Application of Digital Twin Technology to Aeronautical Combustion: A Case Study on Hydrogen Microinjectors
Bibliographic record
Abstract
Abstract The rise of alternative fuels leads to numerous new possible types of injection technology for gas turbine combustion. One promising candidate is microinjection, which relies on the creation of multiple miniaturized flamelets in order to reduce NOx production. From a design and engineering perspective, new sets of tools of various fidelity are needed to make the design screening step faster and more exhaustive. A reduced-order model (ROM) based on the OpenMeasure library and NEXT STEP has been implemented in order to create a digital twin of hydrogen micro-injectors. The ROM is based on either Sparse Sensing or Kriging methodology, both involving a Proper Orthogonal Decomposition. This approach has been carried out on 26 designs, where several geometrical parameters (e.g. number of fuel injection holes, aspect ratio, etc.) and operating conditions (i.e. atmospheric and high pressure, equivalence ratio, and fuel mass flow rate) are varied. The prediction of fields (e.g. temperature, OH mass fraction, etc.) via the reduced model was assessed using 33 RANS simulations, the latter allowing to establish a database of micro-injector behaviour. The RANS approach has been validated against both experimental results and Large-Eddy Simulations. A selection of model inputs was made based on an assessment of the model’s predictive accuracy using the Kriging estimation method. The predictions of the reduced-order model showed qualitative agreement with the reference data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".