James Watt School of Engineering

Dr Kiran Ramesh

Description

In the last 2 decades, the field of fluids engineering has seen an explosion of research interest in new ideas and concepts, such as: wind/tidal energy conversion devices, unmanned air vehicles (UAVs), high-altitude long endurance (HALE) aircraft, electric vertical take-off and landing (eVTOL) vehicles, and blended-wing-body (BWB) aircraft using distributed electric propulsion. All these research themes are aligned with the overarching 21st century goal of generating clean energy and reducing emissions of pollutants and greenhouse gases due to transport. Hence "optimal designs" are desired, but suitable design methodologies for these problems aren't yet available. This is primarily because all of these problems in some manner involve unsteady and nonlinear fluid flow, for which analytical solutions which are usually the basis of design methodologies aren't available.

In the project, we propose to augment physics-based equations and models with machine-learnable structures in the field of unsteady, nonlinear fluid dynamics. This "scientifically-based" learning approach will enable discovery of previously unknown governing terms/equations, accurate extrapolation beyond the training data, and acceleration of model simulation.

Using the recently-discovered topology and dynamics of dynamic-stall-type unsteady flows[1], we propose to split the full, complex topology into constituent unsteady flows which may be represented by Ordinary Differential Equations. The ODE system will be augmented with neural-network structures that account for nonlinear interactions absent from the physics-model. The resulting system will be trained with high-fidelity computational fluid dynamics (CFD) data generated using the open-source software package OpenFOAM. We aim to uncover new terms in the system of equations describing the flow that are absent in the scientific model based on ideal flow conditions. We expect that the trained solver will be able to make real-time, accurate predictions of flows that are loosely based on the dynamic-stall-topology (containing boundary-layer separation and leading-edge vortex formation), even when the conditions are far from ideal.

Pre-requisites: Interest (and preferably experience) in theoretical and numerical methods in fluid dynamics, interest in AI and machine learning .

[1] Widmann, A., and Tropea, C. "Parameters influencing vortex growth and detachment on unsteady aerodynamic profiles." Journal of Fluid Mechanics 773 (2015): 432-459.

Dr Kevin Worrall and Dr Gerardo Aragon Camarasa

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.

Funding

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

Dr Angela Busse

Description

Many empirical relationships used in the context of wall-bounded turbulent flows have been obtained under the assumption that the wall is flat, e.g. by approximating the geometry as a flat plate or channel flow. However, turbulent boundary layers that develop over surfaces that are strongly curved can exhibit a different behaviour. An example is the case of the turbulent boundary that develops along the axial direction of a circular cylinder with a high length to diameter ratio. This type of configuration occurs for example in the context of towed array sonars which are used to conduct geological surveys of the seabed.

Unlike the flat-plate turbulent boundary layer, which has been studied widely, there is currently only limited experimental and numerical data available on axisymmetric boundary layers. In this project, direct numerical simulations (DNS) will be used to investigate axisymmetric turbulent boundary layers. In the first phase, the smooth wall case will be investigated. In the second phase, effect of surfaces roughness, e.g. due to marine biofouling, will be included.

Dr Kevin Worrall

Description

The capabilities of UAVs and μUAVs are expanding each year. There are autonomous UAVs that can fly in open and complex environments and UAV’s that can delivery supplies and operate as remote sensors.

One reason for this is the increase in processing power, and the size reduction, of microelectronic devices and sensors. With the new capabilities of the microelectronic devices available there is now a drive to implement onboard vision based navigation and obstacle avoidance for UAVs and μUAVs. This method of navigation/obstacle avoidance could further improve the capabilities of the systems and provide a means to increase the range of applications that can be carried out by the UAVs.

This project is to consider the use of an onboard vision system for obstacle avoidance. The aim of which is to allow a UAV to fly freely around a complex unpredictable environment. It is proposed that to achieve this the system is required to learn what obstacles are and then react to them.

Using machine learning, it is envisioned that a suitable algorithm can learn what obstacles are and, using this knowledge, provide a means in which to successfully implement an obstacle avoidance routine on board the uUAV.

This work will involve:

• Implementation and testing of machine learning algorithms
• Hardware implementation and testing of algorithms
• Comparison of algorithms for different scenarios

The ideal candidate will have a strong background in software and control with experience of hardware and an excellent track record, preferably including evidence of outstanding research, such as previous awards and/or publications.

Dr Douglas Thomson and Euan McGookin

Description

Wheeled vehicles are used for many different transportation applications e.g. road transportation, off-road travel, mobility.  Each scenario has its own requirements in terms of performance and the specification of the vehicle.  However, all have safety as a main concern and requirement for the operation of these types of vehicle.  In particular road safety has become a focus for government bodies and the automotive industry in an attempt to reduce fatalities. One way of improving safety is through in-vehicle assistance where the driver is provided with assistive cues that direct their driving in a safe manner.  In addition, the operation of the vehicle on rough terrain can be enhanced through assistive control systems that compensate for variations in the ground profile. To implement such assistive technologies there needs to be knowledge of how desired driving trajectories can manifest themselves as inputs to the vehicle.  A novel technique that can generate such a relationship from the dynamics of the vehicle is Inverse Simulation. 

Inverse Simulation is a method by which the control inputs necessary for a given system to respond in a defined manner can be calculated.  The most common area where this method is applied is in the aerospace field where the pilot control inputs necessary for an aircraft to fly a specific manoeuvre can be calculated based on an inverse structuring of an appropriate mathematical model.  Recent work at the University of Glasgow has demonstrated that the mathematical model can be treated as a module of the Inverse Simulation algorithm allowing its use for various dynamic systems.  Inverse Simulation works by taking such a standard mathematical model and solving it in a conventional form over a discrete time step.  It is an iterative process where step changes in the various controls are applied until the predicted response matches the predefined response.   Its value can be as a predictive tool for design or optimisation, or it can be inherent in the basic control system design. 

For wheeled vehicle safety, Inverse Simulation could be used to define an optimum driving corridor that would provide safe and/or efficient utilisation of the vehicle.  This corridor would be a spatial/temporal path for the driver to follow.  The underlying Inverse Simulation would be based on a mathematical model of the vehicle and suspension system dynamics for an estimate of the current driving conditions ahead.  The driver would be instructed on the best way to improve their own driving skills and maximise the performance of the vehicle. 

The aim of this project will be to develop an appropriate mathematical model of wheeled vehicles for different applications (e.g. road driving, off-road and mobility) and apply the techniques of Inverse Simulation to them. The Inverse Simulation will then be used in a number of case studies to demonstrate the capabilities of this method in the design and analysis of systems for wheeled vehicles.  These applications will include driver assistance and automatic control of the vehicles being considered.

Dr Shuja Ansari

Description:

The James Watt School of Engineering of the University of Glasgow is seeking a highly motivated graduate to undertake an exciting 3-year PhD project entitled ‘Modelling of Electronic Attack in a complex EME’ as part of the £7m DTSL funded Electromagnetic Environment Hub bringing leading groups and industrial partners working towards next generation and generation after next science and technology research and capacity building.

Electronic attack (EA) is a crucial aspect of modern warfare, involving the use of electronic systems to disrupt, disable, or degrade an adversary's electronic systems. However, the complexity of the electromagnetic environment (EME) in which EA takes place poses significant challenges for effective planning and execution of electronic attack operations. The aim of this PhD project is to develop a comprehensive model of EA operations considering systems of systems and spanning over a wide spectrum used in complex EME. The main objectives of this PhD project include:

·      To identify and understand the key factors that influence the effectiveness of electronic attack in a complex EME, including environmental factors, adversary capabilities, and system characteristics.

·      To develop a comprehensive model of electronic attack that integrates these key factors, using a combination of mathematical models, simulations, and data analysis.

·      To validate the accuracy of the model by comparing its predictions to real-world data and experimentation results.

·      To use the model to simulate different scenarios and assess the effectiveness of different EA techniques and tactics in a complex EME.

The studentship is supported by DSTL, and it will cover home tuition fees and provide a stipend at the UKRI rate (£17,688 per annum in session 2022/23) for 3 years. Interested students should email Dr Shuja Ansari at the first instance.

Dr Shuja Ansari

Description:

The James Watt School of Engineering of the University of Glasgow is seeking a highly motivated graduate to undertake an exciting 3-year PhD project entitled ‘Battlefield Effect Assessment Using Digital Twins’ as part of the £7m DTSL funded Electromagnetic Environment Hub bringing leading groups and industrial partners working towards next generation and generation after next science and technology research and capacity building.

The complexity and unpredictability of modern warfare make it challenging to assess the impact of battlefield effects accurately. Traditional methods rely heavily on human expertise and physical experimentation, which can be costly and time-consuming. However, advances in digital twin technology offer a promising solution to this problem. Digital twin technology involves creating a virtual replica of a physical system, which can be used to simulate different scenarios and predict their outcomes. The main objectives of this PhD project include:

·      To identify and understand the key factors that influence the effectiveness of different weapons and tactics on the battlefield and integrate them into the digital twin model.

·      To develop a digital twin model of a representative battlefield environment, including terrain, structures, and key assets.

·      To validate the accuracy of the digital twin model by comparing its predictions to real-world data and experimentation results.

·      To develop guidelines and best practices for using digital twin technology for battlefield effect assessment.

The studentship is supported by DSTL, and it will cover home tuition fees and provide a stipend at the UKRI rate (£17,688 per annum in session 2022/23) for 3 years. Interested students should email Dr Shuja Ansari at the first instance.

Dr Wenjuan Song and Dr Mohammad Yazdani-Asrami

Description

Renewable energy systems, such as wind turbines and solar panels, are becoming increasingly popular as we transition towards a more sustainable future. However, these systems face challenges related to their integration with the electrical grid. One of the main challenges is ensuring the safety and reliability of the electrical system. This project aims to address this challenge by developing superconducting fuses for renewable energy systems.

Superconducting fuses have the potential to provide faster and more reliable protection against overcurrent faults compared to conventional fuses. This is because they operate based on the rapid transition of a superconducting state to a normal state. The use of superconducting fuses can therefore help to prevent damage to electrical components and reduce downtime.

The successful candidate will work on the development of superconducting fuses for renewable energy systems, including the design, simulation, fabrication, and testing of prototype devices. The project will involve collaboration with industry partners and other academic researchers. The candidate will also have the opportunity to develop their skills in superconducting materials, electrical engineering, and renewable energy systems.

Dr Wenjuan Song

Description

We are seeking a highly motivated PhD candidate to join our research team working on the development of superconducting flux pumps.

The project will focus on the design, simulation, and experimental characterization of superconducting flux pumps, which are critical components in a wide range of superconducting systems and applications, including high field magnets for fusion energy and machines. Superconducting flux pumps are used to generate high magnetic fields in superconducting magnets by pumping magnetic flux into the system without the need for an external power source. This enables more compact and efficient superconducting systems, which have important applications in fusion energy technologies.

The PhD candidate will work closely with other members of the research team to develop new approaches for designing, simulating, and testing superconducting flux pumps. The project will involve a combination of numerical simulations, and experimental measurements, and will require the candidate to have strong problem-solving skills.

Dr Wrik Mallik

Description

The unsteady flow and fluid-structure interaction due to the wing flapping of birds and insects and those around dynamically oscillating aerofoils have attracted significant interest over the last two decades. For example, the thrust generated from complex interaction of leading-edge vortices and flapping kinematics of small flyers inspires the development of efficient micro-air vehicles. Similarly, control of the complex dynamic stall phenomena generated due to pitching or heaving aerofoils can lead to better designed wind turbine and propeller blades. The flow physics observed in these phenomena are highly complex and challenging to solve. Thus, a better understanding of such phenomena over a wide range of flight and structural parameters is required before we can attempt to develop robust design and control strategies. The present challenge to this is the huge computational time and complicated experiments required to analyse such fluid-structure interaction phenomena. Thus, we need generalised techniques which can provide quick prediction over a wide range of operating conditions.

Data-driven techniques have recently become highly popular to learn and predict complex physical phenomena. Being data-driven, such models are agnostic to how the data is obtained and can provide generalised predictions. Various data-driven techniques have been employed in the recent past, but machine learning-based models developed via deep neural network architectures have proven to be highly successful of late. Popular deep neural network architectures like convolutional neural networks and long short-term memory networks have been employed in the last three-five years to efficiently learn complex flow around vortex-induced vibrations and far-field propagation of acoustic waves. These have demonstrated the huge potential of deep learning networks. However, at the same time we have observed various challenges associated with such networks in learning multi-scale flows and flows around rapidly deforming boundaries. Interestingly, multi-scale flow and rapid deformation/movement of the solid boundary are expected during the flow separation and vortex shedding due to dynamic stall and flapping flight. Thus, we need to develop new deep learning-based models to develop better low-dimensional and scalable modelling techniques for dynamic stall around aerofoils and flapping flight of small flyers. The research will explore the state-of-the-art neural network architectures and geometric nature of the flow phenomena to devise new strategies of generalised deep learning-based model development.

Prerequisite skills: undergraduate courses in fluid mechanics and numerical methods, programming skills in Matlab or Python. Familiarity with computational fluid dynamics and machine learning is a plus.

To discuss about potential research activities and projects contact me directly at: Wrik.Mallik@glasgow.ac.uk

Dr Wrik Mallik

Description

Morphing and shape optimisation have recently attracted significant research interest. The ability to optimise aerofoil shapes to reduce the drag due to shock waves at high-speed flow has been recently demonstrated. Similar demonstrations of reduction in skin friction at low speed attached flow via shape optimisation is also recently published. However, we also need to demonstrate if we can reduce flow separation and stall at high angles of attack via shape optimisation. Similarly, shape morphing can enable us to completely change slender body configuration to enhance its thrust, stability and drag. This enables us to design bio-inspired shapes observed in various flying organisms, which are often immensely efficient and well-evolved. Such aerofoil shape morphing, and complex aerofoil shape optimisation require general shape representing methods, which can represent even complex shape and topology changes. Furthermore, we need to be able to define such shapes with the help of a few parameters so that we can include these into online shape control and optimisation algorithms seamlessly. This motivates the exploration of level set methods for this research.

Level set methods are a well-known technique of representing even complex shapes and topologies implicitly, on a fixed and uniform Cartesian grid. Over the last decade various radial basis function-based parameterisation of level set methods have been developed, which have immensely popularised their usage for topology optimisation.  However, the very large number of design variables associated with the conventional use of such parametric level sets have made them less popular and computationally expensive than other popular techniques for shape optimisation. This research will explore a new approach of employing parametric level set methods for shape optimisation and morphing of aerofoils via polynomial perturbation. The project will involve shape optimisation and morphing via the parametric level sets and comparison with other state-of-the-art shape optimisation techniques.

Prerequisite skills: programming skills in Matlab or Python and undergraduate coursework in fluid and/or structural mechanics required. Interest in computer graphics and engineering optimisation are a plus but not essential.

To discuss about potential research activities and projects contact me directly at: Wrik.Mallik@glasgow.ac.uk

Dr Wrik Mallik

Description

Application Deadline: N/A

Funding Available: No. Contact supervisor for scholarship availability

Project description

Future innovative configurations for commercial aviation and urban air-mobility would likely be developed with flexible aircraft configurations like the Truss-braced Wing (TBW) or NASA Helios for maintenance and manoeuvrability. Such flexible flying configurations will be highly flexible and undergo complex fluid-structure interaction between the flexible, deformable structure and flow around it. The goal of this research project is to investigate how such flexible structures deform under various flying conditions and if we can control the deformed shape of such structures by adaptive morphing. Adaptive morphing ability would enable us to perform the required flight missions even with the flexible structures. However, they would require a deep physical understanding of the complex aerodynamic and aeroelastic behaviour of these flexible configurations and the flight loads generated under various flying conditions. Novel shape optimisation and morphing technologies can then be developed, which will employ the physical understanding of the unsteady aerodynamics and aeroelasticity of flexible configurations for the development of highly efficient innovative aircraft configurations. The long-term project can be separated into two major PhD sub-projects:

- Computational aeroelastic analysis via high-fidelity numerical solvers for physical understanding and accurate load prediction: high fidelity computational fluid dynamic simulations will be employed for investigating the complex physics of unsteady flow separation and dynamic stall leading to aeroelastic instabilities like flutter.

- Development of novel shape optimisation and morphing methodology for flexible aircraft structures: Novel shape optimisation via adjoint methods and parametric level set functions will be developed to explore the design space of complex aircraft configurations like the TBW. Optimal strategies will be developed to improve aerodynamic and aeroelastic performance of flexible wings via wing morphing.

The long-term goal of the research is to provide scientific data and engineering guidance to policy makers and manufactures for development of greener and sustainable aircraft configurations.

Prerequisite skills: programming skills in Matlab or Python and undergraduate coursework in fluid mechanics and structural mechanics required. Interest or experience in computational fluid dynamics and engineering optimisation are a plus but not essential.

The project title is indicative of the research activities. Please contact supervisor directly to discuss available topics in the area.

How to Apply: Please refer to the following website for details on how to apply: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/

Supervisor(s): Dr Wrik Mallik, Dr Mohammad Yazdani-Asrami

Application Deadline: N/A

Funding Available: No. Contact supervisor for scholarship availability

Project description

Truss-braced wing (TBW) configurations are next-generation aircraft configurations, which have demonstrated significant potential for fuel burn reduction for both medium and long-range flight missions. However, to achieve the emission reduction goals of UK's Jet Zero programme, further improvements are required. Cryo-electrification or electrification enabled by combining cryogenics and superconducting technologies, shows potential as a disruptive technology and is considered a promising way forward for future aerospace electrifications. Integrating cryo-electrification with fuel efficient configurations like TBW can potentially have synergistic benefits for enabling near-zero emission flights for both medium and long-range flight missions. This project will focus on conceptual design and analysis of cryo-electric TBW configurations and assess its potential for reaching net-zero aviation in the near future.

Prerequisite skills: undergraduate courses in aircraft performance, aerodynamics and aircraft propulsion. Background in electric propulsion, or electric motors and powertrain in general, is a plus.

The project title is indicative of the research activities. Please contact supervisor directly to discuss available topics in the area.

How to Apply: Please refer to the following website for details on how to apply: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/

Supervisor(s): Dr Wrik Mallik

Application Deadline: N/A

Funding Available: No. Contact supervisor for scholarship availability

Project description

Noise will be a significant factor in the design and operation of urban aerial vehicles. To develop low-noise designs or to devise silent operation strategies for urban aerial vehicles, we need accurate far-field acoustics signatures of such aerial vehicles. Thus, we require a coupled aerodynamic-aeroacoustics analysis of aerial vehicles in urban setting, which can analyse the noise propagation and noise backscattering from urban structures for various flying conditions. The project will involve the development and application of a coupled computational fluid dynamics (CFD)-computational aeroacoustics (CAA) analysis tool. The project will explore various numerical techniques for performing far-field aeroacoustics analysis due to noise generated from various urban VTOL/drone takeoff and flight conditions. Such numerical techniques will be coupled to the aerodynamics of urban fliers to predict noise from various flight conditions. Development of such coupled CFD-CAA framework will enable us to accurately predict noise from urban aerial vehicles and devise strategies for noise reduction from urban air mobility.

Prerequisite skills: Undergraduate coursework in fluid mechanics is required. Interest or experience in computational fluid dynamics are a plus but not essential.

The project title is indicative of the research activities. Please contact supervisor directly to discuss available topics in the area.

How to Apply: Please refer to the following website for details on how to apply: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/

Professor Konstantinos Kontis

Project No. 1: Aerodynamic Optimization of Nonplanar Lifting Systems

Description of project No.1

This topic explores the aerodynamics of nonplanar planforms through wind tunnel testing and computational fluid dynamics (CFD) analysis. It examines both steady and unsteady flow conditions, using multidisciplinary design optimization tools to propose novel aerodynamic concepts for enhanced performance in subsonic regimes.

Project No. 2: Flow Control and Performance Enhancement in S-shaped ducts

Description of project No.2

Investigate the flow characteristics and operational benefits of S-shaped ducts, focusing on fuel efficiency, noise reduction, and lightweight design. Through experimentation and advanced flow control techniques, the research aims to mitigate undesirable flow features while optimizing performance for aviation and propulsion systems.

Project No. 3: Adaptive Aerodynamic Control for Connected and Automated Vehicles

Description of project No.3: 

This topic focuses on the aerodynamic flow control of automated vehicles, employing wind tunnel testing, CFD, and dynamic control methods. The research seeks to develop optimal flow control strategies using Digital Twins and other advanced technologies for automotive applications.

Professor Konstantinos Kontis

Project No. 4: Numerical Simulation and Experimental Investigation of Hypersonic Flows

Description of project No.4

This topic explores the complex flow phenomena encountered in hypersonic regimes, such as those occurring during atmospheric re-entry. Using the Direct Simulation Monte Carlo (DSMC) method and experiments, the research will develop and apply physical models, including chemical reactions, to better understand hypersonic flows at high Mach numbers.

Project No. 5: Plume-Surface Interactions and Mitigation in Lunar and Martian Applications

Description of project No.5

This topic investigates the interactions between rocket exhaust plumes and extraterrestrial surfaces, with a focus on understanding cratering, erosion, and ejecta dynamics. Using experimental setups and numerical simulations, the research seeks to mitigate risks to landing modules and spacecraft systems in future lunar or planetary exploration missions.

Professor Konstantinos Kontis

Project No. 6: Transverse Jet Injection into High-Speed Crossflows

Description of project No.6

This research investigates the aerodynamic behaviour of jets injected transversely into supersonic and hypersonic crossflows. Applications include scramjet combustion systems, thrust vector control, and reaction control jets. Key variables include Reynolds numbers, gas properties, surface roughness, and shock wave interactions.

Project No. 7: Directed Energy Deposition Using ns-DBD, Lasers, and Microwaves

Description of project No.7

The research will focus on the development and optimization of directed energy deposition techniques, including nanosecond Dielectric Barrier Discharge (ns-DBD), lasers, and microwaves. These methods have potential applications in material processing, surface modification, and plasma control in high-speed flows.

Professor Konstantinos Kontis

Project No. 8: Shock Wave Boundary Layer Interaction in High-Speed Flows

Description of project No.8

This topic examines the interaction between shock waves and boundary layers in high-speed aerodynamic environments. The research aims to improve understanding of flow separation, reattachment, and control strategies for systems operating at supersonic and hypersonic speeds, with applications in aerospace engineering.

Project No. 9: Scramjet Intake and Flow Compression Surface Optimization

Description of project No.9

Focusing on the optimization of scramjet intakes and compression surfaces, this research seeks to improve the efficiency of air compression in supersonic combustion systems. The work will involve both CFD simulations and experimental testing to refine intake geometries for optimal performance in hypersonic flight conditions.

Professor Konstantinos Kontis

Project No. 10: Shock Diffraction Phenomena and Richtmyer-Meshkov Instabilities

Description of project No.10

This topic explores shock diffraction and the Richtmyer-Meshkov instability, phenomena encountered in high-speed flow environments. The research focuses on the interaction of shock waves with material interfaces, studying instability growth and mitigation techniques in applications ranging from aerospace propulsion to blast wave dynamics.

Project No. 11: Flow Control Techniques in High-Speed Aerodynamic Systems

Description of project No.11

This research investigates advanced flow control methods for managing high-speed aerodynamic environments, such as shock wave manipulation and boundary layer control. The aim is to develop strategies for reducing drag, enhancing stability, and improving performance in supersonic and hypersonic applications.

Project No. 12: Develop Novel Diagnostics and Study of PSI Phenomena and Interactions

Description of project No.12

This research aims to create and implement novel diagnostic tools to study Plume Surface Interaction (PSI) phenomena. It will focus on investigating the underlying physical mechanisms and improving the understanding of PSI interactions, with applications in spacecraft landing and launch scenarios.

Supervisors team:

Dr. Mohammad Yazdani-Asrami, Dr Wenjuan Song, Prof. David Flynn in University of Glasgow

Sam Tippetts and Dr. Stuart Wimbush in UKAEA

Descrition of project:

A fully funded PhD position for 3.5 years, open to highly motivated UK home students to apply for, with start date no later than 01 September 2025. 

This PhD programme proposes to explore and develop a novel accurate protection technique for high field fusion magnets. The PhD will focus on developing multiple intelligent algorithms to enable fast and reliable magnet protection. The experimental data upon which to base/test the algorithms will be generated and provided by the UK Atomic Energy Authority (UKAEA), based on their testing of prototype systems, as well as small-scale superconducting magnet  via RF measurments. The intelligent protection algorithms developed by UofG will be tested in the UKAEA testing facility with a view to adoption by STEP or a future commercial reactor. 

The student will experience cutting-edge research within a multidisciplinary team in the CryoElectric Research Lab of “Propulsion, Electrification & Superconductivity” group, in which individuals from a diverse range of backgrounds are all supported to thrive.  This PhD programme is in partnership with the UKAEA. The student will also be in close interaction with the lead researchers from UKAEA involved in this project and will exchange knowledge with them regularly, offering a unique practical slant to this studentship.  

More details can be found in the link. PhD in Engineering - Protection of high-temperature superconducting fusion magnets using RF technology and machine learning techniques at University of Glasgow on FindAPhD.com

Supervisors: Dr Tao Zhang and Prof. George N. Barakos

Project description:

Glasgow University is a centre of excellence in Computational Fluid Dynamics (CFD) and Multi-Disciplinary Analysis and Optimisation (MDAO), especially for helicopters, rotorcraft, and rotary wings. The CFD laboratory of Glasgow has produced the HMB3 solver (www.gla.ac.uk/cfd ) that is the UK flagship code for rotary wing analysis.

We are now looking for a highly motivated PhD candidate to tackle a long-standing bottleneck in CFD: meshing. The meshing process has always been an essential, crucial, and labour-intensive task in the CFD workflow. It is a labourious and repetitive process that directly impacts the modelling results, and in many cases, dominates the pace of the entire CFD analysis.

This project investigates a thorough and drastic solution to this bottleneck: high-fidelity meshless CFD methods. The goal is to entirely eliminate the need for meshing in CFD workflows via new numerical schemes and paradigms. The outcome will fundamentally change current CFD paradigms, and pave the way for a fully automated CFD workflow that will work seamlessly with AI agents and digital twins.

The project builds on existing world-leading developments on collocation-based meshless CFD methods in the CFD lab. Moreover, we are to combine these developments with cutting-edge physics-informed machine learning techniques to solve the governing flow equations. We will adopt hybrid CPU/GPU architectures for massive parallel computing for industrial applications.

The project will include leading industrial partners in aviation, including the UK Vertical Lift Network, GKN Aerospace, Sora Aviation, the Vertical Flight Society, and the GARTEUR group. The developed meshless CFD methods will be trialed by our partners, and will be applied to complex and practical aerospace developments including helicopters, wind turbines, and emerging multi-rotor vertical take-off and landing vehicles. These lead to practical real-world impact, and provide an ideal career development opportunity for the right candidate.

How to Apply: 

Interested students should email Dr. Tao Zhang  (tao.zhang@glasgow.ac.uk). A successful candidate should have a solid background in CFD, aerodynamics, and computer programming.

Supervisors: Dr Tao Zhang and Prof. George N. Barakos

Project description:

Propellers, helicopter rotors, wind turbines, and tidal turbines, broadly known as rotary wings, are commonly used in aviation and green energy. These rotary wings are characterised by fascinating yet complex physics such as tip vortices, interference, flutter, and blade-vortex interactions. Glasgow University is a centre of excellence in CFD and in AAA (Aerodynamics, Aeroacoustics, Aeroelasticity) of rotary wings. The CFD laboratory of UofG has produced the HMB3 solver that is the UK flagship code for rotary wing analysis (www.gla.ac.uk/cfd).

Nevertheless, blade-resolved CFD modelling of rotary wings remains a challenging and resource-demanding task, especially when an array of propellers is involved such as in wind farms and distributed propulsions. We are now looking for a highly motivated candidate to develop novel rapid and intelligent virtual blade models for rotary wings.

The objective is to create explainable and robust AI models that accurately predict rotary wing physics, and run almost in real-time when coupled with state-of-the-art CFD methods. This is a challenging and ambitious research objective, but achievable with the state-of-the-art multi-fidelity physics-informed machine learning techniques being developed now in the CFD lab, along with the abundant rotary wing database collected over the years.

A successful candidate should have a solid background in CFD, aerodynamics, and computer programming. The candidate will work in a vibrant, warm, and highly supportive environment in the CFD Lab. The project will work closely with leading industrial partners in aviation and green energy, including the UK Vertical Lift Network, GKN Aerospace, Sora Aviation, the Vertical Flight Society, and the GARTEUR group, providing an ideal career development opportunity for the right candidate. The developed virtual blade models will be trialed with our partners to accelerate their aircraft/turbine design and to deliver real-world impact.

How to Apply: 

Interested students should email Dr. Tao Zhang  (tao.zhang@glasgow.ac.uk).