Explosion pressure and duration prediction using machine learning: A comparative study using classical models with <scp>Adam‐</scp> optimized neural network
Bibliographic record
Abstract
Abstract The application of machine learning (ML) for the prediction of gas explosion pressure remains limited, and the prediction of the explosion duration is nearly non‐existent. A series of dispersion and subsequent explosion computational fluid dynamics (CFD) simulations were conducted to determine explosion pressure and duration values. These results were used to train classical ML models, that is, support vector regression (SVR), random forest (RF), and decision tree (DT) models. Additionally, a multi‐output Adam‐optimized artificial neural network (ANN) model was employed for performance comparison. All the models demonstrated respectable predictions for both parameters, while the RF model demonstrated the highest performance based on the metrics analyzed, followed by the DT model. The proposed gas volume and gas volume blockage ratio (gas‐VBR) emerged as the most crucial feature for predicting explosion pressure, while the monitoring point and gas‐VBR was the most important feature for explosion duration. It is recommended to consider the gas‐VBR feature in future studies rather than solely focusing on blockage ratio or obstacle location. The model proposed was compared with models from previous studies for predicting explosion pressure. The findings conclusively demonstrate that the multi‐output model outperforms the compared models, offering a notable advantage in its ability to predict both gas explosion pressure and duration.
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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 | 0.001 |
| 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 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".