PhD Opportunities

Possible research topics to be undertaken in the Systems, Power & Energy Division of the James Watt 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. The deadline for application is 31 January 2020. See details on Scholarships on our 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 Sandy Cochran (Sandy.Cochran@glasgow.ac.uk), who will direct you towards a prospective supervisor with expertise in that area.

Themes

Medical and Industrial Ultrasonics

Space and Exploration Technology

Energy and Sustainability

Materials, Design and Manufacturing

Communications, Sensing and Imaging

Medical and Industrial Ultrasonics

Ultrasonic fluidisation of granular materials for industry and exploration

Supervisors

Dr Patrick Harkness

Funding

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

Description

Granular materials are among the most widely-traded substances, as they include many fuels, foods, and feedstocks. They also cover large parts of the Earth, both surface and subsurface, and parts of other asteroids, moons, and planets as well.

These materials are difficult to handle. They can behave as quasi-fluids, but more often they are almost uniquely challenging. If you have ever tried to press your finger directly into sand, without wiggling, you will discover that their stiffness rapidly increases. Similarly, they can stop the rotation of a drilling auger, and jam in the chutes of handling systems.

One solution may be ultrasonic vibration. Our initial studies have shown that sonicated tools can ‘fluidise’ granular materials, making them flow almost like a liquid. With viscosity reduced, penetrators and augers can operate more easily. We can push through the materials, and handle them as we wish, with lower forces and less power. We may even be able to pump them like liquids. This has the potential to facilitate both trade and planetary exploration, where landers might have to drill through regolith using low forces and torques in a low gravity environment.

This project will require the design of resonant ultrasonic tools, and the construction of autonomous mechatronic drilling rigs to carry out multiple penetrometry and augering tests in granular materials such as glass microspheres. Applications to use external, variable-gravity facilities are also anticipated.


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

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.


Space and Exploration Technology

Distributed Earth imaging

Supervisors

Dr Matteo Ceriotti

Dr Kevin Worrall

Funding

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

Description

Aerial imaging (using drones or satellites) is increasingly becoming essential: pictures from above are today used for mapping, security, disaster prevention and monitoring, weather forecast, traffic monitoring and routing, intelligence, environmental monitoring and planning, and many others. Spatial and temporal resolution are the two main metrics of an imaging system, and very often one is to be traded off for the other: aircraft and drone photography can often provide higher spatial resolution than satellites, but at the cost of a lower temporal resolution or limited coverage area and time window. Conversely, satellites can repass the same large area of interest frequently, and for many years, but due to their altitude, the spatial resolution is lower.

Depending on the purpose of imaging, one requirement might be more important than the other. The spatial resolution of an image is limited ultimately by the distance of the subject, the optics and the aperture of the lens used, due to diffraction. If the distance of the subject is not negotiable, ground-based systems overcome this limitation using larger-aperture optics (e.g. telescopes) as mass and volume are often not an issue. This is entirely different for Earth-imaging airborne and space-borne systems, where volume and mass are heavily constrained, and in fact are often to be minimised. This limitation has led to using large optics supported by large aircraft and spacecraft. However, disadvantages of this approach are high costs and low temporal resolution.

This research will investigate using a constellation of small satellites, effectively acting as a distributed imaging system. Instead of relying on a single device imaging the nadir, multiple devices (agents) in different locations can cover the same area at the same time. Computer-based image-processing techniques, including the use of neural networks and deep learning, will be used to fuse the source images and generate a super-resolved picture of the area of interest, of a better quality overall than each one of the sources. The ultimate aim of the project is to prove that this is a viable alternative to current distributed Earth imaging.

Background in computing science and/or space engineering is highly recommended. In order to be eligible to apply for the School of Engineering Scholarship, an excellent CV is required.


Optimisation of inter-satellite communications

Supervisors

Dr Matteo Ceriotti

Dr Kevin Worrall

Funding

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

Description

Intersatellite links (ISLs) are telecommunication routes between different satellites which allow a swarm or constellation of satellites (or agents) to effectively become a network of relay nodes. ISLs can be used to share data amongst different nodes of a network; one possible aim is to maximise the bandwidth between two specific agents in the network, or between an agent and an external entity (e.g. a ground station in the space scenario). With these links in place, satellites in large-enough constellations can communicate with relevant ground stations in quasi-real-time, regardless of whether the ground station is in line-of-sight and/or range. It is clear that the extent of the usefulness of ISLs depends on the effectiveness of the routing strategy employed. The main difficulty in utilizing ISLs is the fact that in most satellite constellations, the network topology is time-varying; links will constantly be found/lost as each satellite progresses along its own orbit, hence the effectiveness of the routing strategy becomes key to exploiting the availability of ISLs.

This PhD will investigate distributed algorithms for the autonomous optimisation of ISLs within a satellite constellation. Previous research [http://eprints.gla.ac.uk/159120] has looked into the use of Ant Colony Optimisation, a bio-inspired technique that mimics the behaviour of ants foraging for food; the PhD will expand this research and assess and compare the use of other optimisation methods. It will also investigate the effect of constraints introduced into the network (such as unavailability of one or more nodes) and develop techniques to cope with them optimally. One of the paramount aspects to consider is that the system should be able to self-optimise itself (fully-distributed) without the need of a central controlling node. In this way, the loss of one or more agents does not prevent the swarm to continue to find optimal solutions.

The techniques developed for the satellite scenario can readily be extended to other applications with different agents, such as autonomous vehicles, drones, sensors, etc.

Background in computing science, applied mathematics and/or space engineering is highly recommended. In order to be eligible to apply for the School of Engineering Scholarship, an excellent CV is required.

 


IN-ORBIT ASSEMBLY: ROBUST AUTONOMOUS METHODS FOR CONTROLLING ROBOT MANIPULATORS IN SPACE

Supervisors: Dr Kevin Worrall, Dr Gerardo Aragon Camarasa

 

Funding

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

 

Description

With the current push towards space for both private and government organizations, and the recent increase on initiatives to the industrialization of space, there will be an important need for humans to be supported by robotic systems. Understanding and mastering the unique properties that will intervene in the robot behaviour is essential to offer a fully autonomous robotic system which will be expected to work with no human intervention while being robust, accurate and responsive.

The work will consider the different advantages of both traditional and AI-based control methodologies to support the development of a vision-based control system that is able to control a robot manipulator within the space environment during in-orbit assembly tasks. The expected outcome of this work will be a simulation environment of a suitable setup and a practical real-life implementation.

This project will engage with recent research studies on the field on autonomous robotics, building in-orbit structures, satellite assembly and support studies on manufacturing in space. This project can also engage with users beyond space, with advanced manufacturing research being a potential area to explore.

Background in either control engineering mechatronics, computing science, and/or space engineering is highly recommended. In order to be eligible to apply for the School of Engineering Scholarship, an excellent CV is required.

 


Low energy capture of near Earth asteroids using multiple spacecraft

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 represent a useful resource of material to support future space ventures, from water to electrolyse into propellant and metals for in-orbit fabrication. We have recently investigated the use of stable manifolds for low energy capture in the Earth-Sun and Earth-Moon systems and are currently investigating mission design by integrating trajectory and economic optimisation.

This project will investigate a new strategy for low energy orbit transfer and capture of small near Earth asteroids. Rather than a single spacecraft used to rendezvous with and then retrieve a target object, multi-spacecraft spacecraft will be considered; for example a deep space ‘pitcher’ and a near-Earth ‘catcher’, both of which will be used to retrieve multiple target objects. Key research questions include:

  • What multi-spacecraft architectures can be devised to efficiently manoeuvre and capture a number of small near Earth asteroids, maximising the total mass of material returned?
  • What target objects are energetically the most suitable for the capture processes devised and how can groups of suitable target objects be identified?
  • How can such orbit manoeuvre and capture strategies be delivered through realistic mission concepts using current, or near future, propulsion technologies?

The project will combine astrodynamics, mathematical modelling and simulation to investigate these research questions, while also exploring other applications of near Earth asteroid resources. Candidates should have a strong interest in astrodynamics and simulation. The project will be embedded within a large group pursuing a programme of novel research on emerging space technologies.


Ultra-large space reflectors to enhance utility-scale terrestrial solar power

Supervisors

Prof Colin McInnes
Dr Gilles Bailet

Funding

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

Description

Our prior work has undertaken pilot studies on the use of ultra-large membrane space reflectors to illuminate utility-scale terrestrial solar power farms at night or in the evening when output is low but spot prices are high. We are also investigating in-orbit manufacturing processes which could enable the fabrication of such ultra-large membrane reflectors through the use of 3D printing technologies adapted for the space environment.

This project will undertake a system-level analysis of the use of ultra-large membrane reflectors to enhance the performance of terrestrial solar power farms, while investigating the top-level configuration and design of the reflector assemblies. Key research questions include:

  • What are the possible architectures for a constellation of solar reflectors and how could in-orbit fabrication reduce the cost of such a venture?
  • What configurations can be envisaged for the reflectors and how can they be designed to ensure membrane flatness while minimising mass and in-orbit manufacturing complexity?
  • How can the optical and mechanical properties of laboratory-scale reflectors be characterised and used to provide confidence in the performance of larger assemblies?

The project will combine modelling, simulation and laboratory-scale testing to investigate these research questions. Candidates should have an interest in modelling and simulation and an enthusiasm for laboratory experimentation. The project will be embedded within a large group pursuing a programme of novel research on emerging space technologies.


Biomorphic control for micro-spacecraft swarms

Supervisors

Prof Colin McInnes
Dr James Beeley
Dr Kevin Worrall

Funding

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

Description

Early work on biomorphic autonomous spacecraft considered the use analogue circuits to mimic simple spiking neural networks. It has been shown that such biomorphic systems can demonstrate quite complex emergent behaviour and can be robust to failures. While our work on 3x3 cm PCB-satellites currently uses conventional microcontrollers, the use of biomorphic control may enable even smaller, yet capable devices.

This project will firstly investigate the use of biomorphic control for ultra-small, centimetre-scale micro-spacecraft and then further develop our ideas to consider a large networked swam of devices. Key research questions include:

  • How can low-level behaviours be embedded in individual centimetre-scale micro-spacecraft; for example de-tumbling, Sun-pointing, target-pointing and orbit control?
  • How can interaction between the low-level biomorphic control of members of a large swarm of such devices lead to emergent, complex high-level behaviour?
  • What niche applications can be foreseen which leverage the benefits of biomorphic control while competing against the performance of conventional spacecraft swarms?

The project will combine modelling, simulation and laboratory-scale testing to investigate these research questions. Candidates should have an interest in modelling and simulation and an enthusiasm for laboratory experimentation. The project will be embedded within a large group pursuing a programme of novel research on emerging space technologies.

 


Design and optimisation of hybrid-propulsion space trajectories

Supervisors

Dr Matteo Ceriotti
Prof Colin McInnes

Funding

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

Description

The design of a space mission starts with the design of a spacecraft trajectory that allows for all the mission goals to be achieved. Traditionally, spacecraft have a propulsion system, which provides the impulse necessary for transfer and orbit control. The technologies mostly used today are high-thrust chemical rockets and low-thrust solar electric thrusters, however other intriguing concepts, such as solar and electric sailing, are being investigated and demonstrated. The types of space trajectories depend heavily on the propulsion technology used, and so do the design methodologies, to a certain extent. Hybrid propulsion is the use of two different propulsion technologies on the same spacecraft, and it can include, for example: low and high thrust, low thrust and sailing. At the increased cost of carrying two propulsion systems, the two of them can be used cooperatively in order to generate novel missions and trajectories that are unfeasible otherwise, or provide cheaper options for existing missions. The design of hybrid-propulsion trajectories will also require the use of new dynamical models and optimisation techniques in order to be able to fully explore their potential.

This project will investigate both the development of new numerical tools to design and optimise hybrid trajectories, and the consequent application to future space missions. Possible missions of interest will include debris mitigation, asteroid orbit manipulation, and more generally, interplanetary transfers, particularly in multi-body environment, where the benefit of different propulsion systems can be exploited most. The PhD will involve both analytical and computer-based (numerical) research, preferably in the MATLAB environment.

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.


Energy and Sustainability

Modelling of 5th generation district heating networks

Supervisors

Professor Zhibin Yu

Funding

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

Description

The UK became the first major economy in the world to pass laws to end its contribution to global warming by 2050. In Scotland, the government plans to reduce its greenhouse gas emissions to net-zero by 2045. It is expected that District Heating Networks (DHN’s) will be a big part for decarbonising heating sectors, using renewable heat sources such as ground/ air and water source heat pumps, geothermal power, solar thermal, and industrial heat recovery (including waste water treatment).

This project will focus on developing novel concepts of 5th generation district heating networks, which utilises multiple low carbon heating sources, such as heat from waste water, river water, data centre, off-peak electricity. The project will focus on developing numerical models and tools for system simulations and optimisation. Power-to-heat concept and thermal energy storage technologies will be introduced to utilise the off-peak wind power generation. The interactions between such heat network with the grid will be then investigated.


Study on the heat transfer associated with oscillating gas flows

Supervisors

Professor Zhibin Yu

Funding

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

Description

A group of heat engines (or refrigerators), including Stirling engines and coolers, thermoacoustic engines and coolers, pulse-tube coolers and son on, employ oscillatory flows of a gas (e.g., pressurised helium) to execute thermodynamic cycles to convert heat energy to power (or consumes power to produce refrigeration effects). However, the heat transfer associated with such high amplitude oscillatory flows are not fully understood. In the current practice, a so-called “Iguchi assumption” (i.e., the flow history does not influence the flow at the next instant) is used to simplify the fluid dynamics and heat transfer process, and thus those correlations of steady flow such as friction factors and Nusselt numbers are used for designing the such engines and coolers.

The “Iguchi assumption” becomes invalid when the velocity and frequency are higher according to our previous research, namely the flow history strongly affects the flow state at next instant; and that the Reynolds number is no longer the only parameter controlling the state of the flow. Therefore, the correlations and experimental data obtained using steady flow are invalid for designing devices that involve oscillatory flows with a high amplitude or frequency. However, very little research has been conducted on high frequency oscillatory flows, especially experimental data and correlations are rarely available in literature. This project will focus on the experimental research of heat transfer of oscillation gas flows using advanced techniques such as particle image velocimetry (PIV), filling the knowledge gap in this area.


Enhancing the thermal conductivity of phase change materials for developing cost-effective heat storage systems

Supervisors

Professor Zhibin Yu

Funding

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

Description

The UK has set an ambitious target to cut its greenhouse gas emissions, and the government’s strategy is to increase both the power and heat generation from renewables. However, these sources suffer from their intermittence, causing a time mismatch between supply and demand. Power-to-heat concept becomes an attractive low-cost solution, e.g., “wrong time” electricity is used to power heat pumps to produce and store heat for later use. As this stored heat will be directly used for space heating and domestic hot water, there is no need to convert it back to electricity, leading to a higher round-trip efficiency than alternative electricity storage technologies which suffer at least two conversion losses.

Cost-effective heat storage products are essential for facilitating the uptake of intermittent power and heat generation. Phase Change Materials (PCMs) are the most promising form of heat storage technologies. However, challenges remain that inhibit the development of cost-effective heat storage products for broad commercial uptake in the domestic heating equipment market. The poor heat transfer resulting from the extremely low thermal conductivity of PCMs leads to low power density, while the commonly used metal heat exchangers are heavy, complicated and expensive.

This project will address these challenges through developing efficient but inexpensive additive materials for enhancing the thermal conductivity of PCMs. It will involve numerical modelling and simulations using CFD software and experimental research using high speed camera and infrared camera.


Machine Learning in Improving Offshore Wind Turbine Operation and Maintenance (O&M)

Supervisors

Xiaolei LiuDr Shengrong Bu

Funding

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

Description

The mix of energy supply worldwide has been changed dramatically in the last few decades. In the UK, in order to tackle climate change and increasing energy consumption, there has been a clear movement from fossils towards renewable and sustainable energy sources. Wind energy, for example, accounting for 98% of Scottish electricity demand in October 2018, has established a world-class record.

Compared with onshore wind turbines, offshore wind could provide a relatively larger capacity and a lower level of noise pollution. Offshore wind energy conversion systems are more sophisticated and new methodologies are urgently required based on more advanced analytics. In light of recent developments in the wind energy sector, it is becoming extremely difficult to ignore the existence of data science, which will still be a fast growing field over the next 10 years. More specifically, it has been widely applied to wind speed/power forecasting & predictions, conversion systems optimization, and fault detection & diagnosis.

This project will first investigate the simulation of wind turbine operations based on advanced numerical methods (e.g. CFD, FEA and multi-body method, etc.). Based on the simulated load effects, responses in normal operation and extreme conditions will be solved by a coupled model of the wind turbine system, which is significant for operation and maintenance. Finally, high-frequency SCADA data will be collected for data science/mining based on principles with Python3 and Machine Learning established by various Intelligent frameworks.

If you are new to programming (mainly Python3) and have a passion to learn/practice it with real on-site data, you are welcome to apply. If you already have a basic knowledge of general computer programming (no matter Python, Fortran, Matlab, C or C++), you will get chances to strengthen your knowledge through applying the most state-of-the-art Artificial Intelligent algorithms (focusing on Machine Learning).

Applicants should have a strong academic background in mechanical engineering, civil engineering, electrical engineering, ocean engineering, naval architecture, mathematics or a related subject at a Master’s level, or holding a BEng/BSc degree, or equivalent.

Applicants should send their application directly to Dr Xiaolei Liu, Xiaolei.Liu@glasgow.ac.uk

Applications should include:

  • Cover Letter
  • CV

 


The food-water-bioenergy nexus for remote villages: Design, economics, and environmental impacts

Supervisors

Dr Siming You

Funding

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

Description

Sustainable food, water, and energy supplies for remote villages remain a great challenge in some developing countries. The development of self-sustaining food, water, and energy systems serves as one of the promising solutions and is receiving an increasing attention in recent years. This project will be based on typical remote villages in several developing countries and aims to develop bespoke concepts of feed-water-bioenergy nexus according to the environmental and resource background of villages. Biochemical (e.g., anaerobic digestion and aerobic digestion) and thermochemical bioenergy technologies (e.g., pyrolysis and gasification) will be included in the system design. The nexus will be optimised using multi-objective methods such as large-scale mixed-integer linear and nonlinear programming. The economic and environmental feasibility of the nexus will be evaluated using cost-benefit analysis and life cycle assessment.

 


Comparison of centralized and decentralized bioenergy systems for municipal solid waste treatment: Economic, environmental, and social impacts

Supervisors

Dr Siming You

Funding

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

Description

Sustainable management of municipal solid waste (MSW) has become one of the major challenges for megacities. Gasification, anaerobic digestion, and pyrolysis serves as environmentally friendly bioenergy technologies for MSW treatment. It could convert carbonaceous MSW into valuable products such as biogas, bio-oil, synthesis gas, biochar, etc. Biogas, bio-oil and synthesis gas could be further converted into electricity and heat, while biochar has been recognized as an effective carbon abatement tool upon its application in soil. Decentralized energy supply has been regarded as an important component of future smart grid systems. However, decentralized energy production could be economically challenging considering the economy of scale. This project will compare the economic, environmental, and social performance of centralized and decentralized bioenergy systems in both developed and developing countries. Different system and supply chain configurations will be proposed, and a decision support tool will be used to make the comprehensive comparison from the perspectives of different types of stakeholders (i.e. policy makers, investors, and consumers).

 


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

Supervisors

Dr Nader Karimi
Professor 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 are 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).


Oxy-combustion of biomass for negative production of CO2 through CCUS

Supervisors

Dr Nader Karimi
Dr Manosh C Paul
Dr Siming You

Funding

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

Description

Reduction of CO2 emissions as the main driver of global warming is a rapidly growing concern worldwide. A wealth of technologies for low-carbon power and heat generation has been already developed. Yet, that is still insufficient to control the rate of global warming and, particularly incapable of reducing the carbon dioxide content of the atmosphere.

Plants naturally absorb CO2 from the atmosphere and store that in different biological materials, such as wood, leaves and grass, collectively known as biomass. Combustion of biomass has been a source of energy for thousands of years. It is regarded as a renewable and low-carbon method of energy production as it simply recycles the CO2 already in atmosphere.  However, if the CO2 produced by combustion of biomass is captured through CCUS (carbon capture, use and storage) technologies, it results in removal of CO2 from the atmosphere. To enable practical implementation of such process, combustion should happen in an O2/CO2 atmosphere rather than air to eliminate the need for nitrogen separation from the flue gases. This calls for further fundamental research on the combustion of biomass in gases different to air. It also requires techno-economic and environmental studies to evaluate the sustainability of the technology at large scale. The PhD project aims at tackling both of these aspects through conduction of a series of numerical and experimental investigations on oxy biomass combustion together with an environmental analysis.

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).

 


Investigation of two-phase convection of heat in microchannels for electronics cooling

Supervisors

Dr Nader Karimi
Dr Manosh C Paul
Prof Gioia Falcone

Funding

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

Description

With the invention of the new generations of high-performance electronics, the challenge of cooling the electronic devices has become most substantial. The conventional air and water cooling methods are clearly insufficient and consequently heat transfer barriers are now hindering the development of more powerful and compact computers.

Boiling is an efficient method of heat transfer capable of offering very high convection coefficients and is therefore ideal for the applications with large heat loads over modest temperature differences, such as those encountered in electronic cooling. Further, microchannels are well-known for their superior heat transfer characteristics as well as their favourable space efficiency. Boiling in microchannels is therefore an attractive method of transferring large amounts of heat from a small surface area. The basic merits of this technology have been already demonstrated. Nonetheless, a number of important fundamental questions remain undressed, as such the dynamics of bubble growth and coalescence and the subsequent effects upon the heat transfer rate and pressure drops are yet to be fully understood.  Further, the possibility of using complex fluids, and particularly nanofluids, as the heat transfer agent is largely unknown and thus requires far more investigations. This PhD project aims at answering some of these questions through taking a combined numerical and experimental approach. This includes design and building a multi-phase, micro-cooling device and conducting experimental and numerical flow visualisation with the purpose of developing physical understanding. Depending upon the capability and interest of the selected candidate, either of the experimental or numerical aspects of the work can be more emphasised.

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).

 


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

Supervisors

Professor 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

Professor 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.


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.


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

Symmetric cryptographic key generation algorithms for secure connected autonomous vehicles

Supervisors

Dr Petros Karadimas

Description

Secure data exchange between communicating vehicles is one of the greatest technical challenges pending to be addressed prior to mass production of fully autonomous vehicles. The security solution has to be energy-efficient and adaptable to any wireless propagation environment in which vehicle-to-vehicle (V-V) communications take place. A symmetric cryptographic key generation solution will be considered by exploiting the physical layer characteristics of the wireless propagation environment. In the international literature, it has been named as physical layer security solution and proven to be an ideal candidate for secure communications with strict constrains on computational resources and power consumption. Starting from a very thorough literature review, the student will have to understand and become familiar with the most recent advances in physical layer security and how this can be implemented in V-V communications. Accordingly, the student will have to understand the algorithmic solutions and steps involved in the key generation process, including V-V channel modeling and simulation, received signal quantization, information reconciliation and privacy amplification. The final goal is to design a symmetric cryptographic key generation algorithm and evaluate its performance according to certain key performance indicators such as key generation rate and key entropy.

This project is suitable for applicants with interdisciplinary interests in wireless communications, security/cryptography and programming/algorithms. Indicatively, applicants should have good performance in the following subjects: Communication Principles/Theory, Digital Communications, Security/Cybersecurity and Cryptography, Programming/Algorithms and Software Engineering.

For further details, it is strongly recommended to contact with the principal investigator and supervisor of this research, Dr. Petros Karadimas: Petros.Karadimas@glasgow.ac.uk.

Funding

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


Hybrid OFDM transceiver design and implementation for connected autonomous vehicles

Supervisors

Dr Petros Karadimas

Description

Orthogonal Frequency Division Multiplexing (OFDM) is a signalling technique that exploits orthogonal carriers to transmit information and enhance received signal diversity and consequently increase received signal-to-noise ratio (SNR). In wireless mobile communications, carrier orthogonality is violated due to the inherent Doppler spread arisen by the temporal variability of the wireless channel. This effect causes degradation of received signal quality and becomes more evident in environments with very high mobility such those involving communications between vehicles, i.e., vehicle-to-vehicle (V-V) communications. However, the increased Doppler spread in V-V communications provides an alternative signal diversity mechanism by using spread spectrum signalling. It is characterized as Doppler diversity and it can be a significant mechanism to enhance performance in V-V communications. Starting from a very thorough literature review, the OFDM technique will be theoretically/mathematically studied and analyzed first to understand the important parameters affecting its performance. Focus will be on V-V communication channels and the student should come up with a solution compensating the increased Doppler spread in such channels. A further direction of the project will be to investigate the implementation of a novel OFDM architecture by inserting an extra add-on block that is capable to exploit the inherent Doppler diversity of V-V communications. Thus, a hybrid "two-dimensional" OFDM architecture will arise with dual degrees of freedom, i.e., those due to orthogonal carriers and those due to Doppler diversity, offering potentials for improved performance compared to the standard OFDM architecture. Both the hybrid and the standard OFDM architectures should be then implemented/simulated in a appropriate software tool (e.g., Matlab), where the student will demonstrate parameterized case studies, showing how the performance is affected in diverse V-V communication scenarios by varying relevant parameters of the V-V communication channel (e.g., power angular spread, vehicles' velocities, etc). A comparative study of both architectures will demonstrate the performance improvement (if any) of the hybrid against the standard OFDM architecture.

This project is suitable for applicants with interests and good background in wireless communication systems and particularly in the physical layer of wireless communications. Indicatively, applicants should have good performance in the following subjects: Communication Principles/Theory, Digital Communications, Digital Signal Processing, Statistics and Stochastic Processes, Engineering Mathematics.

For further details, it is strongly recommended to contact with the principal investigator and supervisor of this research, Dr. Petros Karadimas: Petros.Karadimas@glasgow.ac.uk.

Funding

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


Design and performance evaluation of multi-element antenna systems for connected autonomous vehicles

Supervisors

Dr Petros Karadimas

Description

Antennas are the corner stone of wireless communications as they are responsible for transmitting and receiving the electromagnetic wave that carries the information message. Although a very classical topic with more than 100 years of history since the first wireless transmission, the design of efficient antennas remains a timely issue. Particularly, for vehicular communications, a compact design should be integrated in the vehicles' on-board unit (OBU) and operate according to optimized performance criteria, such as maximum data rate and minimum power consumption. Starting from a very thorough literature review, the student will have to understand very well the radiation mechanisms of antennas and become familiar with the Maxwellian basis of the electromagnetic solutions for the radiation pattern, antenna gain, efficiency, polarization, etc. Accordingly, the student will advance his/her knowledge to existing multi-element antennas for vehicles' OBUs and show some understanding of their key performance indicator including the diversity antenna gain and channel capacity. The goal is to design such compact multi-element antennas for the OBUs of future vehicles using software tools (e.g., CST, HFSS) and fabricate them. After the design stage, a performance analysis of the antennas will take place considering relevant key performance indicators such as the diversity antenna gain and channel capacity. The aforementioned step of studying, designing and fabricating existing state-of-the art multi-element antennas will enable the student to gain significant experience on this field necessary to progress to the next level. The next level and ultimate goal of this project is the student to come up with novel brand new multi-element antenna designs (at least three) that will show better communication performance (e.g., diversity antenna gain and channel capacity) against existing ones.

This project is suitable for applicants with interests and good background in electromagnetics and electromagnetics design and particularly in electromagnetic wave propagation, antennas and antenna arrays for communication systems. Indicatively, applicants should have good performance in the following subjects: Electromagnetic Theory and Fields, Microwave and mm-Wave Transmission Systems and Devices, Communication Principles/Theory, Engineering Mathematics.

For further details, it is strongly recommended to contact with the principal investigator and supervisor of this research, Dr. Petros Karadimas: Petros.Karadimas@glasgow.ac.uk.

Funding

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


Design and performance evaluation of multi-element antenna systems for mobile handheld devices

Supervisors

Dr Petros Karadimas

Description

Antennas are the corner stone of wireless communications as they are responsible for transmitting and receiving the electromagnetic wave that carries the information message. Although a very classical topic with more than 100 years of history since the first wireless transmission, the design of efficient antennas remains a timely issue. Particularly, for future mobile handheld devices, such as mobile phones and tablets, a compact design should be integrated and operate according to optimized performance criteria, such as maximum data rate and minimum power consumption. Starting from a very thorough literature review, the student will have to understand very well the radiation mechanisms of antennas and become familiar with the Maxwellian basis of the electromagnetic solutions for the radiation pattern, antenna gain, efficiency, polarization, etc. Accordingly, the student will advance his/her knowledge to existing antennas comprised of many elements, the so-called multi-element antennas and show deep understanding of the key performance indicators of such antennas including the diversity antenna gain and channel capacity. The goal is to design as many as such compact multi-element antennas using software tools (e.g., CST, HFSS) and then fabricate some of them. After the design stage, a performance analysis of the antennas will take place considering relevant key performance indicators such as the diversity antenna gain and channel capacity. The aforementioned step of studying, designing and fabricating existing state-of-the art multi-element antennas will enable the student to gain significant experience on this field necessary to progress to the next level. The next level and ultimate goal of this project is the student to come up with novel brand new multi-element antenna designs (at least three) that will show better communication performance (e.g., diversity antenna gain and channel capacity) against existing ones.

This project is suitable for applicants with interests and good background in electromagnetics and electromagnetics design and particularly in electromagnetic wave propagation, antennas and antenna arrays for communication systems. Indicatively, applicants should have good performance in the following subjects: Electromagnetic Theory and Fields, Microwave and mm-Wave Transmission Systems and Devices, Communication Principles/Theory, Engineering Mathematics.

For further details, it is strongly recommended to contact with the principal investigator and supervisor of this research, Dr. Petros Karadimas: Petros.Karadimas@glasgow.ac.uk.

Funding

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


5G+ Enabled Blockchain Systems

Supervisors

Dr Lei Zhang
Prof Muhammad Ali Imran

Funding

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

Description

Blockchain, as the backbone technology of Bitcoin digital currency, has become a revolutionary decentralized data management framework. It is history's first permanent, decentralized, global, trustless ledger of records that may reshape the future digital economy and transforming society. Blockchain technology can solve the long-lasting security and overhead issues in the IoT systems, as a result, enabling decentralized, automatic, massive connected mobile IoT ecosystems with the wide applications from smart contract, supply chain, healthcare, digital identity, to digital voting, etc.   

In this project, the classic consensus mechanisms will be extended into the wireless communication scenarios, to serve as a theoretical foundation for blockchain-enabled IoT ecosystems. In addition, the unique requirements in blockchain protocol will motivate a new 5G+ communication protocol dedicated for the scenarios to maximizing the communication spectrum efficiency and minimizing the cost and power consumption at the low cost IoT devices.

The project is cross-disciplinary that involves communication engineering, IoT, computer science, etc. The methodology proposed in this work will bridge the gap between the blockchain and IoT systems through wireless communications. It can catalyse the future research in all aforementioned related topics.

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

 


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.


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.


Fully-Funded PhD Studentship for Engineering Optimization

Fully-Funded PhD Studentship for Engineering Optimization  

Candidates with AI, Computer Science or Applied Mathematics backgrounds are especially welcomed

Target qualification:

PhD

Location:

Glasgow, Scotland, United Kingdom

Funding for:          

UK students, EU students, or International students

Funding amount:

Full tuition fee waiver for four years plus an annual stipend of £15,285 (tax free)  

Mode of study:

Full Time

Anticipated starting time:

1st October 2020

Application deadline: 31st July 2020 (Please apply as early as possible. Often the post is filled in the first two months.)

Principal Supervisor: Dr. Bo Liu

 

Applications are invited for a four-year fully funded PhD research studentship in engineering design optimization in the Communications, Sensing and Imaging (CSI) Group, James Watt School of Engineering, University of Glasgow, Scotland, UK. The PhD student will focus on research into evolutionary computation, conventional optimization, and machine learning techniques with applications to communication systems. The project is in collaboration with a multi-national telecommunication industry partner and the research outcomes are expected to be applied in the future 5G and beyond solution and hence will have very wide international visibility. As a parallel outcome, corresponding computer-aided design (CAD) programs will be developed which will have the potential to be embedded into well-known CAD tools, such as MATLAB, FEKO, EMPro (Momentum), etc. thus obtaining global visibility and the potential for long-term funding generation. Involvement with this work is expected to generate an impressive track record for the PhD student’s future career, either from industry or academic point of view. The studentship is expected to be available from 1st October 2020 (negotiable).

Applicants should hold at least a first or upper-second class honors degree (or the equivalent, e.g. GPA greater than 3.3) in computer science, mathematics, or engineering. Competence in conventional optimization, evolutionary optimization, and mathematics (at least two of these) is essential and evidence of extensive experience in programming is required. Knowledge of machine learning is highly desirable, as is experience with MATLAB. Knowledge of microwave devices and communication systems is not required since the student will collaborate with research students in these domains, but having such knowledge is still very desirable.

 

Inquiries to: Dr. Bo Liu

James Watt School of Engineering, 

University of Glasgow,

Scotland
Telephone: +44 (0) 141 330 2000
E-mail: Bo.Liu@glasgow.ac.uk

https://www.gla.ac.uk/schools/engineering/staff/boliu/

Informal enquires are very welcome and should be made to Dr. Bo Liu via email. Please submit a full CV together with a covering letter describing how you meet the requirements.  

 


AI Techniques for Automated Design of Microwave Antennas

Fully-Funded PhD Studentship in AI Techniques for Automated Design of Microwave Antennas

Candidate students with AI, computer science or applied mathematics backgrounds are especially welcomed

Target qualification:

PhD

Location:

Glasgow, Scotland, United Kingdom

Funding for:          

UK students, EU students, or International students

Funding amount:

Full tuition fee waiver for four years plus an annual stipend of £15,285 (tax free, funded by MathWorks)  

Mode of study:

Full Time

Anticipated starting time:

1st July 2020

Application deadline: 31st May, 2020

Principal Supervisor: Dr. Bo Liu

 

Applications are invited for a four-year fully funded PhD research studentship in AI techniques for automated design of microwave antennas in the Communications, Sensing and Imaging (CSI) Group, James Watt School of Engineering, University of Glasgow, Scotland, UK. The program is funded by one of the leading CAD companies, The MathWorks Inc. (the creator of MATLAB and SIMULINK). The PhD student will focus on research into surrogate model-assisted optimization techniques and corresponding CAD tool development: the final product will be available as part of the global MATLAB distribution and hence will have very wide international visibility. The studentship is expected to be available from 1st July 2020 (negotiable).

Machine learning and heuristic optimization-driven automated antenna design methodology is one of the strengths of the CSI group. Its SADEA algorithm series is widely used by many academic and industry users. For the design of complex antennas, the SADEA algorithm series is often the only viable solution. Given that SADEA-I is available in MATLAB 2020a, this project focuses on investigating next generation AI techniques for microwave antenna design. The source code will be provided to MathWorks and embedded in future MATLAB versions. Clearly, this will be a sound track record for the PhD student’s future career, either from industry or academic point of view.  

Applicants should hold at least a first or upper-second class honors degree (or the equivalent, e.g. GPA greater than 3.3) in computer science, mathematics or engineering. Competence in mathematics and machine learning (at least one of these) is essential and evidence of extensive experience in programming is required. Experience with MATLAB is highly desirable. Knowledge of electromagnetic simulation and antennas is not required since the student will collaborate with antenna design researchers, but having such knowledge is highly desirable.

 

Inquiries to: Dr. Bo Liu

James Watt School of Engineering, 

University of Glasgow,

Scotland
Telephone: +44 (0) 141 330 2000
E-mail: Bo.Liu@glasgow.ac.uk

https://www.gla.ac.uk/schools/engineering/staff/boliu/

Informal enquires are very welcome and should be made to Dr. Bo Liu via email. Please submit a full CV together with a covering letter describing how you meet the requirements.