A note on the improvement of Artificial Neural Network models for detonation cell size and critical tube diameter prediction
Why this work is in the frame
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Bibliographic record
Abstract
In this note we present strategies to improve a deep Artificial Neural Network (ANN) to predict the dynamic parameters of gaseous detonations in hydrogen- and other hydrocarbon-based mixtures. These new strategies involve using only non-dimensional features for the model, which have been created using thermochemical and chemical kinetic parameters from the steady reaction zone structure commonly used in detonation studies, as well as a non-dimensional target, obtained by dividing the experimental cell size with the induction length Δ I . In addition, the ANN model's structure has been supplemented with dropout layers, thus improving the training process and also leading to a better determination of the model's uncertainty. Apart from predicting the detonation cell size, this updated model creation approach is implemented to the critical tube problem, combining thermochemical and kinetic parameters with experimental data to create an accurate model that predicts the critical tube diameter D C . The optimal structure and combination of features for the ANN are thoroughly assessed. The source codes of the ANN models are readily available on GitHub. • Prediction of detonation dynamic parameters in different gas mixtures based on ANN. • Improvement on the ANN formulation using non-dimensional input features. • The dropout technique is implemented to assess the uncertainty of the prediction. • A series of ANN- λ and ANN- D c models are built and tested for accuracy.
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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) | 0.000 | 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 it