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10 PhD Degree-Fully Funded at Cranfield University, England

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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: Aero-Engine Nacelle Aerodynamics And Optimisation PhD

This is a full funded PhD (fees and bursary) in propulsion system aerodynamics in collaboration with Rolls-Royce and EPSRC. The focus of the research is on the aerodynamic design and optimisation of aero-engine nacelles and the integration with the airframe. The project will investigate advanced adjoint based optimisation methods for the powerplant design as part of a full engine-airframe configuration. A key focus is on determining optimisation strategies to address various objective functions, design constraints, geometry modifications as well as quantifying the potential fuel burn reduction benefits.

Deadline : 28 Feb 2024

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(02) PhD Degree – Fully Funded

PhD position summary/title: An experimental and computational study for predictive assessment of material failure

Initially, the successful candidate will identify the critical data capture steps for accurate prediction of material failure in macroscale blast assessments. The current test methods will be trialled, benchmarked and optimised, incorporating enhanced novel, dynamic, diagnostic tools to accurately capture failure modes. Fundamental microstructural characterisation techniques will also be used to interrogate the failed material and enhance understanding of failure. This knowledge will be used to parametrise damage/failure models for use in FEA and these models will be used to predict the outcome for a wide range of load conditions against various thicknesses of protection material. The experimental and numerical results will be combined to generate a numerical tool to predict rupture of materials under blast loading. Machine Learning techniques will also be investigated throughout the project to rapidly assess the large experimental and numerical datasets. The student will also receive training in industrial placements from numerical modelling, materials and armour practitioners.

Deadline : Open until filled

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(03) PhD Degree – Fully Funded

PhD position summary/title: Development of Advanced Coatings for Improved Environmental Performance PhD

Global warming and climate change are consequences of greenhouse gases produced by burning fossil fuels. These gases trap heat in the earth’s atmosphere. By reducing these greenhouse gases, net zero emissions may be achieved. It is the United Nations’ (UN) aim to secure global net zero by the year 2050. Phasing out fossil fuels in favour of greener alternatives, such as hydrogen fuel, is therefore imperative. However, deployment of hydrogen as a fuel for power generation still requires significant research and therefore bridging technologies are also required to help achieve the UN aim.

Deadline : Open until filled

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(04) PhD Degree – Fully Funded

PhD position summary/title: Experimental and Numerical Study of Parameters Affecting the Wake Dynamics of a Simplified Car: Effects of Roughness – Turbulence – Yaw Angle

Air quality is a major public health issue worldwide. Two origins can be identified for pollutant particles that emanated from road transport vehicles.The dynamics of exhaust and non-exhaust pollutants released into the atmosphere in the wake of passenger cars are still poorly understood. An enhanced understanding of these physical processes involved in the dispersion of pollutants is essential. The flow topology must be accurately described and analysed while both the flow and the interaction of the flow and particles must be better understood. Our ability to find solutions to either limit the infiltration of pollutants into car cabins (and thus reduce the exposure of the occupants) or to identify areas where they can accumulate (areas to be avoided by pedestrians and cyclists) depends on these issues. This is the context of this PhD project.

Deadline : 24 Apr 2024

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(05) 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.

Deadline : Open until filled

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(06) PhD Degree – Fully Funded

PhD position summary/title: Flow distortion measurements using event-based vision cameras PhD

For future aircraft concepts there is an expectation that new architectures feature closer integration of the propulsion system with the airframe. A key challenge for such configurations will be the more complex aerodynamic characteristics associated with the propulsion system integration. Within that context there is an on-going interest is developing new flow measurement capabilities that for new aero-engine configurations. Previous work within the group at Cranfield has demonstrated the use of stereo PIV methods for a range of aero-engine related topics such as intake ground vortex, distortion ingestion as well as the characterisation of unsteady for complex intake configurations. These have demonstrated the considerable benefits of PIV to provide rich measurements which are unobtainable using conventional methods. For example, the previous research enabled the quantification of the unsteady swirl characteristics of a complex intake along with statistical analysis of the distortion metrics in the temporal and frequency domain. In addition, a new taxonomy of unsteady distortion events was developed whereby the distortions were evaluated in a relative to a fan rotor frame of reference and classified according to their extent, duration, magnitude and likelihood of occurrence. This approach enables the identification of peak instantaneous events and links them with their likelihood to impact the operation of the fan, hence provides guidance on the significance of each distortion class to the fan system. 

The overall aim of this PhD project is to determine the feasibility and potential benefits of using event based cameras for stereo PIV measurements and to characterise the dynamic flow distortion for internal domains such as convoluted intakes. The experiments will initially focus on relatively simple configurations to explore the measurement system capabilities and then develop to more complex configurations where the focus will be on flow distortion. 

Deadline : 17 Apr 2024

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(07) PhD Degree – Fully Funded

PhD position summary/title: Future Aircraft Communications and Data Management for Flight Dynamics Predications PhD

This initiative aims to enhance the reliability and data management of the aviation system by addressing communication gaps between the current and future systems and improving flight dynamics. The existing aviation technology and processes have limitations in managing data exchange and ensuring high-quality service (QoS). Consequently, the project proposes a system that leverages connectivity and flight dynamics, incorporating Artificial Intelligence (AI) and Machine Learning (ML) to develop innovative prediction mechanisms.

The primary goal of this project is to design advanced communication system algorithms that take into account the integration of both terrestrial and non-terrestrial networks. The focus is on exploring the optimization of aircraft communication links by leveraging hybrid networks, including 5G, HF, VHF, satellite, ground-based, and unconventional bearers. The optimization process will be based on considering flight dynamic parameters and manoeuvres, aiming to enhance the overall efficiency and reliability of communication systems in aviation.

Deadline : 20 Mar 2024

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(08) 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 : 13 Mar 2024

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(09) PhD Degree – Fully Funded

PhD position summary/title: Sensing wastewater for real-time public health – PhD

The  exciting PhD opportunity was funded by 5-year Leverhulme Trust Leadership Award, which aims on sensing wastewater for real-time public health, particularly on the development of novel low-cost and rapid sensors for rapid and on-site wastewater surveillance. We are offering two fully funded national PhD studentship covering tuition fees, competitive stipend, research and consumables, and travel for international conference. The candidate will be working with a highly interdisciplinary and international team to develop the advanced sensors technology for wastewater surveillance (wastewater-based epidemiology) to provide new understanding for real-time public health by monitoring of chemical and biological biomarkers in wastewater and advancing sample preparation method for complex matrix. The candidate is welcome to apply with a background or an interest but not limited to biosensors, microfluidics, synthetic biology, analytical chemistry, environment science, molecular biology, microbiology and engineering.

Deadline : 06 Mar 2024

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(10) PhD Degree – Fully Funded

PhD position summary/title: Trust evaluation of autonomous systems through a correlation analysis between brain activity monitoring and micro expressions PhD

The rapid development of automated vehicles offers promising advancements in driver-vehicle interaction and cooperation. Trust is an important concept to consider for the future implementation of autonomous driving. An inappropriate level of trust can lead drivers to either under-trust and reject the system’s potential benefits, or over-trust and abuse the capabilities of the system. Therefore, autonomous vehicles need an appropriate level of trust for drivers to experience the full benefits of autonomous driving.   

This project aims to develop a trust evaluation solution using a combination of existing sensors in vehicles (e.g., cameras), neuroscience sensors, and artificial intelligence. The selected visual features to measure trust will be informed by a neuroscience approach, using sensors (e.g., EEG or fNIRS) to measure brain activities. 

Deadline : 28 Feb 2024

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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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