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The University Graduate School is offering PhD studentships for October 2023 entry. The Faculty of Engineering, Computing and the Environment mainly accepts applications for specific pre-approved which will be given priority for funding (see project proposals).
The Faculty is particularly keen to receive applications for projects in its priority areas that are closely linked to the activities of its two Research Centres of Excellence (Centre for Engineering, Environment and Society Research and Digital Information Research Centre). See advertised projects below. A project proposal is not required with the application - you should upload a Word document with the title of the selected project and supervisor's name instead
In addition, the Faculty welcomes applications for projects advertised by its staff on the Find a PhD website. If you wish to apply for one of these projects, you must include the project title and supervisor's name with your application. A project proposal is not required.
The Faculty will also consider applications from exceptional candidates whose research vision is aligned with its activities. In this case, applications must include a full research proposal and the name of a member of staff who has been identified as a suitable supervisor. Please see the information on how to write a project proposal.
Applicants are strongly encouraged to contact the project's first supervisor to discuss their interest before making an application.
In recent years, the Internet of Things (IoT) has progressed dramatically due to advancements in technology. It plays an important role in the development of abundant applications and economic ventures. In IoT, smart devices communicate with each other using the Internet without any human intervention. The 6G vision also considers the massive IoT as a driving force, in which a strong relationship has been identified between 6G and high-performance mobile edge computing . While edge computing resources will handle some of the IoT and mobile device data, much of it will require more centralized resources to perform the data processing task.
To execute the big data analytics generated by IoT devices easily, the whole IoT network can be divided into different subnetworks and data of each subnetwork is collected, aggregated and forwarded by their respective central nodes. In such cases, the presence of malicious nodes causes severe issues in the sensing results, localization and service provisioning, which discourages new entities to join the network. Therefore, it is very important to establish trust between all entities by detecting and removing such nodes. Moreover, for reliable service provisioning in IoTs, the service provider nodes deliver services to the client nodes. However, there is no mechanism to collect enough information that assures the non-repudiation of both the service provider and client node in the service provisioning mechanism. A blockchain-based localisation mechanism is proposed for resource-constrained IoT nodes to find their location . However, there is no mechanism yet to prevent the client nodes from repudiating demanded services.
The federated learning for the detection of malicious nodes will be further studied, which uses SVM and RF classifiers for identifying malicious nodes . In terms of implementation, the data of the IoT network is initially collected by the respective sink node and then provided to the virtual machine associated with it. The model on this virtual machine is trained using the distributed dataset. After the models' training, the trained models are sent to the B5G fog server. The B5G fog server fuses all the models and sends the fused model back to each virtual machine. Then these virtual machines use this model for the classification of legitimate and malicious nodes of their clusters.
The project aims to investigate distributed AI and implement federated learning techniques in the dense IoT network in which malicious nodes are detected in the network without compromising the privacy of sensing data. Federated Learning enables the IoT's data to remain on the device, the model will be further investigated to articulate how it learns with time and with the collaborative effort of all distributed IoT devices as agents.
Applicants should have, at least, an honours degree at 2.1 or above (or equivalent) in Computer Science or a related discipline. In addition, they should have a good mathematics background, excellent programming skills in Python/MATLAB and an interest in machine learning and AI.
Qualified applicants are strongly encouraged to contact informally the lead academic, Dr Deepak, GC (email: email@example.com), to discuss their application.
 Chen, M. Y., Fan, M. H., & Huang, L. X., "AI-based vehicular network toward 6G and IoT: Deep learning approaches" in ACM Transactions on Management Information System (TMIS), 13(1), 1-12, 2021  A. Thakkar and K. Kotecha, "Cluster Head Election for Energy and Delay Constraint Applications of Wireless Sensor Network," in IEEE Sensors Journal, vol. 14, no. 8, pp. 2658-2664, Aug. 2014.  Lim, Wei Yang Bryan, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, and Chunyan Miao. "Federated learning in mobile edge networks: A comprehensive survey." IEEE Communications Surveys and Tutorials 22, no. 3, pp. 2031-2063, 2020.
Supervisor: Dr Deepak GC firstname.lastname@example.org
The high-frequency emergence of drug-resistant diseases and recent surges of fast-spreading communicable diseases pose a challenge to medical professionals when treating patients. Indeed, not only can determining the drug resistance of a mutant disease/organism be lengthy, but in some cases, this can be lethal.
This PhD project will explore, learn, and design new deep-learning techniques and natural language processing (NLP) methods to predict the causes of drug resistance at the molecular level. As their data-hungry nature, poor interpretability and inherent non-interoperability are limitations of deep transfer models, this project will seek to address these using novel approaches to enable their wider usage.
The core hypothesis behind this project is the exploitation of deep learning-based NLP models that have the unique property of capturing spatial relationships among terms or words in natural texts. Based on them, the PhD candidate will develop novel methods to capture the subtle long-distance inter-relationships in the genetic code of known drug-resistant variants. Those models should be able to discover new patterns currently out of reach from current state-of-the-art methods, e.g. Drug-Resistant Mutation (DRM) profiles.
To validate putative discoveries, the candidate will mine the literature to attempt to explain the detected inter-relationships. This will provide an opportunity to create a novel method to "interpret a large body of text at a pace". This will require investigating keyword-directed searches to perform topic modelling using named entity recognition and extended classification mechanisms.
Key contributions to knowledge from this PhD work will include pre-trained, transferable models for a single disease by analysing relevant factors. In addition, a stretch goal for the candidate will be to apply (through model tuning) the method/model to a new/potential drug-resistant disease/organism.
Applicants should have at least an honours degree at 2.1 or above (or equivalent) in Engineering, Computer Science or a related discipline. In addition, they should have excellent programming skills in Python, understanding of statistics and an interest in machine learning and AI.
Qualified applicants are strongly encouraged to contact informally the first supervisor to discuss their application.
Supervisor: Dr Farzana Rahman email@example.com
Collaborating with others is an essential part of work and play, and digital platforms that facilitate remote collaboration are becoming ever more important. This is due to an increased reliance of companies on teams distributed around the globe, a growing awareness of sustainability concerns about travel, as well as adoption of remote-working practices in the aftermath of Covid-19.
Such globally-distributed teamwork typically involves tasks and personnel distributed over different time zones, which requires that systems support asynchronous work-practices. Importantly, asynchronous collaboration has several advantages over synchronous collaboration, as it increases the ability of teams to work in parallel, coordinate flexibly on time-management, while also giving teams more time to review and reflect on the tasks at hand (Mayer, 2022). Currently, instant messaging platforms such as Slack and WhatsApp natively support asynchronous interactions, while groupware such as Microsoft Office, Google Workspace and GitHub provide rich sets of tools for large groups of distributed teams to coordinate. However, despite the importance and ubiquity of asynchronous collaboration, there are currently no commercially available virtual or augmented reality platforms (XR) which support asynchronous collaboration. The main reason is that, although the interaction design criteria for standard groupware are well established, building such platforms in XR requires addressing multiple interrelated Human-Computer Interaction challenges in 3D space whose constraints and affordances are still poorly understood (Chow, 2019).
For example, instant messaging platforms such as WhatsApp support asynchronous interaction by providing simple, but subtly sophisticated affordances that go beyond what is possible in face-to-face communication. Instead of improvising on the spot, users can privately formulate, edit and perfect messages before making them public. This efficiency is further enhanced by providing users with the ability to scroll back and consult the conversational history, which allows users to refer directly to the previous context by resending, quoting or responding to specific messages. Crucially, this functionality makes the communication more robust, and is used by interlocutors to identify, signal and recover from miscommunication (Clark,1996). Moreover, unlike face-to-face interaction, where people can only participate in a single conversation at a time, removing the pressure to respond immediately enables users to engage quasi-simultaneously in multiple conversations; when (re)joining a conversation, users can rapidly read through the most recent messages to "catch up" and respond appropriately.
With the affordances of instant messaging now well understood, there is an urgent need to investigate how to provide such functionality in XR. Arguably the biggest conceptual and technological hurdle is that, unlike standard platforms which are designed to support the exchange of simple messages (e.g. text, audio, video), in asynchronous XR, the transmitted "messages" involve a massive causal nexus of data that needs to be reconciled with a potentially incommensurable causal flow in the current user's reality (Fender,2022). Consider, e.g. distributed design team working asynchronously on different versions of the same artefact: an update by each team consists of a detailed recording of avatars interacting with each other and with the artefacts in the virtual environment. A system supporting such interactions must be able to update and synchronise separate causal flows with ramifications that cannot be generally predicted in advance (Chow, 2019). The most immediate challenge, which is still unsolved, is how to provide users with the ability to seamlessly and collaboratively view, edit and reconcile multiple past histories of interactions within the interaction itself.
The aim of this project is to build and evaluate an asynchronous XR platform which will address three interrelated challenges:
Supervisor: Dr Gregory Mills firstname.lastname@example.org
Technological firms are regarded as key for national and regional economic development. Today, more than ever before, the business environment is burgeoning with innovation. Simply Googling for apps and services to help boost elementary office efficiency, returns over 2,000 items.
In an organisation, implementing a beneficial innovation, results in benefit "I" minus costs "C", but implementing a ‘bad' innovation is very costly, (i.e. minus [I+C]). Without spillover and without hierarchy in an organisation that inhabits an environment that contain many ‘bad' innovations; fads and fashions can circulate unchecked, and these detrimental ideas can swarm into the organisation and resemble epidemics, harming the organisation. Adding the simplest form of hierarchy, one manager who makes decisions about implementing innovations by flipping a coin, means the loss is halved compared to the flat organisation. However, successful high-tech firms are paradoxically well-known for being flat organisations.
This project investigates this important enigma because SMEs and other start-ups are major sources of innovation and employment. With intensified competition and globalisation, the imminent prospect of recession, national and regional Governments are increasingly looking towards being able to create high-tech ‘knowledge ecosystems' to promote innovation and increase financial growth.
This work will model factors leading to success or failure of tech firms and their clusters, Science and Technology Parks, using a combination of classical techniques like GIS, mining standard open source (e.g. ONS) data and commercial business databases. It will rely largely on econometric techniques including SEM, Markov Chain and kinetic Monte Carlo methods, using Python and R, for example.
Supervisor: Dr Jay Kiruthika email@example.com
Neuromorphic vision sensors enable the acquisition of scenes with limited energy requirements and low data rate, only acquiring information on what changes in the scene, with a behaviour similar to the one of the human eye. The acquired information can be easily exchanged in internet of things scenarios where battery-operated visual sensors are connected to monitor a scene or perform a task requiring limited delay (e.g. tele-operated driving).
In the opposite direction, light field capturing and visualisation involves the acquisition of information of a scene from multiple angles / with multiple microlenses and the rendered content is visualised as holographic content, resulting in a truly immersive experience, but also huge data rates.
The project will study compression methods for the data acquired with such methodologies, as well as subjective and task-based criteria for quality assessment of the compressed information. The latter task may leverage machine learning strategies, hence a background in the areas of machine learning is desirable. Also, edge-computing supported compression and processing strategies will be addressed. Data compression at smart edge/fog-based gateways is beneficial, as data compression can be too computationally intensive to be performed on the acquisition sensor/location.
The possibility to acquire information from multiple neuromorphic vision sensors will also be considered, as well as application in real life scenarios (e.g. smart cities, healthcare, gaming).
Collaboration with national and international partners (research bodies/academy/industry) is expected, as well as the possibility of an industrial internship during the PhD.
The project will benefit from the state-of-the-art equipment available in our Centre for Augmented and Virtual Environments (CAVE lab) including neuromorphic vision sensors, plenoptic camera for the acquisition of light fields, autostereoscopic and light field displays.
Candidates are expected to have at least a BSc 2:1 in a relevant area (computer science, engineering, statistics, mathematics) and possibly an MSc in a related area.
Supervisor: Professor Maria Martini firstname.lastname@example.org
With the development of deep learning approaches and convolutional neural networks (CNN) in particular, the task of recognising objects from an image has become associated with the ability to train a network using a large number of labelled images for each class of interest [He2016].
The creation of large, labelled datasets such as ImageNet [Ru2015] has permitted the development of ever-more-performing architectures. In order to extend further the number of classes to which objects can be recognised without requiring to have access to a large number of labelled images of the new classes, few-shot learning (FSL) and even one short learning were proposed: in such frameworks, the initial training set is completed with a support set containing only a few images to define each additional object class [Sung2018]. Eventually, reliance on training images for new classes has been totally removed; instead, textual attributes proved sufficient. By learning a mapping between object images and textual attributes using a large training set, an object from a new class only defined by its textual attributes could be recognised.
While initially zero-shot learning (ZSL) focused on identifying images belonging to the unseen classes of interest, generalized ZSL offers predictions for both the seen and unseen classes [Ji2021]. Although textual attributes can be retrieved automatically from the known labels using resources such as word2vec [Ch17] and Wikipedia, ZSL is essentially a retrieval problem as labels of all classes of interest must be known. Unfortunately, such constraint can only be met in a limited number of scenarios, such as those associated to existing specialised datasets dedicated to specific applications, e.g. 'bird watching' [Wa11]. Here, it proposed to replace this constraint by a less limiting one, i.e., access to an internet connection (or at least an electronic copy of an encyclopaedia). In such scenario, in principle, a picture of any not only unseen, but also unidentified object can be processed and labelled. For example, if a system were able to characterise an unidentified object in an image as ‘a beaver with a duck beak', a simple query using one's favourite search engine would be sufficient to label the object as a 'platypus'.
The aim of the project is to develop an efficient deep learning-based pipeline allowing the annotation of any object present on an image without prior knowledge by the system of their existence. Taking advantage of extracted visual features to create an Unidentified Featured Object (UFO), and existing mapping between visual and textual features, natural language processing techniques can then be applied to generate a list of putative annotations. Eventually, they can be analysed using an FSL-based framework to identify the most likely label.
Applicants should have, at least, an honours degree at 2.1 or above (or equivalent) in Computer Science or related disciplines. In addition, they should have a good mathematical background, excellent programming skills in Python, and an interest in machine learning.
Qualified applicants are strongly encouraged to contact informally the lead academic, Professor Nebel (J.Nebel@kingston.ac.uk), to discuss their application.
Project supervisor: Professor JC Nebel email@example.com
Aircraft research is in a state of transition, with a much broader range of possible new aircraft technologies under investigation than ever before. New technologies include boundary layer ingestion, hydrogen fuelled aircraft, electrically powered aircraft and distributed propulsion schemes [IATA Technology Roadmap to 2050 (2019)]. Most of these are driven by the imperative to reduce carbon emissions in order to reduce, and indeed reverse, the growth of the carbon footprint produced by the increasing number of air miles flown each year [IATA 20 year passenger forecast (2019)].
This imperative has reinvigorated the notion of the use of ballonets to produce lift in aircraft, primarily in assisting craft to reach their cruising altitude, and considerable research interest in this area has been generated [Zhu et al (2021), Wang et al (2021)]. It is estimated that up to 20% of the total fuel consumption for a short-haul flight is consumed in reaching the required cruising altitude alone [see, for instance, the Quora reference below], and at the moment the energy content of the fuel is completely lost. This project proposes to investigate the use of ballonets to assist in fuel reduction by production of buoyant lift, by modelling a range of possible configurations and the use of a mix of potential gases. A number of possible configurations will be investigated, such as the use of fixed or inflatable elements, along with potential energy recovery in descent by re-pressurisation of the buoyant gases.
This project will require a sound understanding of physical principles, especially those relating to flight and buoyancy, as well as good modelling skills, It would particularly suit a graduate in Physics, Aerospace or Aeronautical Engineering, or an equivalent area. Detailed background knowledge in this area is not required, but enthusiasm, flexibility and a drive to make progress are essential.
For further information, please contact Professor Augousti via firstname.lastname@example.org
Supervisor: Professor Andy Augousti email@example.com
The thermal protection system (TPS) is a critical component of any rocket, responsible for protecting the vehicle and its payload from the extreme temperatures and heat loads encountered during flight. The nozzle, in particular, is subjected to some of the highest temperatures and heat loads, as it is the point through which the hot gases produced by the rocket's engines are expelled. It is therefore essential that the TPS for the nozzle be designed and implemented effectively to ensure the integrity and reliability of the rocket. The primary criteria of a TPS material are to survive high heat flux without getting excessively degraded.
This study investigates different advanced coating materials which have the capability to minimise temperature and heat flux reaching to the substrate and the ability to withstand high compressive stress and strain. Selection of materials for coating and the optimisation of coating thickness will be based on the change in temperature, von-mises stress/strain and flux. A combination of simulation procedures such as Ansys CFX for fluid simulation, Ansys ACP for composite nozzle design, Ansys Steady State thermal for thermal analysis and Ansys static structural for structural analysis which provides detail review and critical analysis of different coating materials over the substrate will be used.
The research will be supported by literature review, computational simulations (CFD, FEA) and comparison of varying thickness of materials based on optimisation functions.
Supervisor: Dr Doni Daniel firstname.lastname@example.org
In order to tackle the triple planetary crisis of climate change, air pollution and biodiversity loss, the current linear economy, which adheres to a take-make-use-dispose philosophy, is being transformed into a circular economy, aims to make more effective use of resources and thus creating a zero waste and emission ecosystem. The Ellen MacArthur Foundation characterises circular economy as "an economy that is restorative and regenerative by design and aims to keep products, components, and materials at their highest utility and value at all times". Therefore, it is evident that design is recognised as a key enabler to support the transition to a circular economy. Design can be seen as a problem-solving process that finds viable solutions to fulfil specified requirements. It normally exists in the form of iterative process models such as the Systematic Design Approach, Total Design and Axiomatic Design. Institutions and organisations have their variations, but they all involve essential early design activities such as opportunity identification, idea generation and concept development. Decisions made at these stages are unquestionably critical to the whole process as changes are more difficult and costlier to make downstream. As a result, integrating a circular economy at the early design stages is critical to achieving a circular design. Despite the methods and tools developed to promote circular design such as eco-design, green design and design for sustainability, research indicates that there is still a research gap in product design from a circular perspective, especially in the early stages, resulting in unsustainable product designs. For example, the life cycle assessment evaluates the environmental impact of a product but can only be carried out at the late stages of design when detailed layout, material and components are determined. Another example is circular indicators which have been developed to support companies to assess the circularity of business but are only capable of providing measures rather than solutions. Methods and tools that support the systematic integration of product design and circular design are still under development and can have unintended consequences effects if not considered from a whole system perspective. Research also highlights issues with regard to compartmentalised knowledge required for circular design and the challenge to transfer and integrate such cross-disciplinary knowledge by designers.
This PhD aims to establish an interactive knowledge-based tool to promote circular design at the early development stages, by integrating cross-disciplinary knowledge from various types of sources, offering preliminary circular design analysis and suggestions for the designers to consider. This knowledge-based system is enabled by a concept mapping tool which will be developed based on open-access databases such as Google Patents and Kickstarter. Designers can then obtain circular design insight for any idea and hence inform a better circular design.
This tool will, later on, be embedded into an open-source product lifecycle management (PLM) software to enable an integrated environment to promote circular design and help designers make circular decisions from the beginning of the development process.
Supervisor: Dr Pingfei Jiang email@example.com
There is an increasing urgency to understand the biochemical and physiological responses of plants to global environmental change. This is because environmental stressors––climate and land-use change, environmental pollution (e.g., nitrogen deposition) experience in the vicinity of urban areas––directly influence species' survival, growth and reproduction, which in turn determine community structure and function, as well as ecosystem processes. In effect, environmental stressors drive permanent, long-term changes in ecosystems. Equally important, plant functional traits can be altered as plants adapt (acclimate) to environmental constraints, and therefore, variation in plant responses and tolerances to environmental stressors can be captured by directly measuring fluctuation in the most relevant functional traits. However, retrieving rapid, reliable, and repeatable measurement of key plant traits indicative of plant stress has proven challenging. It is within this framework that we will investigate climate-driven environmental stressors using hyperspectral (HS) leaf reflectance to better understand plant responses to drivers of environmental change.
The project will focus on observations collected near urban and peri-urban landscapes, where ecosystems are continuously exposed to multiple environmental stressors, including climatic abnormalities (e.g. high sunlight radiation, extreme temperatures, highly variable water inputs) and high levels of ozone and other air pollutants (Tausz et al. 2007, Bussotti 2008, Paoletti et al. 2010, Sharma et al. 2012). These environmental conditions can induce plant oxidative stress because of increased production of reactive oxygen in plant cells. Plants exhibit species-specific tolerances against such oxidative injury, which is modulated by their efficiency at mobilizing antioxidant defences (i.e., balancing pro-oxidant and antioxidant levels). Recent studies have established that both Non-enzymatic (e.g. carotenoids, ascorbic acid and glutathione) and enzymatic substances (e.g. superoxide dismutase, catalase, ascorbate peroxidase and glutathione reductase) are important antioxidants, and can therefore be used to characterise plant susceptibility to natural and anthropogenic oxidative stress (Iriti and Faoro 2008, Foyer and Noctor 2011, Foyer and Shigeoka 2011). Additionally, other leaf compounds, such as the levels of chlorophyll or hydroperoxide, may also help characterise plant susceptibility to oxidative reactions (Gratao et al. 2012). Equally important, plant functional traits can be altered as plants acclimate to environmental constraints and their responses to environmental stressors can be captured by directly measuring fluctuation in plant functional traits, such as biochemical leaf traits. This project will investigate species-level physiological tolerances to environmental stressors and up-scale to community -and- ecosystem-scales. We will focus on biochemical leaf and root traits that are ideal for measuring plant tolerance to (against) environmental stressors. The project involves multidisciplinary work including acquisition of HS data, exploration of the spatial distribution of the environmental stressors in relation to urban areas, and application of artificial intelligence and spectral imaging techniques to create predictive models.
First supervisor: Dr Olga Duran O.Duran@kingston.ac.uk
Throughout the history of nature conservation there have been major conflicts of interest between different stakeholders, for instance, between national governments and local communities. These conflicts have been most profound and visible for international conservation interventions, such as establishing protected areas in Lesser Developed Countries. However, with the recent focus on implementing nature-based solutions (NbS) in urban and peri-urban landscapes, the fraught nature of conservation interventions is increasingly being felt in urban areas. Certain stakeholders perceive NbS as 'messy', unkempt, and unmanaged spaces, while local authorities view them as vital for mitigating effects of anthropogenic climate change (ACC) in urban and peri-urban areas, e.g. planting urban forests, creating urban green -and- blue-spaces and re-wilding initiatives). NbS projects are thought to maximise delivery of ecosystem services, improving everything in urban landscapes ranging from health and well-being and carbon sequestration to biodiversity and flood mitigation. The benefits of NbS are seen by certain stakeholders as an acceptable solution to attenuate the negative impacts of urbanisation on natural habitats by safeguarding some of the benefits of nature in urban spaces. However, NBS projects are far-removed from traditional manicured and managed urban landscapes, wherein urban gardens and forests, or re-wilding zones, come into direct conflict with citizens' preferences and perception of what is acceptable in their neighbourhood.
It is within this framework that the project will contribute to address a fundamental knowledge gap: what factors shape citizens' perceptions, attitudes, and behaviour toward NbS in urban and peri-urban environments? The primary aim of the project will be to investigate barriers to implementing nature-based solutions for climate adaptation in urban and peri-urban environments. A complimentary aim is to investigate whether ecosystem services provisioned by NbS are aligned with people's perception of the NbS. The programme of research will take an interdisciplinary approach, integrating social science (e.g. contingency choice experiments) with ecology (e.g. biodiversity and ecosystem function) to get a comprehensive understanding of climate adaptation and mitigation policy adapted vis-a-vis NbS initiatives. The studentship will allow a PhD candidate to join a team of researchers and contribute to the aims of this ambitious research programme.
Supervisor: Dr Kerry Brown firstname.lastname@example.org
Nowadays, in electric cars, batteries contribute a large part of a vehicle's weight, which means additional foot printing and load-bearing function. From the other hand an integrated structural battery and supercapacitor system, is one that works with a double function as the power source and as part of the structure a car/drone/plane body. This is a "massless" energy storage, because in essence the battery's weight vanishes when it becomes part of the load-bearing structure. Calculations show that this type of multifunctional battery could greatly reduce the weight of an electric vehicle or other mobile application significantly. Recent development of structural batteries at Chalmers University of Technology has proceeded through many years of research, including previous discoveries involving certain types of carbon fibre. This work was named by Physics World as one of 2018's ten biggest scientific breakthroughs in our times.
Different approaches to improve battery run-time with supercapacitors has been done in the past decade and a series of papers was presented. Looking ahead at consumer behaviour it is conceivable that the next generation of electric cars, electric planes and satellites and intelligent houses will be powered by such structural composite systems.
The aim of this project is to generate different composites parts able to act like the power source battery function and have a powered electrical contribution.
First supervisor: Dr Spiros Koutsonas S.Koutsonas@kingston.ac.uk
The main goal for the next generation of the aircraft industry is to continue improving both the economical aspect and the impact on the environment. Although the annual passenger number is expected to increase to 6.4 billion in 2030 in comparison to the number in 2013 [Statista, 2019] the aero industry is expected to reduce the fuel burn by 50% by 2050 in comparison to the value in 2005 [CAP 1524, Dikson, 2015]. To be able to achieve these, numerous aircraft and engine manufacturers have invested billions of dollars in advanced technology research and development for the sustainable future of aviation. For example, completely new aircraft designs, such as the Airbus A380, A350 XWB and the Boeing 787 (the only commercial aircraft with an electro-thermal wing anti-icing system); updated versions of existing aircraft, such as the 737MAX, A320neo, A330neo and Boeing 747-8. On average, each new generation of aircraft is roughly 15% to 20% more efficient than the previous generation [Crippa et al., 2019].
One area of focus in these investments include developing new and more efficient de-icing systems compatible with composite airframe structures of the next generation. This includes, among others, heater mat technology, intelligent IPS and icephobic coatings [Shinkafi et al., 2014]. Charpin and Verdin (2010) studied the challenges of anti-icing and de-icing models and outlined the significance of advancement for these models. They discovered that one of the effective methods of de-icing is to use electro-thermal elements placed on the aerofoil which would destroy the ice adhesion. It was concluded that existing models have potential of much more advancement and development. I addition it was outlined that maximum efficiency can be achieved through optimisation of geometries and positions of the heating pads.
Icing (for an aircraft in fight) refers to the accretion of supercooled water onto an airframe during flight. Water droplets with no particles around in the cloud could stay supercooled with temperature of about -40°C. When an airplane (with the surface temperature below freezing) flies through this type of cloud, the droplets hit on the aircraft surface and freeze. As ice keeps accreting, this will change the profile of the aerofoils which will significantly affect the aerodynamics performance, resulting into reduction in the lift force and increase in drag for a given angle of attack and hence lead to catastrophic failure of the aircraft. Similarly, ice on a propeller blade reduces the efficiency of the propeller and reduces thrust. (EGAST, 2015). Therefore, it is significantly important to use appropriate icing protection system (IPS) to avoid the ice accreting. There are mainly two types of IPS, these include anti-icing systems which prevent ice from forming and de-icing systems which remove ice after it is formed. The type of IPS depends on the energy available from the aircraft and the ice protection strategy.
This is a computational project which will provide optimisation study of electro-thermal icing protection system (IPS) in modern aircraft. The main aim is to optimise the distribution and layout of the heating material or elements in multi-layer composite structure of the electro-thermal IPS by using an efficient, reliable and practical topology method in order to reduce energy consumption. This will improve the aircraft performance and reduces the fuel burn which not only improves the impact on the environment but also improve the economic aspect and the cost to the airliners as well.
Supervisor: Dr Zaineb Saleh email@example.com
Innovative technology development for next-generation aviation transport is demanding due to pressing concerns in terms of associated environmental impacts and the eco-sustainability. Smart structures technology, particularly the bio-inspired morphing and multifunctional wing architectures are among the most promising technologies for the improvement of aerodynamic performance in large civil aircraft. The controlled adaptation of the wing shape to external operative conditions naturally enables the maximisation of aircraft aerodynamic efficiency within the full flight envelope accompanied by significant reduction of fuel consumption and pollutant emissions.
The morphing wing design has exhibited significant influence on aircraft performance, such as aerodynamic, aeroacoustic, and aeroelastic characteristics. It is of fundamental importance to understand fully the underlying mechanisms of these characteristics in order to optimise morphing structures design and to accelerate their technical maturity. However, the aerodynamic characteristics of morphing structures are still unclear due to the lack of in-depth research.
Therefore, the proposed project aims to discover fundamental flow physics and the associated aerodynamic characteristics of a morphing wing configuration at static and dynamic morphing conditions. More specifically, a variable camber morphing configuration will be considered in the design, an advanced scale-resolving turbulence modelling technique will be developed to perform a thorough analysis on the static/dynamic morphing characteristics. The resulting research data could be used for further aeroacoustic and aeroelastic characteristics investigations. The research outcome will contribute to the development of smart structures technology for sustainable aviation, wind energy and emerging transport platforms development, such as morphing-wing UAVs
This project will require a sound understanding of aircraft aerodynamics, computational technologies (CFD), and good modelling skills. It would particularly suit a graduate in Aerospace Engineering, Mechanical Engineering or equivalent areas. Detailed background knowledge on morphing wing is not required, but enthusiasm, flexibility and motivation to success are essential.
Supervisor: Dr Yujing Lin firstname.lastname@example.org