PhD Opportunities
Possible research topics to be undertaken in the Autonomous Systems and Connectivity Division are given below. If you are interested in any of these projects, you should email the prospective academic.
Alternatively, you are welcome to identify a different project topic within any relevant research area by emailing your project proposal to any of the Staff of the Autonomous Systems and Connectivity Division using the Our Staff link on the left panel.
The current call for scholarship applications will close on 31 January 2025.
Social-Techno-Economic Analysis of Hydrogen Integration for the Energy Transition.
Description
|
The UK like many nations needs to radical reform its energy services as to support rapid decarbonization. The energy transition from decarbonization, net zero and towards sustainable solutions, must also be responsive to the needs of society, in terms of energy availability and affordability. Energy is a significant factor in both individual quality of life and prosperity, but also an integral factor in national and global inflation. Responsible Research and Innovation in the energy transition requires us to consider equity and equality in the design of future energy systems and services. Technically, the energy transition requires the deployment of different technologies such as heat pumps, electric vehicles or other hydrogen-based solutions for the supply of decarbonized heat, electricity or transport. Tomorrow's energy system will be a mixed energy system, with several energy carriers interacting with each other. For example, gas turbines or fuel cells for the transition from gas to electricity, electrolysers or batteries for the transition from electricity to gas or transport. With an emphasis on People and Place, future energy systems need to unlock capacity from existing energy infrastructure and utilize energy demand flexibility from consumer and prosumer engagement. Social-Techno-Economic modeling has been identified as weakness in UK Energy Systems Modelling by UK-ERC. To address this weakness, the PhD will explore the social and economic dynamics of consumer and prosumer perspectives and behaviours to inform the modelling. In response to rising energy costs, a need to redefine energy citizenship and improve consumer agency, as well as the resilience of local, regional, and national energy services through intelligent demand response, our new understanding of the social-techno-economic modeling of hydrogen integration, into multi-vector smart local energy systems (SLES), is integral. Given the widespread nature of these challenges, a holistic approach is needed to address them. Although various technological solutions exist (electrochemical storage, hydrogen technologies, decentralised control of electric vehicles via Smart Charging, Smart Heating, Demand Response, energy communities, etc.), their economic viability and social acceptability still need to be analysed and improved to facilitate the energy transition. This PhD aims to understand the drivers for the economic and social success of a flexible, multi-vector energy system’s management solutions, especially energy systems integrating hydrogen solutions. The main outcome of the PhD will consist in the integration of social aspects into multi-agent-based models of future energy systems. This requires that the PhD candidate develops an expertise in energy, in modelling, and in social science studies. The research will involve social-techno-economic modelling of future multi-vector energy systems, using a multi-agent systems approach to explore new scenarios, planning and control strategies for distributed whole energy systems. This modelling phase will include the modelling and design of new solutions for local multi-vector energy markets, and the modelling of energy flexibility strategies. In the second phase, the PhD candidate will study the factors that promote or limit the social acceptability by the residential sector of the new energy transition technologies, strategies and solutions studied and modelled in the first stage. This will be done by means of questionnaires, scenarios, focus groups and solution testing. Outcomes of this research will include; influence on new social-demand-response strategies, local energy market business models, and an assessment on the influence of residential flexibility on decarbonization and energy service resilience. The PhD student will work with other members of the Glasgow Centre for Sustainable Energy and the Digital Society and Economy (IRT). Leverage expertise in digital technologies, energy systems and social sciences. This PhD is funded by the University of Glasgow in support of the EPSRC National Hydrogen Integration Hub (HI-ACT). Applicants with interests in Social Science applied to the energy transition, Integrated Energy System Modelling, Multi-Agent Modelling and Systems Engineering. Closing Date: 15 January 2024 Start Date: 2024 How to Apply: Please refer to the following website for details on how to apply: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/. Please also complete a Scholarship Application via the Scholarship Portal at the following link: University of Glasgow | The Scholarships Application Portal Please note that as this process also requires your proposed supervisor to upload a supporting letter, you should allow sufficient time to complete the application in advance of the scholarship closing date of 31 January. |
Federated Digital Twins for Resilience Modelling of Transport Infrastructure
University of Glasgow - Schools - James Watt School of Engineering - Our staff - David Flynn
Description
|
This PhD will explore the emergent role of Cyber Physical Infrastructure (CPI) and Federated Networks of Digital Twins in the design and validation of decarbonization strategies and resilience analysis for future transport services and infrastructure. The roads, cables, and pipes of the UK enable goods to arrive, lights to glow, and tea to flow. As our climate changes and resources become more scarce, it is necessary to decarbonize the UK’s infrastructure and ensure it is resilient to these changes. Rapid decarbonization of our infrastructure will be dependent on de-risking and reducing uncertainty about our decarbonization strategies, as well as enabling co-design of future solutions to include transport-energy infrastructure, transportation types, and social (behavioral factors). This research will answer “how do we validate decarbonization strategies?” by exploring concepts such as symbiotic systems of systems analysis, ontology mapping, stochastic programming, distributed and autonomous systems, federated learning, and co-simulation to map, design, and analyse the complex adaptive system which exists at the transport-energy-community nexus. Through a federated network of digital twins, we will explore optimization strategies to enable automated self-organization, resilience, and support positive behavioural change in infrastructure demand. As part of the EPSRC DARe consortium, this PhD will work with people and data from a range of different communities and places. We will combine knowledge and meta-data from people, with real-time data from distributed infrastructure monitoring nodes via CPI and use federated digital twins to explore “symbiotic” and assistance-based learning and reasoning to create solutions in response to changing climate patterns and user demands. Applicants with interests and expertise in systems engineering, digital technology, ICT/CPI , ontology, control theory and digital twins, would be highly relevant to this opportunity. The successful applicant will be affiliated with the Autonomous Systems and Connectivity Division and the Glasgow Centre for Sustainable Energy. In March 2023 the UK Government released its first consultation report on the UKs Cyber Physical Infrastructure:Enabling a national Cyber-Physical Infrastructure to catalyse innovation: consultation document (accessible webpage) - GOV.UK (www.gov.uk) . This studentship is an opportunity to work at the frontier of this emergent and disruptive technology in the context of critical infrastructure and decarbonization. Closing Date: applications accepted for 2024/25 How to Apply: Please refer to the following website for details on how to apply for admission: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/. |
Modelling Flexible Nuclear Power Plant Integration in Future Low Carbon Energy Systems
Prof David Flynn and Dr. Yiji Yu
Description
|
The increasing penetration of intermittent renewable power within the UK energy network will require additional flexibility from conventional sources of generation, such as nuclear power plants. This flexibility is required to support the coupling of increasingly stochastic energy demand and generation patterns. In this context, coupling nuclear reactors with hydrogen, electrical and thermal energy storage could ensure a more efficient operation of nuclear power plants, while also enabling accelerated rates of decarbonization through more flexible and cost-effective services for consumers. In this context, coupling nuclear reactors with hydrogen, electrical and thermal energy storage could ensure a more continuous and efficient operation of nuclear power plants, while at other times allowing their operation to become more flexible and cost-effective for consumers. In addition, such domestic generation has the potential to enhance national energy security and resilience, from the geopolitical dynamics that influence the stability of UK energy market prices. This PhD will explore future energy scenarios and techno-economic modelling of multi-vector services e.g., heat, electricity, and hydrogen from Nuclear Power Plant Integration (NPPI) into Whole Energy System models. Regional and UK national optimization methodologies will be explored. The total whole-system benefits of operating different reactor configurations will be quantified for several scenarios in the context of the current and future (potential) reward mechanisms within the UKs energy market. An objective of this research will be to use whole-system and multi-objective optimization analysis, coupled to multi-agent market analysis, as to understand how best to minimizes the total system costs of NPPI, and to conduct a comparative analysis of traditional nuclear power plants vs flexible NPPI. Learning from this PhD will enhance knowledge in areas such as; the optimal system characteristics of Nuclear Power Plants; influence of intelligent energy demand and multi-vector profile clustering, risk analysis of future NPPI, and pathways for nuclear energy within future whole system energy integration. The project will support the UK in delivering a new generation of Advanced Modular Reactors, which will help the UK meet its obligations to transition to clean energy. This PhD is a strategic partnership between the UKs National Nuclear Laboratory (NNL), University of Glasgow and the National Hydrogen Integration Hub (HI-ACT). Applicants with interests in Integrated Energy System Modelling, Energy Economics, Multi-Agent Modelling, Nuclear Energy, Whole Systems, Cyber Physical Infrastructure and Systems Engineering. In March 2023 the UK Government released its first consultation report on the UKs Cyber Physical Infrastructure:Enabling a national Cyber-Physical Infrastructure to catalyse innovation: consultation document (accessible webpage) - GOV.UK (www.gov.uk) . This studentship is an opportunity to work at the frontier of this emergent and disruptive technology in the context of critical infrastructure and decarbonization.
How to Apply: Please refer to the following website for details on how to apply for academic admission:http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/.Please also complete a Scholarship Application via the Scholarship Portal at the following link: University of Glasgow | The Scholarships Application Portal Please note that as this process also requires your proposed supervisor to upload a supporting letter, you should allow sufficient time to complete the application in advance of the scholarship closing date of 31 January. |
Federated Digital Twins for Reducing Risk and Uncertainty in Hydrogen Integration.
Description
|
The energy transition is a highly stochastic and complex problem, especially given the increasing demand to rapidly decarbonize through whole systems thinking – coupling previously independent critical infrastructure and services e.g., energy-transport networks etc. Energy networks need to unlock new network capacity as to meet growing energy demand and in response to the long lead in times in distribution and transmission network reinforcement. The integration of hydrogen into whole system-based solutions has the potential to accelerate decarbonization within traditionally hard to decarbonize sectors e.g., heavy industry, transport etc., and offer a potential solution for existing energy network constraints. A scalable hydrogen economy will need to be built on targeted hydrogen decarbonization interventions. Ensuring that the hydrogen production method(s) deliver scalable, affordable, and resilient decarbonisation services, responsive to dynamic demand patterns. Using Cyber Physical Infrastructure (CPI) for the bidirectional exchange of data, information, feedback and analysis, we can now create new models that can help us to reduce uncertainty and risk in the design of hydrogen integration. The PhD research topic will explore sources of uncertainty in modelling hydrogen integration and optimization. And through a federated network of digital twins using Cyber Physical Infrastructure (CPI), improve our understanding of how to deliver a scalable hydrogen economy. The federated network of digital models will include data and information from various sources, energy system models and related infrastructure. It should also include the whole spectrum of the hydrogen ecosystem and supply chain from production, storage, distribution system and usage. The federated network of digital models should focus on representative case studies exploring different pathways for hydrogen integration and enable the optimisation of hydrogen in energy systems for range of future energy system scenarios and real-time simulations. The development of strategies that evaluate the (cyber-physical) resilience of future multi-vector energy networks with hydrogen integration will be an area of investigation. This PhD is a strategic investment as part of the University of Glasgow’s commitments to the UKs National Hydrogen Integration Hub (HI-ACT). The successful applicant will be affiliated with the Autonomous Systems and Connectivity Division and the Glasgow Centre for Sustainable Energy. Applicants with interests in Integrated Energy System Modelling, Ontologies, Digital Twins, Multi-Agent Modelling and Systems Engineering. Start Date - 2024 In March 2023 the UK Government released its first consultation report on the UKs Cyber Physical Infrastructure:Enabling a national Cyber-Physical Infrastructure to catalyse innovation: consultation document (accessible webpage) - GOV.UK (www.gov.uk) . This studentship is an opportunity to work at the frontier of this emergent and disruptive technology in the context of critical infrastructure and decarbonization. How to Apply: Please refer to the following website for details on how to apply for academic admission: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/. Please also complete a Scholarship Application via the Scholarship Portal at the following link: University of Glasgow | The Scholarships Application Portal Please note that as this process also requires your proposed supervisor to upload a supporting letter, you should allow sufficient time to complete the application in advance of the scholarship closing date of 31 January. |
A Digital Twin-based approach for Nuclear Reactor Design and Prognosis. (alternatively we could focus on Reliability Availability Maintenance (RAM))
Description
|
The design and lifecycle analysis of low carbon technologies, such as nuclear reactors, is vital to ensuring that nuclear technologies can support dynamic energy demand profiles and support verifiable decarbonization of energy services. Through the integration or networking of digital models – including digital models, shadows and twins, we can better understand the design, operational, and planning decisions we need to make as to secure energy services. ICT or Cyber Physical Infrastructure provides the data and information pipeline, that allows us to create more accurate models in response to reducing risk and uncertainty in the design of increasingly coupled systems of systems. Digital Twins are more advanced than purely numerical, or physics based models, and provide complimentary value to improving our understanding of the dynamic interdependencies between previously uncoupled inputs and systems. Digital Twins provide a virtual proxy version of the physical system, constructed from the integration of historical, offline, and online data with models of differing fidelity, using novel techniques in uncertainty analysis, model reduction, and experimental validation. In this PhD, the student will explore transformative new science and engineering, integrating methods from prognostics and health management, ontologies, model-based design and systems engineering. The results from the project will empower NNL with the ability to create digital twins as predictive design and lifecycle analysis tools for real-world applications of nuclear reactor designs (i) radically improving the design methodology of future technologies leading to significant cost savings, (ii) enhance the accurate of lifecycle analysis of nuclear reactors and (iii) transform predictive asset management in the context of enabling a step change reduction in the associated operation and management costs of nuclear reactors. Research will involve an integration of semi qualitative-quantitative analysis, simulation, and hardware-in-the-loop statistical design of experiment. An area for investigation will include supporting an existing NNL £15M collaboration on the Front-End Engineering Design of a UK-Japan High Temperature Gas cooled reactor. Exploring a digital twin for the design of a heat exchange manifold system focused on hydrogen production. This PhD is a strategic partnership between the UKs National Nuclear Laboratory (NNL), University of Glasgow and the National Hydrogen Integration Hub (HI-ACT). The project will support the UK in delivering a new generation of Advanced Modular Reactors, which will help the UK meet its obligations to transition to clean energy. Applicants with interests in Multi-Objective Optimization, Digital Twins, Nuclear Energy, Energy Systems, Model Based Design and Systems Engineering. Closing Date: 15 January 2023 Start Date: 2024 How to Apply: Please refer to the following website for details on how to apply for academic admissions:http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/. Please also complete a Scholarship Application via the Scholarship Portal at the following link: University of Glasgow | The Scholarships Application Portal Please note that as this process also requires your proposed supervisor to upload a supporting letter, you should allow sufficient time to complete the application in advance of the scholarship closing date of 31 January. |
Design of Helicopter rotor blades using high-fidelity methods [Funded]
Project Summary (8/11/23)
Glasgow University is a centre of excellence in CFD and in AAA (Aerodynamics, Aeroacoustics, Aeroelasticity) of rotary wings. This project is part of a large international effort to bring the aeroelasticity in the design of helicopter blades. The CFD laboratory of Glasgow has produced the HMB3 solver that is the UK flagship code for rotary wing analysis. The tool can be use for design of rotors accounting for the effects of aeroelasticity and this has so far been demonstrated in an earlier PhD project. We are now looking for a student to continue this work and bring in the design methodology a detailed structural modelling of the blades using finite element and finite volume methods. The project partners include NASA, the US Army, DSTL, and the NRC/CNRS of Canada, provides an ideal career development opportunity for the right candidate.
Earlier work on the topic, can be found at: University of Glasgow - Schools - James Watt School of Engineering - Research - Autonomous Systems and Connectivity - Research themes - Modelling and Simulation - Design of Helicopter Rotor Blades
A successful candidate should have a solid background in CFD, aeroelasticity, dynamics and computer programming.
The project is offered to candidates classified for home fees and includes a stipend and home fees for 3.5 years.
Interested students should email Prof. G. Barakos (george.barakos@glasgow.ac.uk)
Intelligent Reconfigurable Environments for Enhanced Visible Light Communications
Description
|
The recent rise of the revolutionary intelligent reconfigurable metasurfaces means that the physical environment can be programmed to enhance the wireless communications performance. This enhancement comes in terms of spectral efficiency, energy efficiency, link reliability, and security. Based on this, it is possible to effectively control the wireless signals to achieve the desired performance gains. This project aims to investigate the use of programmable wireless environments in LiFi systems, which are based on visible light communications. The research will explore how metasurfaces could manipulate the light signals in various setups such as indoor, vehicle-to-vehicle, and aircraft cabin communication systems. Efficient and cost-effective algorithms will be developed to steer and control the wireless propagation in order to achieve various key performance indicators in LiFi systems such as enhanced spectral efficiency and higher physical layer security. How to Apply: Please refer to the following website for details on how to apply for admission: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/. Please also complete a Scholarship Application via the Scholarship Portal at the following link: University of Glasgow | The Scholarships Application Portal |
Light-based Localisation for Internet-of-Things (IoT) applications
Description
|
AI-empowered LiFi Physical Layer Design for LiFi Systems
Description
|
Acoustic simulation of multi-rotor eVTOL using NS and LB methods
Project Summary (8/11/23)
The development of multi-rotor electric powered vehicles brought forward the need to model the acoustics of propellers with high fidelity tools. The eVTOL configurations currently under consideration, are not only producing significant noise, but their noise characteristics change according to their operating environment and conditions, with several acoustic interactions taking place between the radiated noise and their surroundings. The need to model complex multi-rotor vehicles makes the problem harder. The CFD laboratory of Glasgow is the home to one of the most advanced CFD solvers in the world (Helicopter Multi-Block 3) and has access to a local high-performance computing cluster dedicated to and run by the lab, as well as access to national and inter-national parallel computers. The project builds on earlier work of the laboratory, reported at: University of Glasgow - Schools - James Watt School of Engineering - Research - Autonomous Systems and Connectivity - Research themes - Modelling and Simulation - eVTOL Aeroacoustics
and
A successful candidate should have a solid background in CFD, aeroelasticity, dynamics and computer programming. The project will run in collaboration with the GARTEUR AG26 international effort on eVTOL acoustics, providing the right candidate with a unique opportunity for career development.
Interested students should email Prof. G. Barakos (george.barakos@glasgow.ac.uk)
Integrated Terrestrial and Satellite Networks
Description
|
Artificial Intelligence-Enabled Converged Networks
Description
|
Future networks should be intelligent enough to adapt to very dynamic topologies, intensive computation and storage applications, and diverse quality of service (QoS) requirements for ultra-high efficiency and resiliency purposes. The integration of artificial intelligence through machine learning techniques has recently emerged as a viable approach to make wireless communication highly efficient and adaptable. The Ph.D. will thus develop an AI-enabled resource management framework for the next-generation networks to meet the quality of service and quality of experience requirements in a highly dynamic network environment. The developed algorithms and protocols will be verified on both the link-level and system-level simulation platform. The main technologies involved in the Ph.D. include artificial intelligence, machine learning, network slicing, and optimization. How to Apply: Please refer to the following website for details on how to apply: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/ |
Research and Evaluation of traditional and AI-driven control methodologies for robotic manipulator led surgical procedures
Dr Kevin Worrall and Dr Gerardo Aragon Camarasa
Project Summary
What methods of control offer the required robustness, accuracy, and responsiveness to enable autonomous control of surgical robotic manipulators which meet medical requirements? It is essential to build a control methodology which could allow a robotic agent to execute surgical tasks precisely, with a high level of accuracy and guarantee meeting the safety requirements. Given the current advancements in control, the project with research and compare methodologies based on artificial intelligence, machine learning, and traditional control. In addition, a vision system will be developed to support the controllers developed. The development of a vision-based control system will allow approaches to be researched that could lead to the proper operation and coordination of a robotic agent working along with a group of humans in a surgical setting.
This PhD project will investigate novel control methods, including traditional and AI-based, to create a reliable robotic platform to execute surgical tasks, dealing with the required level of safety, invasiveness, and quality. The study will take previous work on the subject to improve existing methods or propose new ones. It aims to engage with recent research studies on the field of autonomous surgical robotics. The project could also support studies on advanced manufacturing and manufacturing in space.
Low-order modelling of unsteady, nonlinear fluid dynamics using “Scientifically-Based” machine learning
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.
In-orbit assembly: Robust autonomous methods for controlling robot manipulators in space
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.
Investigation of axisymmetric turbulent boundary layers using direct numerical simulation
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.
Vortex dynamics
Dr Richard Green and Dr Hossein Zare-Behtash
Description
Propellers are an important class of propulsion system due to their extremely high efficiency at low flight speed. Impressive understanding of propeller aerodynamics was gained leading up to the 1940s, but propellers have been overlooked for decades with research concentrating upon propulsion systems for higher speed flight. Interest is being revived with a drive towards so-called greener aviation and possibilities afforded by distributed propulsion systems due to electrification. Recent research at Glasgow University into propeller aerodynamics has investigated the inflow to a propeller, and it has been revealed that the flow field has far more interesting features than have been previously supposed. This project is to extend this work by looking at the flow leading into propellers due to installation effects, for example due to the position of a wing or fuselage nearby. The project will use experimental methods in a wind tunnel.
Flow over bodies with excrescencies due to growth of biological matter
Dr Richard Green and Dr Angela Busse
Description
Shipping, marine structures, and tidal turbines all suffer significant performance degradation due to growth of biological agents. These include algae and plant growth and encrustation by animals such as barnacles. Research work in this field has been conducted at Glasgow using theoretical, computational and experimental approaches, and this research project is to build upon this. Candidate projects include the effect of barnacle encrustation on hydrofoil surfaces for use with tidal turbines, and specifically what is the loss of potential power production due to this? There is limited understanding of the unsteady flow field in the presence of isolated barnacles or clusters of them, and this aspect is expected to be the major thrust of this research effort. Both supervisors have extensive experience in computational and experimental work in this field, and the PhD candidate will be expected to develop computational and experimental methods. Contact either Dr. Angela Busse or Dr Richard Green if you are interested in conducting a research project in this field.
Unsteady flow of jets and wakes
Dr Richard Green and Dr Angela Busse
Description
Fluid dynamic jets and wakes are rich in phenomena of fundamental significance that are fascinating to observe but are a challenge to predict, measure and understand. As such jets and wake flows have formed the basis of many investigations in fluid dynamics that are relevant to flows in nature, for aircraft and other vehicles, civil engineering, meteorology among other fields. Of particular importance are jet and wake flows that break down into powerful vortex structures as a consequence of the development of fluid dynamic instabilities. Experience at Glasgow in this research in this area includes development of rotor wakes, jet configurations and bluff bodies for the study of both fundamental fluid dynamics and applied aerodynamics. The project supervisors have extensive research experience in this field, and use computational, theoretical and experimental approaches. Contact either Dr. Angela Busse or Dr Richard Green if you are interested in conducting a research project in this field.
High Performance Bio-Inspired Topologically Optimised and Smart Composite Structures
Description
Polymer matrix composites usage is growing rapidly due to their superior strength, stiffness, lightness and low susceptibility to fatigue and corrosion. There is rapid expansion of composite use in aerospace and other applications, such as wind turbine blades, sporting goods and civil engineering. Recent examples include large civil aircraft, such as the Boeing 787 and the Airbus A350, high performance cars, such as the McLaren 650S, and civil infrastructure, such as the Mount Pleasant bridge on the M6 motorway. Despite this progress, composite structures will often fail through poor design, where stress concentrations appear around sharp changes in topology e.g. edges, holes, corners, or due to concentrated loads such as impact. In addition, the damage in composite materials is hidden and failure is without any warning and mainly catastrophic. Therefore, designers are forced to apply conservative design approaches which do not fully exploit the properties. For example, maximum allowable design strains can be as low as 0.1% for carbon fibre composites, despite maximum failure strains of up to 2%.
This project intends to explore bio-inspired examples of stress distribution in living organisms and to utilise some of those methodologies in the design of composites structures. These designs can be mimicked due to advances in modelling, characterisation and manufacturing of composites. The project’s vision is to develop a new generation of high-performance and smart composite structures based on nature’s generative design principles to overcome the aforementioned limitations. These bio-inspired algorithm-based composites will improve both safety and design strain limits at the same time, shifting the traditional dilemma between performance and safety. A step change in the design and performance will be achieved compared to current materials, resulting in simple and cheap approaches for optimal design and health monitoring. The outcomes of this project will enable full exploitation of the weight saving benefit in composite structures by overcoming the limitations of traditional conservative designs and avoiding expensive inspections. Such materials will provide greater reliability and safety, together with reduced design and maintenance requirements, and longer service life.
The key research questions: 1. How to produce bio-inspired topologically optimized and smart composite structures to overcome the limitations of current composite structures, i.e. over-engineering and catastrophic failure. 2. How to generate design tools for implementation of these high-performance bio-inspired topologically optimized and smart composite structures?
Established in 1451, the University of Glasgow has been a home to aerospace research for over 80 years, and today our researchers are tackling the multidisciplinary challenges faced by the aerospace industry in the 21st century. We have excellent modelling, simulation and ESA-ESTEC Testing facilities.
The ideal PhD candidate has a degree in either aerospace, mechanical, materials, or a related area with an excellent research track record.
Funding and application
For funding opportunities and the application process, please consult: https://www.gla.ac.uk/schools/engineering/phdopportunities/
Contact
For an informal discussion or further information on this project, please contact: mohammad.fotouhi@glasgow.ac.uk
A theoretical and experimental investigation of subscale flight testing
Description
Accurate subscale models have been used by aircraft designers since the earliest days of flight and continue to inform the design process of new aircraft today. The most common use of accurate models is in wind-tunnel testing, where the objective is to predict aircraft aerodynamic loading and interactions for a range of flight conditions. However, there are limitations to the range of experiments that can be conducted within a wind tunnel, particularly when measuring loads under dynamic vehicle conditions. Unfortunately, understanding such edge cases is critical when developing new high-bandwidth flight controllers, to ensure aircraft safety, efficient operation etc., with problems often only discovered during the final flight-testing programme, leading to very costly delays. Consequently, there is a desire (and significant financial benefit) to be able to identify any stability, control or performance issues earlier in the design process.
This PhD research project will investigate the usefulness of data obtained from conducting subscale flight tests with dynamically scaled aircraft within the context of aircraft design. Overcoming the issues associated with Reynolds number scaling – a significant problem for wind-tunnel experiments will be the primary objective. Specifically, machine learning techniques will be used to extrapolate data collected during subscale flight tests to help validate mathematical models of the full-scale aircraft. This will be accomplished by developing an accurate mathematical simulation that can be configured for both full-scale aircraft and models of differing dynamic scale factor. Model validation at the subscale end will be achieved by fabricating several dynamically scaled aircraft with a common sensor suite and conducting a flight test program for each. To ensure that flight test data for the full-scale aircraft will also be available, the vehicle chosen for this study will be Cranfield Universities new flying laboratory aircraft, the SAAB 340B.
Learned Vision Based Obstacle Avoidance for μUAV
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.
Improvement of wheeled vehicle safety and performance using inverse simulation
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.
Modelling of Electronic Attack in a complex EME
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.
Battlefield Effect Assessment Using Digital Twins
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.
Superconducting Fuses for Renewable Energy Systems
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.
Superconducting Flux Pumps
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.
Deep learning architectures for model reduction and prediction of unsteady fluid-structure interaction phenomena
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
Parametric level set methods for aerofoil morphing and aerodynamic shape optimization
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
Product platforming techniques for evolvable hydrogen civil transport aircraft
Description
|
Aeroelasticity and shape optimization of flexible next-generation aircraft configurations
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/
Conceptual design and analysis of next-generation electric aircraft configurations
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/
Coupled aerodynamic-aeroacoustics analysis for silent design and operation of urban aerial vehicles
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/
Aerodynamic Optimization of Nonplanar Lifting Systems & Flow Control and Performance Enhancement in S-shaped ducts & Adaptive Aerodynamic Control for Connected
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.
Numerical Simulation and Experimental Investigation of Hypersonic Flows & Plume-Surface Interactions and Mitigation in Lunar and Martian 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.
Transverse Jet Injection into High-Speed Crossflows & Directed Energy Deposition Using ns-DBD, Lasers, and Microwaves
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.
Shock Wave Boundary Layer Interaction in High-Speed Flows & Scramjet Intake and Flow Compression Surface Optimization
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.
Shock Diffraction Phenomena and Richtmyer-Meshkov Instabilities & Flow Control Techniques in High-Speed Aerodynamic Systems
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.