Cranfield University, England invites online Application for number of Fully Funded PhD Degree at various Departments. We are providing a list of Fully Funded PhD Programs available at Cranfield University, England.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: Advanced Aerodynamic and Thermal Management Concepts for Environmental Friendly Aircraft with GKN
The research will focus on aerodynamics and innovative aircraft concepts that will contribute to the development of sustainable and green aerodynamic solutions for aviation. It will combine elements of whole aircraft aerodynamic analysis, heat transfer and aircraft performance.
The researcher will develop and apply energy based aerodynamic analysis methods to research and analyse the effect of using the heat generated by novel power and propulsion systems on the aerodynamic performance of the aircraft.
The researcher will join a diverse and inclusive team at Cranfield University that focuses on the development, integration and evaluation of novel aircraft, propulsion and aerodynamic technologies. The team has developed novel, world class energy and exergy based aerodynamic methods and the researcher will have the opportunity to further develop and apply them.
Deadline : 31 Jul 2024
(02) PhD Degree – Fully Funded
PhD position summary/title: Breaking the salt barrier: catalysts for efficient seawater electrolysis PhD
A viable path toward attaining energy sustainability is the production of green hydrogen using renewable energy sources. Nonetheless, conventional water electrolysis technologies predominantly rely on freshwater, exacerbating strain on already limited resources. Seawater, as a vast and natural electrolyte source, provides an alternative but poses significant challenges, including complex ionic chemistry, insoluble by-products formation, corrosion-related issues, and chlorine evolution/oxidation reactions. Crucially, improving energy efficiency is imperative to mitigate the overall cost of hydrogen production from seawater.
Addressing these challenges involves developing high-performance electrocatalysts tailored for seawater electrolysis to achieve industrial-level oxygen evolution reaction (OER) current density below the potential at which competitive chlorine evolution reactions occur. Among potential candidates, transition-metal nitrides (TMNs) stand out as promising electrocatalysts due to their electron configurations, high electrical conductivities, corrosion resistance, and robust mechanical properties. Moreover, molybdenum (Mo) surfaces serve as electron pumps or reservoirs, modulating the electronic states of TMNs and enhancing catalytic activity through charge transfer. Additionally, ultra-thin graphitic carbon coatings can provide excellent conductivity and protective layers.
Therefore, our research project aims to resolve the competition between the oxygen evolution reaction (OER) and other side reactions by pioneering the development of innovative high-performance Mo/TMNs electrocatalyst, with ultra-thin graphitic carbon coating.
Deadline : 29 May 2024
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(03) PhD Degree – Fully Funded
PhD position summary/title: Combinatorial artificial intelligence for defence applications PhD
Combinatory Artificial Intelligence (also known as Third Wave AI as initially described by DARPA) is the term that references the next foreseen advances within Artificial Intelligence. This stems from the two main styles of AI development over the last two decades.
‘First Wave AI’ is used to describe the rules/logic based AI used heavily in the 1990’s and 2000’s and still in wide use today. This involves ‘handcrafted’ expert systems, which are good at reasoning about narrowly defined problems, but poor at handling uncertainty and have no ability to learn or abstract/generalise. In that sense, these systems serve as complex functional approximators trained over an input-output data set.
‘Second Wave AI’ is the term used to describe the current glut of ‘machine learning’ style intelligence, where algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include ‘supervised learning’ approaches (such as Deep CNN’s) and ‘unsupervised learning’ approaches (such as reinforcement based learning and generative adversarial networks). Some of the main problems with Second Wave AI are ‘explainability’ and trust – as the machines learn, they are based upon statistical outcomes on large data sets, rather than human intuitive information. Another problem lies with the fragility of the systems, ‘illogical’ outcomes can sometimes be generated due to biases, gaps or pollution of the training sets. They typically lack the ability to generalise and to reason beyond what it has been trained over.
Deadline : 22 May 2024
(04) PhD Degree – Fully Funded
PhD position summary/title: Developing Ontologies for Digital Engineering and Manufacturing PhD
The goal of an ontology is to enable knowledge sharing and reuse by means of a definitive classification of entities in specific domains constructed on the basis of a controlled vocabulary with logical definitions of its terms. This approach will semantically enhance the data enabling interoperability across a system of systems environment. For ontologies, a significant aspect is a hierarchical classification of ontologies based on their level of abstraction.
Deadline : 31 Jul 2024
(05) PhD Degree – Fully Funded
PhD position summary/title: Development of a Design Synthesis Methodology Appropriate to the Conceptual Design of 6th Generation Combat Aircraft PhD
The existing tool makes use of various interacting modules, operating at differing levels of fidelity, each responsible for a specific design aspect (e.g., Aerodynamics, Mass and Balance, Performance etc.). By manipulating input and other control parameters, one can obtain a broad range of aircraft configurations and results. This method should allow the development of a conceptual design tool tailored for designing future combat aircraft, while also allowing extensive parametrisation of design features and flexibility, thus providing an excellent tool for cost-effective trade or validation studies. Its modular nature may also allow additional features, to be studied and their effect on the overall design and performance to be established, effectively and quickly.
This project will focus on the 6th generation combat aircraft and will likely consider features such as directed energy weapon systems, low observable technologies in addition to considering both crewed and de-crewed concepts.
Deadline : 08 May 2024
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(06) PhD Degree – Fully Funded
PhD position summary/title: Enhancing hydrogen flow metering performance PhD
Deadline : 29 May 2024
(07) PhD Degree – Fully Funded
PhD position summary/title: Extreme learning to handle ‘Big Data’ PhD
As aerospace platforms go through their service life, gradual performance degradations and unwarranted system failures can occur. There is certain physical information known a priori in such aerospace platform operations. The main research hypothesis to be tested in this research is that it should be possible to significantly improve the performance of extreme learning and assure safe and reliable maintenance operation by integrating this prior knowledge into the learning mechanism.
The integrating should enable to guarantee certain properties of the learned functions, while keep leveraging the strength of the data-driven modelling. Most of, if not all, the traditional statistical methods are not suitable for big data due to their certain characteristics: heterogeneity, statistical biases, noise accumulations, spurious correlation, and incidental endogeneity. Therefore, big data demands new statistical thinking and methods. As data size increases, each feature and parameter also becomes highly correlated. Then, their relations get highly complicated too and hidden patterns of big data might not be possible to be captured by traditional modelling approaches.
Deadline : Open until filled
(08) PhD Degree – Fully Funded
PhD position summary/title: Making Lo-Carbon Fuels Sustainable: Biohydrogen and Biomethanol production via Hydrothermal Gasification PhD
Switching to low-carbon fuels is one of the key building blocks of energy decarbonisation and transition towards the net-zero target. In collaboration with Purifier Labs (PL), this project aims to combine experimental and process simulation approaches to develop an innovative biohydrogen and biomethanol synthesis route based on PL’s hydrothermal gasification process. Methanol is the feedstock for many industries, which is mainly produced from fossil fuel sources, thus, is associated with a high carbon footprint. Accordingly, there is an urge to find sustainable and clean alternatives for the production of methanol. The proposed approach in this project is innovative and offers the production of biogenic methanol, which has a significantly lower carbon footprint and can particularly be used for the production of sustainable marine and aviation fuels. PL’s hydrothermal gasification enables the production of syngas, required for methanol production, from wet feedstock, therefore eliminating the need for drying feedstock, which is a significant challenge in conventional syngas production routes. The main challenge to address is to optimise syngas composition (hydrogen-to-carbon dioxide ratio) and to minimise carbon monoxide content that adversely affects catalysts during methanol production.
Accordingly, the successful candidates will first investigate the kinetics of syngas production at elevated pressures within PL’s hydrothermal gasification process to optimise the syngas content. Subsequently, the syngas will be used within the catalytic processes to synthesise methanol, the main objective of which will focus on achieving an optimal production yield. In addition, the process simulation models will be developed based on the experimental data to validate the models and optimise the entire process.
Deadline : 29 May 2024
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(09) PhD Degree – Fully Funded
PhD position summary/title: Multi-dimensional Optimal Order Detection techniques for Arbitrary Lagrangian Eulerian (schemes) for compressible flows PhD
This exciting research seeks to evolve the forefront of computational fluid dynamics (CFD) methods for compressible flows in the emerging Exascale High-Performance Computing (HPC) landscape. The aim is to achieve high-order of accuracy but also to obtain physically meaningful results that can advance the landscape of engineering simulations.
The choice of the best numerical method, governing equations, and framework is far from obvious and unique. Employing a MOOD-style algorithm that continuously scrutinises every facet of the numerical solution, ensuring adherence to both physical and numerical admissible criteria. This algorithm unlocks several possibilities and enables high-accuracy and computational efficiency across single- and multi-physics simulations.
However for Arbitrary Lagrangian Eulerian (ALE) frameworks, conservation errors, dissipation errors in regions of low-mesh quality, and carbuncle are just some of the issues that persist. The primary goal of this project is to advance the MOOD techniques for ALE frameworks by establishing new metrics that can accurately capture conservation errors during mesh deformation, bypassing/minimising the carbuncle phenomenon, establishing new physical and numerical admissible detection criteria for ALE, evaluate any potential benefits of high-order methods in the ALE context, and advance an available open source CFD software with the newly formulated algorithms/methods/frameworks.
Deadline : 19 Jun 2024
(10) PhD Degree – Fully Funded
PhD position summary/title: Novel Methods for the Characterisation of Helicopter Aero-acoustics PhD
The proposed project aims to build a Reduced Order Model (ROM) capable of synthesising the noise hemispheres as functions of pertinent vehicle design parameters and operating conditions. A novel computational framework will be developed for helicopter noise hemisphere generation by integrating a series of validated Cranfield tools for rotor aero-mechanics and acoustics. A non-linear “free-wake” rotor model will be adapted to capture Blade Vortex Interaction (BVI) noise. This model will be extended to resolve the simultaneous evolution of the main and tail-rotor flow-fields, including the impact of the fuselage on the potential flow field. The acoustic model will be modified to be able to predict noise-hemispheres based on the combined free-wake flow solution of the main and tail rotor flows. This framework will be used to generate series of databases of noise hemispheres for a range of rotor architectures as functions of design parameters and operating conditions. Latin Hypercube Sampling (LHS) or Full Factorial (FF) sampling methods will be used for the Design of Experiments (DOE) to discretise the design space. State-of-the-art ROM approaches will be applied to analyse the noise data-bases, such as Proper Orthogonal Decomposition (POD) and dimensionality reduction (auto-encoders, non-linear Principal Component Analysis – PCA) to extract key aero-acoustic features. Surrogate modelling methods such as Gaussian Processes (Kriging) or Artificial Neural Networks (ANN), will be applied to derive analytical approximations of the scalar POD coefficients. The developed ROMs will be transferred to DSTL in the form of interpretable and generalizable aero-acoustic models using an agreed software format. This will be carried out through a series of dedicated student placements to facilitate the integration of the derived ROMs, as well as their application to a series of cases studies according to Dstl’s interest.
Deadline : 26 Jun 2024
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(11) PhD Degree – Fully Funded
PhD position summary/title: Optimise and automate pre-production for wire based Directed Energy Deposition (w-DEDAM) production PhD
Wire-based directed energy deposition additive manufacturing (w-DEDAM) systems have effectively constructed qualified parts, now extensively employed in many industrial applications. To ensure a stable, reliable, high-quality and environmentally sustainable deposition process, the pre-production process is crucial which includes multiple activities, in terms of pre-forming original Computer Aided Design (CAD) models, recognising and segmenting design features, simulating geometry and mechanical properties, defining build sequences, and planning paths with appropriate process parameters.
Currently, the entire pre-production process is heavily reliant on the expertise and experience of additive manufacturing (AM) engineers. The decisions have also been decided based on prior experience, which may result in various part quality, lead time, and the use of material. This current artificial process is also time-consuming and fraught with uncertainties, often prone to human errors during decision-making. Therefore, there is an urgent need to fully optimise and automate this pre-production process with the combination of expert knowledge and artificial intelligence (AI) driven digital tools.
This project aims to explore and discover a non-expert pre-production process for w-DEDAM which can be implemented automatically based on expert knowledge and AI-driven digital tools combined with multi-objective optimisation. It will routinely provide an optimal production solution in terms of productivity, minimal or no distortion and high quality.
Deadline : 26 Jun 2024
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(12) PhD Degree – Fully Funded
PhD position summary/title: Production of hydrogen and sustainable fuels in solid oxide electrolysis cell PhD
Deadline : 29 May 2024
(13) PhD Degree – Fully Funded
PhD position summary/title: Three-dimensional hybrid composites for repair and recycling PhD
This project simultaneously addresses the challenges of autonomous repairability and end-of-life recyclability by using through-thickness reinforcement to embed large-diameter (>1 mm) metallic hybridising elements into composite laminates. These novel through-thickness elements will be used as a means of targeted heat introduction to activate re-processible polymer matrices including thermoplastic or vitrimer-based resins. This intrinsic self-healing negates the need for external heating apparatus like hot presses or ovens to melt the composite’s matrix. The academic knowledge gap exists in understanding the thermo-mechanical interaction between metallic TT-elements and composite constituents which is foundational and necessary for exploiting this technology for repair and recycling purposes.
The Composites and Advanced Materials Centre at Cranfield University has world-class facilities to support through-thickness reinforcement activity and functional/mechanical characterisation of materials including an electrical, thermal, and thermo-mechanical characterisation suite, pilot scale composites manufacturing equipment including a tufting robot, and z-pinning gantry as well as the mechanical testing lab in the School of Aerospace, Transport and Manufacturing. This project supports a Royal Academy of Engineering Research Fellowship project entitled Multifunctional z-direction hybridisation of composites (web link: https://www.cranfield.ac.uk/research-projects/multifunctional-z-direction-hybridisation-of-composites).
The activities in this project will cover manufacturing process development and manufacture of hybridised components, optimisation of material parameters through finite-element simulation, testing and validation of functionality and performance. This will represent an industrial paradigm shift in the use of through-thickness reinforcement in composites, expanding this potential to recycling and repair. Increased longevity and durability of the structures associated with the solution will affect a savings on maintenance, repair, and replacement.
Deadline : 22 May 2024
(14) PhD Degree – Fully Funded
PhD position summary/title: Understanding pathogen reduction mechanism(s) in vermifilter ‘Tiger’ toilets – PhD
Thousands of vermiform-based wastewater treatment and sanitation systems have been installed primarily in the global south. This PhD will aim to provide a fundamental understanding of the pathogen reduction mechanisms in vermifilter technology. The student will embark on an exhilarating exploration to unravel the dynamic role of Tiger worms in the decomposition of particulate organics. This exploration will delve into the transformative effects these organics have on the compost ecosystem. The investigation will also spotlight the pivotal function these substances play in the enzymatic degradation of complex polymeric particulate faecal materials. The expected impact of the research project will be improved understanding and performance of vermiform-based wastewater treatment and sanitation systems.
A successful applicant will benefit from skills training in the following areas: biological engineering reactor design, molecular microbiology, molecular ecology and bioinformatics. You will also have the opportunity to attend conferences.
Deadline :22 May 2024
(15) PhD Degree – Fully Funded
PhD position summary/title: Understanding the Past, Securing the Future: Advanced techniques for the detection of chemical hazards in Archive Repositories through Heritage Science
The PhD holder will adapt and optimise the handheld CRIM-TRACK sniffer sensor, currently able to detect vapours of illicit substances, to a new detection scenario of historical pesticides within archives. The investigation will also be extended to other analytical techniques. Application of the novel device to TNA’s large collection (and beyond) will inform variability in worldwide historic archival practices and inform current storage and access regimes.
By intertwining engineering, chemistry and history, the supervision will ensure a comprehensive interdisciplinary research experience for the PhD student. The project will also provide a novel methodology for detecting hazardous pesticides with important benefits and impact to both academic and professional audiences, in the UK and overseas.
It will be jointly supervised by Dr Licia Dossi at Cranfield University and Dr Marc Vermeulen at The National Archives. The student will be expected to spend time at both Cranfield University and The National Archives.
Deadline : 26 Jun 2024
(16) PhD Degree – Fully Funded
PhD position summary/title: Wave devouring propulsion for marine decarbonisation PhD
Deadline : 28 Aug 2024
About Cranfield University, England – Official Website
Cranfield University is a British postgraduate-only public research university specialising in science, engineering, design, technology and management. Cranfield was founded as the College of Aeronautics (CoA) in 1946. Through the 1950s and 1960s, the development of aircraft research led to growth and diversification into other areas such as manufacturing and management, and in 1967, to the founding of the Cranfield School of Management. In 1969, the College of Aeronautics was renamed the Cranfield Institute of Technology, was incorporated by royal charter, gained degree awarding powers, and became a university. In 1993, it adopted its current name.
Cranfield University has two campuses: the main campus is at Cranfield, Bedfordshire, and the second is at the Defence Academy of the United Kingdom at Shrivenham, southwest Oxfordshire. The main campus is unique in the United Kingdom (and Europe) for having its own airport – Cranfield Airport – and its own aircraft, used for teaching and research.
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