision support, especially for the on-line environment. The prioritisation scheme may take into account the job’s attributes, the machines’ attributes as well as the current time. Compared to exact algorithms and meta-heuristics, dispatching rules are easy to implement and fast to calculate, and can be used in real time to schedule jobs. In other words, dispatching rules usually can deliver reasonably good solutions in a relatively short time.

Thus, in this project, dispatching rules which include electricity consumption as an objective to minimise when jobs arrive at the flexible production system at randomly distributed times will be developed. Techniques like genetic programming will be used to construct the composite dispatching rules. Reinforcement learning will be used to identify the electricity consumption pattern of assets in the manufacturing system.


Manufacturing and properties of titanium porous structures

Supervisors

Dr Peifeng Li

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Open-cell titanium alloy porous structures (foams) are attractive materials for applications such as sound damping, heat exchange, impact energy dissipation and tissue engineering. The structures with periodic unit cells can be manufactured via additive manufacturing (3D printing) such as selective laser melting (SLM). The combined slurry coating and powder metallurgy approach can produce porous structures with irregular unit cell topologies. It has been a challenge to select the appropriate manufacturing route for titanium porous structures as different routes lead to dissimilar final properties.

This project aims to comparatively evaluate the properties of titanium porous structures fabricated by the different manufacturing processes. Research will focus on both additive manufacturing for periodic unit cell topologies and slurry coating with powder metallurgy for irregular topologies. Mechanical, thermal, and/or acoustic properties of porous structures will be quantitatively characterised and compared. In particular, the effect of processing parameters will be investigated to improve the manufacturing processes. Numerical simulation such as FE, CFD will also be used to explore the properties of titanium porous structures.


Deformation and failure micromechanisms in additive manufactured (3D printed) metals

Supervisors

Dr Peifeng Li

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Additive manufacturing (3D printing) of metals is one of the significant research focuses in the Materials and Manufacturing Group. Selective laser melting (SLM) has been successfully used to manufacture lightweight metallic structures with complex geometries, such as microlattice structures, which can potentially be used in aerospace components and biomedical implants. Despite the numerous investigations on bulk mechanical properties of SLM metals, there is a scarcity of research on the underlying deformation and failure micromechanisms that determine the bulk behaviour.

This project aims to investigate the underlying micromechanisms on the deformation and failure process of metals (e.g., titanium alloy, aluminium alloy and stainless steel) made by the SLM technique using advanced experimental characterisation approaches such as in-situ SEM. Research will focus on how the microstructure and micro-texture in SLM metals in very small length scales affect the micromechanisms on both the elastic and plastic deformation behaviour. The constitutive behaviour for SLM metals will also be formulated for FE modelling of SLM components to predict their service performance.


Thermoforming of advanced thermoplastic composites

Supervisor

Dr Philip Harrison

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Description

Advanced composites are attracting a huge amount of interest in the automotive sector where government legislation on emissions means that light-weighting is now a primary driver in the design process. Due to their enhanced damage tolerance, fast production times and potential recyclability, advanced thermoplastic composites are of particular interest. However, current computer aided manufacture modelling tools for these materials are inaccurate and the lab time required to characterise their forming behaviour for input into computer simulations is prohibitive. The goal of this project will be to predict the comprehensive forming mechanics of advanced thermoplastic composites directly from the matrix rheology and fibre volume fraction of the composite, a capability that will lead to significant reductions in design and manufacture costs, facilitating the wider use of advanced thermoplastic composites in the automotive sector and ultimately contributing to a greener economy. With the skills and experience gained during the project, can expect excellent employment opportunities in both the aero and automotive sectors.


Virtual manufacture with advanced carbon composites: from manufacture to structural optimisation

Supervisor

Dr Philip Harrison

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support.

Description

Composite materials are an exciting and fast-moving research topic. Developments in this area are driving economic growth and transforming society in a number of fundamental ways; from the reduction of carbon emissions through light-weighting, to the development of multi-functional smart composites that can self-sense and self-heal damage for low-cost in-service maintenance. This project will focus one of the most promising fully-automated low cost   manufacture   techniques in manufacturing advanced composites,   namely,   sheet   thermoforming. For the PhD student, the crux of the project will be to develop a mapping interface allowing fibre angle predictions following sheet forming to be fed into subsequent mechanical   simulations for structural analysis and for the prediction of warpage due to thermally generated residual stresses. The student will gain expertise in composite manufacturing, computational modelling and material characterisation. Computational modelling will involve FEA and coding in Matlab and python. Once the mapping algorithm is implemented, genetic algorithms will be used to optimise structural performance (minimise mass) and control warpage. With the skills and experience gained during the project, can expect excellent employment opportunities in both the aero and automotive sectors.


Communications, Sensing and Imaging

Big data aware 5G wireless networks

Supervisors

Dr Shengrong Bu

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Rapid growth in commercial data services over several decades has lead to the “Big Data” era. Among various data sources, mobile wireless networks are one of the primary data contributors. Due to the increasing applications supported by smart phones and other smart devices, mobile data traffic is expected to increase 5 times within next 5 years.

5G wireless networks are under research to address the following challenges: higher capacity and data rate, lower end-to-end latency, larger numbers of connected devices and better quality of experience for users. The requirements for 5G are on a completely different scale compared to existing deployed 4G networks. Various novel techniques, e.g., small cells, cloud-based radio access networks, and software-defined networking will be used in 5G networks to address the challenges. With the use of these techniques, high levels of signaling traffic will be generated, and this traffic will increase 50 percent faster than mobile data traffic. Therefore, 5G wireless networks will face even more difficult challenges in the big data space.

Initial work has been proposed two approaches to manage big data traffic in 5G networks: 1) exploit various signal processing methods to reduce data traffic within the network. 2) improve wireless system design so that spectrum efficiency, computing capacities and link capacity will be increased. The growing data volume is expected to overwhelm these combined processing and link capacity improvements. Therefore, it is urgent to seek fundamentally novel approaches to address big data related problems.

For this project, the PhD candidate will have the chance to contribute to an exciting new research area: big data aware 5G wireless networks. Compared to the existing work where big data is considered as a burden, this research area will consider the opportunities provided by big data. Specifically, the scholar will research how to extract important individual and social features from wireless data traffic. Novel data analytics methods need to be proposed for data from 5G networks to handle their high dimensionality and complex features. Second, the candidate will exploit the new extracted context information for optimizing and managing resource allocation in 5G wireless networks to improve wireless service quality and network efficiency.


Adaptive and energy efficient cryptographic systems for wireless security of connected autonomous vehicles

Supervisors

Dr Petros Karadimas

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Cryptographic key generation exploiting physical layer attributes of the wireless communication medium is a quite promising scientific domain in terms of low energy consumption and absence of centralized public key infrastructure. The main idea is the exploitation of the inherent temporal, spatial and frequency variations of propagation channels for symmetric key generation (identical keys at the transmitter and receiver sides). Ongoing research deals with several features of such cryptographic algorithms, including quantization of channel response for generating bit strings and information reconciliation in order the bit strings generated at the transmitter and receiver to become identical. However, communications between vehicles are quite complex and very rapidly time varying, whereas vehicular communication channel features affecting the temporal variability have been never taken into account in terms of adaptability and scalability, i.e., adaptability to every wireless scenario ranging from dense urban, to open rural and high speed/highway environments. It is exactly the purpose of this project to investigate and propose algorithmic solutions for cryptographic key generation for providing physical layer security in vehicular communications, i.e., communications between vehicles. The analysis will incorporate in a comprehensive statistical manner all the physical attributes of the vehicular wireless channel that can affect key generation performance including, for example, three-dimensional multipath propagation, scatterers’ mobility (e.g., mobility of vehicles, pedestrians) and strong reflected multipath components. The target is to end up with configurable cryptographic algorithms with parametrical quantization and information reconciliation routines. Moreover, embedding such a key generation algorithm to realistic vehicular communication transceivers requires in the very beginning a channel estimation module from which accurate vehicular channel estimates will arise. It is an additional purpose of this project to investigate on different channel estimation techniques, propose new ones and study on their feasibility for the undertaken research. The PhD candidate will become a member of the cross disciplinary Cyber Physical Systems Security research cluster formed by partners from the RF Propagation group from University of Glasgow and the Cyber Research Institute from the University of Wolverhampton. Basic knowledge of software engineering, stochastic processes, digital communications and information theory is required, however the uttermost features for such interdisciplinary PhD research are strong self-motivation and passion for unexplored new knowledge that pushes forward the current trends and bounds of engineering science. Due to the wide scope of this project, apart from candidates with background in electrical/electronic engineering and telecommunications, those with background in mathematical sciences, algorithm development and software engineering will be considered as well. For further details, please contact (it is strongly recommended) with the principal investigator and supervisor of this research, Dr. Petros Karadimas: Petros.Karadimas@glasgow.ac.uk.


Bandwidth and energy efficient RF circuit designs for mm-wave communication systems

Supervisors

Dr Petros Karadimas

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Mm wave communications are emerging as a future technology to support Gbps data rates in a great diversity of 5G and beyond communication scenarios. The design of compact passive antenna systems that can be packed into the limited space of wireless devices alongside with smart signal processing techniques appears to be the key enabler of implementing mm wave communication systems to support such high data rates. The design of such compact systems should lead to optimal bandwidth efficiency or in other words channel capacity in order to support Gbps data rates. On the other hand, given the energy demands for reliable operation of such systems and limited battery capabilities, the designs should be optimized against energy performance criteria as well. This PhD research will come up with efficient RF circuitry designs as part of an optimal, in terms of both bandwidth and energy efficiency, and complete “RF circuit/printed multi-antenna/mm wave propagation channel” multiple input-multiple output (MIMO) ecosystem. The bandwidth efficiency or channel capacity will be optimized first, followed by the energy efficiency optimization of the end-to-end MIMO system. Energy efficiency represents the SNR per information bit of transmission and is mathematically related to the bandwidth efficiency. The current state of the art RF trends of analogue, digital and hybrid beam-forming will be analyzed and compared against the bandwidth and energy efficiency key performance indicators. The first phase of this PhD project will analyze the theoretical aspects of RF circuit designs for mm wave communication systems by incorporating in a comprehensive manner all physical mechanisms underpinning the performance of a complete “RF circuit/printed multi-antenna/mm wave propagation channel” MIMO ecosystem. The second phase will be the efficient design and implementation of such RF circuits by exploiting the unique facilities and equipment of the School of Engineering such as those of James Watt Nanofabrication Centre:  http://www.jwnc.gla.ac.uk/. Basic knowledge of Electromagnetics, Antennas, RF systems, Communication Theory and Digital Signal Processing is required, however the uttermost features for such ambitious PhD research are strong self-motivation and passion for unexplored new knowledge that pushes forward the current trends and bounds of engineering science. For further details, please contact (it is strongly recommended) with the principal investigator and supervisor of this research, Dr. Petros Karadimas: Petros.Karadimas@glasgow.ac.uk.


Design of machine learning-based schemes for wireless caching in 5G networks

Supervisors

Dr Muhammad Majid Butt
Prof Muhammad Ali Imran

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

This research will explore one of the key enablers of fifth generation of wireless networks (5G) and beyond, proactive wireless caching. The exponential growth of smartphone capabilities has enriched the mobile users experience with online multimedia streaming. It is estimated that mobile traffic accounts for the 50 percent of data traffic. The massive deployment of small cell networks will play a key role for capacity and coverage enhancement of 5G systems. However, due to huge traffic generated by mobile users, the existing backhaul connection will experience congestion, and the deployment of high-speed backhaul will result in tremendous operational expenditures. In addition to operational cost, data fetching using backhaul connection is not an attractive option for the applications requiring latency of the order of 1 ms, e.g., remote surgery. These so called ‘tactile’ applications need proactive radio resource management. One of the promising techniques to solve backhaul bottleneck issue is a paradigm of proactive caching at network edge that exploits the recent advances in storage, context-aware networking, big data, and heterogeneous networks. The nodes at the edge of the network (closer to the user) can predict the popular content, store the content, and deliver it when requested by a user. However, most of the work in literature does not consider the dynamic nature of the popular content and assumes that content popularity is fixed. Since the content popularity is time-varying, caching can only be optimized if the fresh view of the system is maintained. This requires huge data collection, data processing, and statistical inference from this data. This can only be realized by integrating machine learning (ML) tools across the wireless infrastructure and end-user devices  This project will design new proactive cooperative caching techniques that combine ML for a time-varying content popularity. We will use ML algorithms such as reinforcement learning, neural networks, and deep learning to predict the popular content and update it with time. Therefore, the objective of this research is to design and develop cooperative content caching and delivery policies using ML techniques for proactive caching at network edge. The central research question to be addressed is thus whether it is possible to minimize the energy consumption and transmission delay by the development of ML-based cooperative caching schemes for time-varying popularity in wireless networks. Moreover, we will consider contacting network operators to collect real-time data (if possible) of the users for content prediction and analysis.


Economics of spectrum sharing in 5G wireless networks

Supervisors

Dr Muhammad Majid Butt
Prof Muhammad Ali Imran

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

It is a time of unprecedented change, where traffic on telecommunication networks is growing exponentially, and many new services and applications are continuously emerging. In contrast to previous generations of mobile communication, where broadband services were the main source of revenue, 5G networks bring new challenges as well as opportunities for the mobile operators. Internet of Things (IoT), vehicular networks and smart grid are some of the examples of these emerging business market for telecom operators. As compared to mobile broadband (MBB) services, these services have different requirements in terms of rates, latency and reliability of data. For example, vehicular services have strict latency constraints but the coverage requirements are limited. Smart grid applications do not have stringent rate and latency requirements and mobility is very low. These attributes of various services demand for different approach for spectrum allocation, as compared to the approaches adopted in previous generations of communication where licensed spectrum is provided for broadband services. In 5G networks, new spectrum use opportunities are available where unlicensed bands and licensed bands are being exploited to share and utilize the spectrum more efficiently. License Assisted Access (LAA), Licensed shared access (LSA), Spectrum Access System (SAS), Multifire are just a few examples of such spectrum sharing systems. These spectrum bands have various attributes in terms of propagation models, geographical coverage area and penetration loss. Depending on the attributes of the services and various spectrum bands available through the combination of licensed, unlicensed and shared access, various spectrum access options can be considered. It is important to evaluate the various trade-offs involved in the allocation of spectrum to maximize the utility (“well-being” function) of the network.  Various economics theories have been used to deal with these kinds of trade-offs where multiple sellers and buyers are available in an open market, e.g.,

  • Game theory and its subfields (Auction theory, Team decision theory);
  • Matching theory;
  • Contract theory (applied to spectrum sharing, wireless communication networks);
  • Pricing (e.g. dynamic pricing).

The proposed PhD project will apply some of the ideas from these (and other) economic theories to evaluate various options for spectrum access where we treat the operators that own the licensed spectrum as spectrum providers, while the operators that provide telecom services without actually owning the spectrum as spectrum buyers. The project will address the spectrum and/or revenue sharing models in a spectrum trading market considering the competitive nature of the market.


Distributed data delivery and resource management algorithms for disaster management

Supervisors

Prof Muhammad Ali Imran

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Although there is significant progress in the provision of higher data rates, low latency and energy/spectral efficient wireless communication systems over the years, the emergency management system (EMS), which is conventionally based on wireless communication, has often been left behind. Given that current platforms are susceptible to being destroyed by disasters, it is paramount to implement robust EMSs to handle such situations. Moreover, current platforms require human intervention, making them expensive to operate and maintain in times of disaster. The next generation EMS needs to be an effective and efficient critical communication platform that provides situational awareness and coordination. Fortunately, the advent of the 5G mobile system presents a new opportunity for research and the design of a distributed, autonomous and resilient EMSs.

A single point of failure is a major concern in most centralised solutions and its detrimental effect is even more pronounced in emergency scenarios. Hence for improved resilience, this PhD will focus on proposing distributed data delivery and resource management algorithms that are suitable for emergency situations. The fundamental theoretical underpinning of stochastic geometry will be used to model the nodes and the network entities’ locations along with appropriate traffic models to develop theoretical, as well as practical understanding of the system. The performance of these algorithms will be evaluated in terms of carefully selected key performance indicators. 


Mobility and handover management in millimetre wave cellular networks

Supervisors

Prof Muhammad Ali Imran

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Millimetre wave (mmWave) communication is one of the enablers to meet the requirements of future communication systems, in particular, to provide the high bandwidth necessary to achieve the expected high data rates for extreme broadband. However, to overcome the adverse propagation conditions at higher frequencies, high gain directional beamforming is required at the transmitter and receiver. This kind of directional, coordinated communication leads to a new design paradigm for future systems, which is much more complex than their counterparts operating at lower frequencies.

This PhD will address the handover and mobility management in mmWave cellular networks. In particular, the directivity nature of the base station and user equipment beams will be exploited for developing novel handover and mobility management solutions. The architectures to be considered will include the dual base station option, which integrates the mmWave and microwave frequencies on the same node. Inter-frequency (mmWave and microwave) deployment scenarios without such integration will also be analysed. The PhD will investigate the performance of such architectures in terms of the initial cell discovery, inter-frequency measurement energy consumption, handover failures and seamless mobility. The impact of proactive content caching on handover and mobility management in such deployment will also be analysed.


Popup networks for crowd management

Supervisors

Prof Muhammad Ali Imran

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Mobile networks are generally overprovisioned to accommodate for reasonable increase in user density and the associated rise in traffic. However, in heavily crowded events, mobile networks face increase in both voice and data traffic by orders of magnitude, which severely degrades the performance of the network through increase in transmission delay, packet loss, dropped calls and reduced throughput. To overcome this, network operators deploy additional base stations within the crowded area to reduce the load on existing base stations and increase capacity. Although this approach brings about considerable relief to the network, deployment is very time consuming and expensive.

Due to the nature of cellular networks being heterogeneous, this PhD will incorporate the principles of self-organisation (configuration, optimisation and healing) with the use of entities such as small cells, fixed relays, Unmanned Aerial Vehicles (drones) and even mobile devices in crowded networks, in order to reduce human intervention, as well as deployment time and cost. A load aware modelling of the resource allocation for emergency scenario will also be carried out using stochastic geometry.


Mobility prediction and handover techniques for control and data plane separated networks

Supervisors

Prof Muhammad Ali Imran

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The ultra-dense deployment requirement of future cellular networks implies multifarious complexity and a significant rise in cost of operation. One way to address this challenge is via novel Radio-Access-Network (RAN) architecture such as the Control and Data plane Separated (CDS) architecture, and equipping them with the next generation Self-Organising Network (SON) functions.

In CDS architecture, small cells (Data Base Station – DBS) provide data plane to high rate users on an on-demand basis. Overlaying larger cells (Control Base Station – CBS) provide control plane to high rate users, and both control and data plane to low rate users. The CDS architecture enables having localised database at the CBS that consist network and user measurement such as received signal strength and quality reports, DBS load conditions and user mobility report among others. Intelligence extractable from the database can be used by a CBS to perform a number of SON functions such as dynamic switching on/off of DBS, self-healing (cell outage detection and compensation) and self-optimisation (i.e., coverage, energy efficiency and radio resource optimisation).

This PhD will investigate the use of machine learning for SON functions in the context of CDS. Advanced mobility prediction techniques and protocols related to resource reservation, predictive DBS handover, and predictive DBS switch on/off will be designed for the CDS architecture. The novel algorithms will be validated on the 5GSON testbed being built at the University.


Ultra-low latency and high reliable communications for wireless control

Supervisors

Dr Lei Zhang
Prof Muhammad Ali Imran

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The evolution of the first four generations of cellular systems has successfully connected people and brought us into a cyber world. This is essentially based on information exchange, which allows us to communicate to people and access the internet almost at anytime and anywhere. In future fifth generation (5G) cellular networks, a significant number “things”, e.g., sensors and actuators, are expected to be connected, which allows us to interact with the physical world in a read-time fashion. In other words, we are stepping into a cyber-physical world, which offers many emerging applications, such as tactile internet, industrial automation, self-driving vehicles, remote surgery, smart grid, and more. The essential function that enables these emerging applications is the real-time wireless control, which is beyond the conventional cellular networks that are designed for supporting massive high data-rate information exchange.

This project is to design ultra-low latency and high reliable communication solutions for wireless control. The joint design between control and communication will be considered. Specifically, the basic feedback control and wireless communication model will be built. Then, we will analyse the dynamic interactions between the two sub-systems, where both computer simulation and theoretical analysis will be conducted. The project also designs algorithms to optimize overall control-wireless performance in terms of latency, throughput and reliability, etc. In addition, we expect to build a test bed to verify the developed algorithm, where a robot is controlled through a shared wireless network.

 An ideal candidate should have experience in wireless communications and signal processing.  Strong background in mathematics, Maltlab/C programming, control and telecommunication industry experience is also desirable.


Machine learning algorithms assisting millimetre wave communications for 5G and beyond

Supervisors

Dr Lei Zhang
Prof Muhammad Ali Imran

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

The next generation wireless communication (5G) and beyond will enable fully mobile and connected society, which is required to support a variety of very diverse user cases in terms of throughput, latency, reliability, availability and scalability. Three main communication scenarios have been identified by 3GPP with each having huge potential applications, i.e., enhanced mobile broadband (eMBB) (e.g., supporting mobile HD movie), massive machine type communications (mMTC) (e.g., supporting internet of things communications), and ultra reliable low latency communications (URLLC) (e.g., supporting vehicular to vehicular or virtual reality communications). Millimetre wave (mm-wave), at the frequency band 30–300 GHz, has been proposed as an enabler for many different communication scenarios. Although abundant unlicensed spectrum available in mm-wave band provides excellent possibility to fulfil the capacity requirements of the next generation wireless systems, the channel characteristics in these bands are significantly different from lower frequency channels. Exploiting large antenna array to combat the severe path loss in mm-wave communications is one viable option. For example, hundreds of antennas can be equipped within both access point and mobile station sides (i.e., massive MIMO system) to achieve very high beamforming gain by forming a very narrow beam pointing to the target. Moreover, the narrow beam width can be used for positioning in the scenario of vehicular and drone communications. However, it also causes severe problems for random access and user tracking. In this project, a wide omnidirectional beam pattern will be designed for both LoS and nLoS mm-Wave communications to alleviate the overhead and delay in the beam training. In addition, by taking the user moving trajectory and behaviour into the consideration, beam tracking based on machine learning algorithms will be proposed to fast track moving targets (e.g., car, drone and mobile phone, etc.) to provide high and consistent quality of experience communications. One of the challenge in such communication scenario is the communication and positing algorithm joint design by using the same signal.

For this project, the PhD candidate will have the chance to contribute to an exciting new research area, machine learning algorithms assisting millimetre wave communications for 5G and beyond, the work will include design, modelling and implementation of millimetre communication algorithms to support 5G communication scenarios and other unforeseen applications in the future digital society.

An ideal candidate should have experience in wireless communications and signal processing.  Strong background in mathematics, Maltlab/C programming, telecommunication industry experience is also desirable.


Radar and deep learning for enhanced gesture recognition

Supervisors

Dr Francesco Fioranelli
Prof Roderick Murray-Smith (Computing Science)

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

Radar works by transmitting electromagnetic waves and processing the echoes reflected back by targets of interest to extract information about their position (range information) and their velocity (Doppler information). In the context of gesture recognition, this information is very valuable, as it provides a way to distinguish between different movements by analysing the specific radar signature, and then performing automatic classification with methods based on machine learning. These look for patterns and numerical features in the radar signatures which are suitable to discriminate movements from one another. The combination of range and Doppler information has been investigated in recent years for a variety of applications, from classification of different activities performed by people, to the identification of specific individuals based on their walking gait, as well as for fall detection and monitoring of elderly people and people with disabilities in the context of ambient assisted living. This project builds upon previous work focused on studying the radar signatures of full-body movements (such as walking, sitting, standing, bending), and aims to distinguish and classify smaller movements performed using a single arm, a hand, or even fingers. These are used as way to interact with 'intelligent machines', such as home appliances, personal smart-phones or smart-watches, devices in public spaces (offices or shopping malls). The recent developments in neural networks based on deep learning algorithms and the proliferation of sensors in the Internet of Things context makes this research project relevant and interesting, as the interest of a big player like Google demonstrates with its Project Soli. One advantage of radar systems over camera-based systems is that no personal images are collected, so risks of privacy breaching are minimised and this can ease the users’ acceptance of this technology.

This project will investigate the many operational parameters to improve the overall performance of a gesture classification system (radar bandwidth, carrier frequency, polarisation, aspect angles, data fusion from multiple sensors, use of heterogeneous sensors), and explore innovative deep learning algorithms and neural networks architectures to maximise the classification performance (what types of features and classifiers are the most suitable for this scenario?).

The PhD candidate will perform a mix of experimental work (collection of a comprehensive database of experimental radar signatures of gestures with different sensors), and theoretical modelling work (data analysis and developing suitable algorithms for the best classification approach). The student will benefit from strong collaboration and support from colleagues at the School of Computing Science in the field of neural networks and deep learning methods.


Intelligent RF sensing for autonomous vehicles

Supervisors

Dr Francesco Fioranelli
Dr Julien Le Kernec
Prof Roderick Murray-Smith (Computer Science)

Funding

Currently unfunded. Please consult the Postgraduate Research section for information on applying for support. 

Description

This project will investigate innovative sensing and classification techniques for autonomous vehicles.

There has been a significant “buzz” in recent years about autonomous vehicles, be passenger cars, trucks, or drones, and the latest Industrial Strategy of the UK Government has indicated them as a priority. One enabling technology for their widespread use will be the capability of sensing the environment to gain awareness of the surrounding (weather, road type, detection of other vehicles and pedestrians), in order to then make decisions on speed, trajectory, and direction of the vehicle.

This project will primarily aim at using automotive radar systems operating in the mm-wave frequency range (60-77 GHz) to perform sensing of the surrounding environment and classification of targets of interest (other cars and vehicles, pedestrians, bicycles and motorcycles).

Challenges to tackle include developing specific radar signal processing and deep learning algorithms for automotive applications, as well as lean implementation of these algorithms for the fast reaction time required for vehicles. The project will see the student performing experimental work for some radar system development, as well as data collection/labelling, in addition to the more oriented data processing tasks mentioned above. An industrial partner will provide access to their radar prototypes and facilities to support this work, with an exciting opportunity for the student to work in an industrial context. Cross-disciplinary work with the School of Computing Science regarding the implementation of deep learning algorithms will also be strongly encouraged in this project.

Students interested in the project are invited to discuss any question they might have with the first supervisor, Dr Fioranelli (francesco.fioranelli@glasgow.ac.uk).