Phase Field Modelling of TRISO SiC Layer Growth by Chemical Vapour Deposition
Bibliographic record
Abstract
The layers of TRISO (TRistructural ISOtropic) particles are manufactured by Fluidized Bed Chemical Vapour Deposition (FB-CVD). The microstructures of the Inner Pyrolitic Carbon (IPyC), Outer Pyrolitic Carbon (OPyC), and SiC layers are affected by the manufacturing conditions of temperature, pressure, and precursor gas concentration during the CVD process. The microstructure and grain morphology of the SiC layer is important since it affects the strength of the adhesion between IPyC-SiC and OPyC- SiC layers as well as the overall integrity of the fuel particle, and permeability of certain elements. Understanding the relationship between the fluidized bed parameters and microstructure facilitates scaling and optimizing particle production and particle performance. Phase field modelling is a proven robust tool for predicting mesoscale phenomena such as mi- crostructure evolution. A thermodynamically informed phase field model was developed to simulate the deposition of the SiC layer during the CVD process. This work presents results of modelling the nucleation, growth, microstructure evolution, and the columnar to equiaxed grain transition; as well as advances in multiphase, polygranular, and stoichiometric phase implementation, density varia- tion between phases, and the use of the computationally efficient Geometric Multigrid (GM) solver in the Firedrake finite element code. The implementation of the GM solver resulted in a significant gain in computational efficiency and enabled the simulation of experimentally-relevant length-scales in 3 dimensions. The results were compared to layer growth data with good quantitative agreement and Electron Backscatter Diffraction (EBSD) images of the SiC layer in surrogate TRISO fuel with good qualitative agreement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".