The University of Manchester

Adrian Kisiel

EngD Fusion Engineering CDT

Current research projectProbabilistic Digital Twins for Nuclear Engineering

Background and qualifications

About Me

Adrian Kisiel

I am an EngD researcher in the Fusion Engineering CDT at The University of Manchester, focusing on computational modelling and the development of digital twins in nuclear engineering.

My work spans both nuclear fusion and nuclear fission, from digital twins of materials for nuclear fusion reactor components to the modelling of nuclear graphite for the new generation of Gen IV reactors.

I studied mechanical engineering at the University of Sheffield, completing a BEng and an MSc, and specialise in computational modelling, materials behaviour and nuclear engineering. My previous research has focused on computational modelling and nuclear materials.

Qualifications

  1. BEng (Hons) Mechanical Engineering
    University of Sheffield
  2. MSc Advanced Mechanical Engineering
    University of Sheffield
  3. EngD Fusion Engineering CDT
    The University of Manchester
    Current

Current and completed work

My Research

Nuclear fusion and nuclear fission

Probabilistic Digital Twins in Nuclear Engineering

My research develops probabilistic digital twins for both nuclear fusion and nuclear fission, using stochastic methods and the random finite element method to treat material properties as spatially varying random fields. Predictions are therefore expressed as distributions rather than single values and are progressively refined as measurements are assimilated, providing a quantified basis for decisions on inspection, maintenance and component life.

In nuclear fusion this work is directed at the materials of in-vessel components, whose behaviour under a nuclear fusion neutron spectrum cannot yet be measured directly, and in nuclear fission at the graphite cores of Gen IV reactors.

  • Stochastic methods
  • Random finite element method
  • Uncertainty quantification
Model aloneTwin updated with measurementsMeasurement
Fig. 1Illustrative. A probabilistic twin narrows its prediction where measurements exist and widens it again when forecasting beyond them.

Nuclear fission · Gen IV

Digital Twins for Nuclear Graphite in Gen IV Reactors

My research focuses on the computational modelling and probabilistic digital twins of nuclear graphite for Generation IV reactors. Graphite is the moderator and principal core structural material in several Gen IV concepts, most notably the very-high-temperature reactor and thermal-spectrum molten salt reactors, where its low neutron absorption cross-section and efficient elastic scattering slow the neutrons released by nuclear fission to thermal energies. Its large heat capacity and the negative fuel temperature coefficient arising from Doppler broadening of the uranium-238 resonances contribute to the inherent safety of these designs.

Under fast-neutron irradiation, however, graphite undergoes dimensional change, irradiation creep and a progressive loss of thermal conductivity and, at high doses, strength. Because these properties vary between and within individual components, the condition of a graphite core is inherently uncertain, and my work quantifies how this variability propagates into predictions of component life and core behaviour.

  • Gen IV reactors
  • Neutron moderation
  • Irradiation damage
Mean responseMaterial variability
Fig. 2Schematic. Dimensional change of nuclear graphite with fast-neutron dose, showing initial shrinkage, turnaround and subsequent expansion beyond the original size.

MSc dissertation · 2026

Modelling and Analysis of Plasma-Facing Components in Nuclear Fusion Reactors: A DBTT Study on Tungsten

Background

Tungsten is the leading armour material for nuclear fusion plasma-facing components, combining the highest melting point of any metal (3695 K) with high thermal conductivity, a low sputtering yield and low tritium retention. Its use is constrained by the ductile-to-brittle transition: below the transition temperature it fractures by cleavage, and because recrystallisation and neutron irradiation raise that temperature, it can fall within the operating range of a reactor that cycles through start-up and shut-down every pulse. No neutron source yet reproduces the neutron spectrum of a nuclear fusion reactor, so the irradiated transition cannot be measured under representative conditions, and validated assessment methods for brittle armour materials remain lacking.

Approach

I implemented a continuum dislocation dynamics model of a single tungsten grain in Julia (around 1,300 lines, with no external dislocation library). Screw dislocation glide is governed by thermally activated kink-pair nucleation, so glide velocity varies by around twelve orders of magnitude across the temperature range. Frank–Read sources multiply dislocations, impenetrable grain boundaries force pile-ups whose back-stress throttles further plasticity, and the grain unloads against its surroundings as plastic strain accumulates. The plastic work dissipated in a fixed 120 s window was computed at 30 temperatures between 100 and 3000 K, and the transition temperature was taken from the inflection of the resulting curve, with nothing fitted to measured transition data.

The model was verified in stages against analytical and published values. The simulated pile-up converged to within 1.1% of the Eshelby–Frank–Nabarro solution under mesh refinement, and correcting a dimensional inconsistency in the published kink-barrier amplitude brought the kink-pair energy to 1.574 eV, within 10% of the 1.75 eV measured on high-purity single crystals.

Findings

The model reproduces the lower shelf, steep transition and upper plateau characteristic of experimental transition curves directly from the dislocation kinetics, with the transition arising from the coupled loss of dislocation mobility and source emission at low temperature. It predicts a transition temperature of 386 K (347–425 K for ±10%), between the 348 K and 398 K measured on warm-rolled pure tungsten.

Coarser grains raised the transition temperature by 63 K across a fivefold increase in grain size, while plateau work increased 21-fold, consistent with dissipative capacity scaling with grain area. Irradiation raised the transition steeply before saturating, with 80% of the shift reached by 0.5 dpa and a predicted shift of 614 K at 1 dpa against approximately 650 K measured experimentally. Because a full 30-point sweep runs in under a minute on a single desktop core, the model serves as an inexpensive, mechanism-based screening tool for conditions beyond practical experimental reach.

+45° slip planes−45° slip planesFrank–Read source
Fig. 3The simulated grain of edge length D under applied shear. Dislocations emitted by the sources glide along their planes and pile up against the impenetrable boundaries.
SimulationInterpolated curveMeasured, pure W (348–398 K)
Fig. 4Plastic work after 120 s against temperature for an 8 µm grain. The predicted transition of 386 K lies between the two closest measurements on pure tungsten (Bonnekoh et al., 2020).
Model, inflection criterionMeasured, Gaganidze et al. (2021)
Fig. 5Predicted shift in transition temperature with irradiation dose. The shift saturates at 652 K, with 80% reached by 0.5 dpa.
Model, inflection criterion
Fig. 6Predicted transition temperature against grain size. A fivefold increase raises the transition from 370 K to 433 K.
Model against measurement
QuantityThis workMeasured
Transition temperature386 K348 and 398 K, pure WBonnekoh et al. (2020)
Kink-pair energy1.574 eV1.75 eV, 220–600 KBrunner (2000)
Grain size, 5 → 25 µm+63 KRises with grain sizeBonnekoh et al. (2019)
Irradiation shift, 1 dpa614 K≈ 650 KGaganidze et al. (2021)
Pile-up verification1.1% errorAnalytical solutionEshelby, Frank and Nabarro (1951)

BEng dissertation · 2025

Digital Image Correlation (DIC) in Material Testing and Analysis

Background

Digital image correlation measures full-field surface displacement by tracking a random speckle pattern across images captured by calibrated cameras. Poor surface preparation, speckle damage and errors in lighting or calibration leave gaps in the measured field, and these propagate into the stress and strain estimates on which material characterisation depends.

Approach

I developed a Python tool, built on Correlated Solutions' VicPy library with a Tkinter interface, that loads, cleans, visualises and reconstructs displacement fields exported from VIC-3D. Five interpolation methods (nearest neighbour, linear, cubic, radial basis function and inverse distance weighting) were benchmarked against four synthetic missing-data patterns, with every reconstruction validated against the known field using RMSE, mean error and error spread.

Findings

Radial basis function interpolation was the most accurate across all four patterns, with a mean RMSE of 0.430 (2.06% of the displacement range) at around 290 ms per run. Linear and cubic interpolation came within 2% of that accuracy in under 13 ms, making them the practical choice for rapid post-processing, whereas inverse distance weighting performed worst (RMSE 1.233) and flattened peak displacements by almost 50%.

Fig. 7Synthetic missing-data patterns applied to a reference displacement field, representing severe speckle damage, minor noise, directional occlusion and loss of peak deformation.
Interpolation methodMost accurate
Fig. 8Mean reconstruction error against mean runtime across all four patterns. Runtime is on a logarithmic scale.

Get in touch

Contact Me