91ÌÒÉ«

Below are projects available in the "Reactor Physics" theme.

Project title: Conventional fuel and lattice optimisation for Small Modular Reactors (SMRs)
Project description: This PhD project aims to optimise traditional UOâ‚‚ fuel and fuel assembly configurations for boron‑free SMRs by systematically exploring pin and assembly geometry, including pin pitch, fuel diameter, and lattice size. Neutronic modelling will be used to assess the impact of these design choices on moderation, power distribution, reactivity coefficients, control requirements, and fuel cycle performance. Particular attention will be paid to how reduced moderation and spectrum shifts influence achievable burnup, peaking factors, and operational margins. 

Thermal‑hydraulic and mechanical considerations will be evaluated in parallel to capture competing design effects. While smaller pins and tighter lattices may improve neutronic performance or fuel utilisation, they may also increase cladding stress, reduce structural robustness, or challenge manufacturability. Sub‑channel analysis and supporting structural assessments will be used to investigate thermal margins, departure from nucleate boiling behaviour, flow‑induced effects, and fuel rod integrity under both steady‑state operation and selected transient conditions. 

The project will integrate these neutronic, thermal‑hydraulic, and structural perspectives to identify balanced fuel assembly designs that maximise performance and minimise fuel cost while remaining credible within existing fabrication and regulatory frameworks. The outcomes are expected to define an optimised, near‑term fuel and lattice design strategy for boron‑free SMRs that leverages proven UOâ‚‚ technology while exploiting the unique opportunities presented by boron‑free operation. 
University: 91ÌÒÉ«
Sponsor: Rolls-Royce SMR
Supervisor(s): Matt Eaton
Desirable candidate background: Engineering, Physics
Desirable candidate skills: N/A
Suitable for part-time study: Y
Eligibility: Home students

Below are projects available in the "Structural Integrity" theme. 

Project title: Characterising fatigue crack growth under secondary loading
Project description: The overall aim is to demonstrate the difference between primary and secondary loading on fatigue crack growth rates, and to develop analytical methods to allow assessment of fatigue crack growth under combined (primary and secondary) loading conditions, like those which occur in real Nuclear plant. In particular, when the stress or stress intensity factor ranges predicted from elastic analysis are significantly altered by plasticity and/or stress relaxation. There may be potential to build on existing approaches, for example the R6 or RSE-M procedures, which attempt to predict the effect of plastic interaction between primary and secondary loads, using simpler elastic inputs. Recent work has also shown that specimen size and constraint can have a significant impact on fatigue behaviour, particularly when the maximum stress intensity factors are high. The impact of elastic follow up on secondary loading is also likely to be important. 

Key research questions are: 

• Can a focussed test programme be used to demonstrate the differences in fatigue crack growth rate under load and strain-controlled loading. 

• Can the impact of specimen size / constraint on secondary load redistribution be quantified in terms of impact on fatigue crack growth.  

• Can an analytical approach be developed to allow assessors to more accurately predict crack growth under combined primary/secondary loading?  

University: University of Bristol
Sponsor: EdF Energy
Supervisor(s): Matthew Peel, Chris Truman
Desirable candidate background: Engineering, Physics, Mathematics
Desirable candidate skills: N/A
Suitable for part-time study? Y
Eligibility: Home students 

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Project title: Developing tools to understand and predict microstructure for through-life performance management
Project description: Current understanding of microstructure development in α-phase processing of Zr alloys is incomplete, limiting prediction of processed microstructures and through-life performance. Texture evolution is understood to some extent, but the role of deformation twinning remains a challenge for physical modelling of texture and microstructure development in hcp alloys. Improved predictive capability could be achieved by combining selective experimental investigation of processing parameters with advanced analytical methods. SPP dispersions influence through-life corrosion performance, potentially influence H trapping, and may play a role in weld microstructure development. However, tracking SPP features such as size distributions in a statistically representative way throughout processing remains challenging due to limitations in lab-based characterisation methods. This in turn limits the ability to connect SPP statistics with material properties. This project will combine experimental investigation of process–microstructure relationships with development of characterisation and analysis pipelines for lab-scale monitoring of these features in production material. You will use a suite of advanced characterisation techniques (electron microscopy, atom probe tomography and anticipated synchrotron experiments) to generate new mechanistic insights and drive improvements in predictive capability for critical materials performance.
University: 91ÌÒÉ«
Sponsor: Rolls-Royce Plc
Supervisor(s): Felicity Worsnop, David Dye
Desirable candidate background: Materials science and engineering or related discipline
Desirable candidate skills: An understanding of nuclear materials/physical metallurgy, experience with experimental lab work, microstructure characterisation and data analysis
Suitable for part-time study? Y
Eligibility: Home students 

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Project title:  Impact of residual stress on nuclear fracture assessments
Project description: There is some experimental evidence that shows that for relatively ductile, tough materials, residual stresses don’t impact failure loads. On the other hand, for brittle materials (or at low temperatures), experiments show a clear impact of residual stresses. An additional complication is that lab scale testing is often at much smaller scale than real plant components. Therefore test specimens may fail in a regime closer to plastic collapse, than for real plant where constraint levels are higher. Therefore some thought may be needed to ensure that any test specimens are similar (for example in terms of position on a typical Failure Assessment Diagram) to what would be expected for plant components. Key research questions to be answered are: 

• Can it be demonstrated that residual stresses may be ignored for fracture of tough materials (such as those used in a modern nuclear plant). 

• Is there a difference in impact on different phases of crack growth – for example first initiation vs stable tearing vs crack arrest. 

• Can a criteria be established to determine when residual stresses do or don’t need to be accounted for? This will be key for industry assessors to be able to make use of any change in approach when temperatures and material properties may vary. 

University: University of Bristol
Sponsor: EdF Energy
Supervisor(s):  Chris Truman, Matthew Peel
Desirable candidate background: Engineering, Physics, Mathematics
Desirable candidate skills: N/A
Suitable for part-time study? Y
Eligibility: Home students 

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Project title: Laser beam shaping in laser powder bed fusion for improved process efficiency of Inconel 625 nuclear components
Project description: This PhD project will investigate whether laser beam shaping can improve the efficiency of laser powder bed fusion processing of Inconel 625 for nuclear applications by increasing build speed without compromising component quality. In nuclear and other safety-critical sectors, the adoption of additive manufacturing depends not only on achieving high material performance, but also on delivering robust and efficient processing routes. By modifying the spatial distribution of laser energy, beam shaping offers a promising route to improved melt pool control and higher-productivity manufacture. The research will combine manufacturing trials with detailed characterisation to determine how shaped beam profiles influence process stability, defect formation, microstructure and mechanical behaviour under high-productivity conditions. The project aims to identify and validate processing windows that enable faster manufacture of Inconel 625 while maintaining the quality and performance required for nuclear-relevant components. 
University: 91ÌÒÉ«
Sponsor: Rolls-Royce Plc
Supervisor(s): Paul Hooper, Catrin Davies
Desirable candidate background: N/A
Desirable candidate skills: N/A
Suitable for part-time study? Y
Eligibility: Home students 

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Project title: Numerical modelling of microscale material behaviour for prediction of damage
Project description: Safe operation of nuclear power stations requires understanding of how materials degrade over time. These processes start at the microscale, for example at grain boundaries, so accurate modelling and understanding of damage at this length scale can help inform life assessment at the component scale. 

Current models are able to capture grain-averaged mechanical behaviour effectively. However damage often initiates from bands of deformation within grains, known as slip localisation. Accurate modelling of behaviour within grains relies on defining where slip bands will form, rather than predicting this behaviour from ‘first principals’. The development of a modelling technique that is capable of  capturing these phenomena using a ‘bottom-up’ approach, based only on local material structure, would be a valuable step forward in understanding damage in engineering materials. 

This PhD will focus on development of mesoscale modelling and simulation approaches for intragranular mechanical behaviour with an emphasis on improving damage predictions. Validation of the methods would involve experimental work using state-of-the-art techniques such as dark-field X-ray microscopy and high-resolution microscopy.
University: University of Bristol
Sponsor: EdF Energy
Supervisor(s): Nicolo Grilli, Chris Truman
Desirable candidate background: Engineering, Physics, Mathematics
Desirable candidate skills: N/A
Suitable for part-time study? Y
Eligibility: 

Project title: Prediction and measurements of dissimilar metal weld properties in support of structural integrity assessment of nuclear structural materials
Project description: Currently the guidance for Dissimilar Metal Weld (DMW) in fracture assessment is limited, and the common practice is to model an abrupt change in material properties and then use the worst combination of inputs in the assessment (max weld residual stress, max stress range, min toughness, max fatigue crack growth rate, min yield strength).  This adds significant conservatism into assessments, resulting in significant effort to reduce conservatism and in some cases requirements for in-service inspections to support life. In this project, we aim to develop modelling techniques to predict the microstructure and map the varying composition across DMW boundaries for prototypic welds (stainless to ferritic, Nickel Based Alloys (NBA) to ferritic and stainless to NBA).  This would need to be validated against scans from actual welds and consider variability of heat input, geometry etc. 
University: University of Bristol
Sponsor: Rolls-Royce Plc
Supervisor(s): Chris Truman, Nicolo Grilli
Desirable candidate background: Engineering, Physics, Mathematics
Desirable candidate skills: N/A
Suitable for part-time study? Y
Eligibility: Home students

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Project title: Prediction of mechanical performance and qualification of laser powder bed fusion components for nuclear applications using in-process monitoring data
Project description: This PhD project will investigate whether in-process monitoring data from laser powder bed fusion can be used to predict the mechanical properties and structural integrity of metal additively manufactured components for nuclear applications. The research will combine in-process monitoring, post-manufacture characterisation, mechanical testing and simulation to establish how process anomalies, defects and residual stress influence final component performance. Through the integration of these datasets, the project aims to develop a robust and traceable qualification framework capable of informing practical decisions for nuclear components. The work addresses a major challenge in the adoption of metal additive manufacturing in the nuclear sector and offers the student the opportunity to work at the interface of advanced manufacturing, materials characterisation, mechanics and predictive modelling on an industrially important problem. 
University: 91ÌÒÉ«
Sponsor: MoD
Supervisor(s): Paul Hooper, Catrin Davies
Desirable candidate background: Engineering, Science, Computing
Desirable candidate skills: Mechanical testing
Suitable for part-time study? Y
Eligibility: Home students 

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Project title: Powder oxygen content effects on microstructure, fatigue and toughness in stainless steel
Project description: The oxygen content of LB-PBF SS316 material its typically much higher than wrought or HIP SS316. This high oxygen content can be traced back to the raw material supply. Improved methods of powder production could potentially enable starting powder oxygen content to be reduced significantly. Conversely, new LB-PBF hardware can recover powder currently wasted during the process that may have an increased oxygen content. This project aims to understand the impact of oxygen content on heat treatment, microstructure, fatigue performance and fracture toughness. 
University: Swansea University
Sponsor: Rolls-Royce Plc
Supervisor(s) Rob Lancaster
Desirable candidate background: Materials Science and Engineering, Engineering
Desirable candidate skills: N/A
Suitable for part-time study: N
Eligibility: UK nationals only. 

Below are projects available in the "Thermal Hydraulics" theme.

Project title: AI‑enabled Reduced Order Models (ROMs), world models, and surrogate modelling for physical fidelity of nuclear reactor simulations
Project description: Future green energy security will require making the most of state-of-the-art data driven artificial intelligence (AI) modelling. High‑fidelity nuclear reactor core simulations—combining neutronics, thermal‑hydraulics, and fuel behaviour—are computationally intensive and limit rapid design iteration, optimisation, uncertainty analysis, and near‑real‑time decision support. This PhD will address these challenges by developing a range of AI‑enabled Reduced Order Models (ROMs), world models, and/or surrogate modelling that retain essential physical fidelity while dramatically reducing computational cost. The project will focus on adapting, training, and validating AI‑based surrogate models capable of predicting steady‑state and transient reactor core behaviour. These models will be integrated into optimisation and decision‑support workflows to enable fast scoping studies, sensitivity analyses, and exploration of large, multi‑dimensional SMR core design spaces. 

The research will be conducted in close collaboration with Rolls‑Royce SMR, ensuring strong industrial relevance and experience to real‑world nuclear engineering challenges. This PhD will contribute directly to the engineering teams enabling faster, more informed engineering decisions in SMR design and operation, supporting safer, more efficient, and cost‑effective nuclear energy. 
Institution: 91ÌÒÉ«
Sponsor: Rolls-Royce SMR
Supervisor(s): Christopher Pain
Suitable for part-time study: Y
Eligibility: Home students

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Project title: Estimating uncertainties in RANS CFD predictions for applications in nuclear thermal hydraulics
Project description: This project focusses on the thermal hydraulics and is a fundamental area of concern for nuclear reactors, defining the performance and operating envelope and the safety of the whole system. Computational fluid dynamics (CFD) offers considerably higher fidelity modelling than historical reduced order models such as systems codes. That said, the practical implementation (discretization, turbulence models, etc) introduces considerable uncertainties in what is a safety and performance critical technology. Validation, verification and Uncertainty Quantification (VVUQ) is thus an important aspect of modelling if we are to achieve trusted, robust and reliable results. 

The approach to this CFD-VVUQ problem will build on recent work by Y Wang et al. at MIT. This work outlines a framework for greatly improved error estimation and UQ of RANS CFD analysis. It takes traditional Richardson extrapolation (RE) (and related) approaches, which are generally unreliable as a predictor of error, and propagates the uncertainties using a Gaussian Random field. Sampling of this random field provides vastly better quantifications of uncertainty. However, there remain problems, specifically the sampling process itself is costly as it requires repeated CFD solutions and it is unclear whether in practice this would perform better overall than RE or least square approaches with further refinement. 

The project will focus on a specific component, the T-junction, as a test case involving non-isothermal turbulent mixing. The methods developed will extend the approach beyond just CFD, and incorporate modelling of fluid flows, thermal mixing (CFD) and the resulting thermal stresses (FEA). This project will study and develop surrogate models as a replacement for the sampling approach in the recent literature. Surrogate models trained on CFD/FEA could greatly accelerate the sampling process, providing a flexible and efficient framework for VVUQ. Plainly, surrogate models will incur some additional error, and that itself will have to be incorporated into the overall VVUQ framework. 
Institution: 91ÌÒÉ«
Sponsor: Rolls-Royce Plc
Supervisor(s) Mike Bluck, Matt Eaton
Desirable candidate background: Engineering, Physics, Mathematics
Desirable candidate skills: Mathematical and computational modelling, computing
Suitable for part-time study: N
Eligibility: UK nationals only

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Project title: Exploring benefits of lattice Boltzmann methods for nuclear thermal hydraulics
Project description: This PhD project will investigate the feasibility of using Lattice Boltzmann Method (LBM) for nuclear thermal hydraulics (NTH) simulations, in collaboration with Rolls-Royce. LBM is a mesoscopic simulation approach, grounded in statistical mechanics, which has demonstrated advantages over conventional Finite Volume Method (FVM) CFD in terms of scalability, CAD-to-CFD workflow efficiency, and computational cost for scale-resolving turbulence simulations. Despite these advantages, LBM has not yet been widely adopted in the heavily regulated NTH sector. This project addresses that gap directly.  

The successful candidate will begin by simulating established NTH benchmark cases, comparing LBM predictions against FVM CFD and experimental data, and identifying and resolving current limitations in LBM codes for NTH-relevant flow physics.  The project will then progress to larger-scale industrially relevant simulations and may develop a novel coupled LBM/URANS methodology that combines the efficiency of unsteady Reynolds-averaged approaches at system scale with the turbulence-resolving capability of LBM in regions of interest.  A further key strand of the project will be the development of Verification Validation & Uncertainty Quantification (VVUQ) frameworks that can robustly demonstrate confidence in predictive LBM simulations where like-for-like validation data is unavailable.  

The work sits at the intersection of computational fluid dynamics, nuclear engineering and uncertainty quantification, and offers the student the opportunity to engage with a genuinely open and industrially important research question, including a placement at Rolls-Royce and attendance at relevant international conferences. 
Institution: University of Manchester
Sponsor: Rolls-Royce Plc
Supervisor(s): Alessandro De Rosis, Alastair Revell
Desirable candidate background: Engineering, Computing, Mathematics, Sciences
Desirable candidate skills: Experience/familiarity with computational fluid dynamics (CFD) and programming
Suitable for part-time study: Y
Eligibility: UK Nationals only

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Project title: Improved component scale CFD wall boiling modelling to better account for heat transfer surface characteristics on CHF
Project description:The project will advance the accuracy of Computational Fluid Dynamics (CFD) models employed for predicting boiling heat transfer and particularly the Critical Heat Flux (CHF) in nuclear Pressurized Water Reactors (PWR). PWR cores are water-cooled and operate at conditions where boiling may occur due to the high surface heat fluxes from the nuclear fuel to the cooling water. All PWRs must therefore control boiling to operate safely. In boiling, CHF identifies the highest reachable heat flux before a large drop in heat transfer efficiency, due to the heating surface becoming blanketed with vapour, triggers an abrupt overheating of the surface. CHF is a major safety limit for PWRs and reactors must be designed in such a way that CHF is never exceeded to avoid overheating and damage to the fuel clad, the nuclear fuel, and the release of radioactive material. At present, limitations in the modelling and computational approaches used to predict CHF force reactor vendors to introduce large safety margins in their design, i.e., PWRs are operated only at around 75% of the predicted CHF limit, which hinders their economic viability. 

In this project, you will develop three-dimensional component-scale CFD approaches capable of predicting boiling and CHF in PWR-relevant conditions. Combining improved physical modelling with the potential of machine learning and data assimilation techniques, you will specifically target two areas where existing models have major limitations: 

• the impact and modelling of real-world boiling surface characteristics. Bubbles nucleate at microscopic cavities, present and non-homogeneously distributed on every heat transfer surface along with different surface defects. These features have a strong impact on boiling conditions but are mostly overlooked in available CFD models. Ideal, homogeneous surface conditions are assumed in models, which can lead to a loss of accuracy when applied to real reactor surfaces. 

• the CFD modelling of CHF that currently does not account for the critical role of the boiling surface microscale characteristics and wettability properties. 

By making available CFD predictions of improved accuracy and reduce uncertainty, your research will have transformative impact on the safety, efficiency and economics of PWRs by enabling: 

• PWRs to be designed and substantiated to be safely operated at higher power levels, making them more efficient and sustainable; boiling conditions and CHF margin to be monitored and characterized over the entire lifetime of the nuclear fuel in reactor operating conditions; easier and faster assessment of surface finishes and coatings, facilitating the development of safer accident tolerant fuels with a higher margin over melting and the optimization of engineered surfaces.

Institution: University of Nottingham
Sponsor: Rolls-Royce Plc
Supervisor(s) Mirco Magnani, Mike Bluck
Desirable candidate background: Engineering, Physics, Mathematics
Desirable candidate skills: Mathematical and computational modelling, computing
Suitable for part-time study: N
Eligibility: UK nationals only

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Project title: Scaling of perturbed natural circulation loops
Project description: This PhD project will investigate the scaling of perturbed natural circulation (NC) loops. Natural circulation is of growing importance in nuclear thermal hydraulics as a passive mechanism for reactor core heat removal, and experimental facilities play a central role in substantiating NC behaviour for reactor design. However, designing scaled experiments for perturbed NC scenarios is a major unsolved challenge, due to the rich and strongly coupled dynamics governing these systems and the wide range of relevant length and time scales involved.  

This project addresses that challenge through a novel combination of computational analysis and data-driven methods. The student will use both 1D system-level codes and 3D CFD to simulate perturbed NC loops, comparing their scaling behaviours and building physical understanding of system response. ML/AI techniques, including Sparse Identification of Nonlinear Dynamics (SINDy), will then be applied to extract simplified evolution equations and scaling laws from the simulation data.  

The project will deliver data, scaling rules and design guidance that enable simpler and more cost-effective experimental facilities to generate results that can be confidently translated to full plant scale. The work spans nuclear thermal hydraulics, computational fluid dynamics, and applied machine learning, and offers the student the opportunity to work on a problem of direct industrial relevance, including a placement at Rolls-Royce and attendance at international conferences.
Institution: University of Manchester
Sponsor: Rolls-Royce Plc
Supervisor(s): Dean Wilson, Alex Skillen
Desirable candidate background: Engineering, Computing, Mathematics, Sciences
Desirable candidate skills: Experience/familiarity with computational fluid dynamics (CFD) and programming
Suitable for part-time study: N
Eligibility: UK Nationals only

Below are projects available that cut across two or more themes.