Bayesian Analysis of Combustion Kinetic Models for Ammonia-Hydrogen Fuel Blends Using Artificial Neural Networks
Bibliographic record
Abstract
Uncertainty quantification (UQ) plays a crucial role in predictive modeling in combustion chemistry, as it improves the accuracy of predictions and the reliability. To accurately predict ignition delay times and nitrogen oxides (NOx) emissions of ammonia (NH3) and hydrogen (H2) fuel blends, minimizing uncertainty in combustion kinetic models is critical. This study introduces a novel approach that integrates Bayesian analysis with Artificial Neural Networks (ANNs) to perform inverse UQ and update combustion kinetic models based on experimentally measured nitric oxide (NO) speciation time history. Traditional Markov Chain Monte Carlo (MCMC) methods are effective, but they are computationally intensive and require large datasets, which limit their practical applicability. ANNs are applied as surrogate models to replace traditional kinetic modeling, optimizing the combustion kinetic model of NH3/H2 fuel blends. By integrating Bayesian analysis with ANNs, the computational cost was significantly reduced compared to conventional MCMC methods, while maintaining high accuracy in uncertainty quantification and parameter optimization. This approach facilitates efficient exploration of parameter space and ensures reliable predictions, making it a valuable tool for complex combustion modeling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".