Long short-term memory time series modelling of pressure valves for hydrogen-powered vehicles and infrastructure
Bibliographic record
Abstract
Long-term reliability and accuracy of pressure valves are critical for hydrogen infrastructure and applications, particularly in hydrogen-powered vehicles exposed to extreme weather conditions like cold winters and hot summers. This study evaluates such valves using the Endurance Test specified in European Commission Regulation (EU) No 406/2010, fulfilling Regulation (EC) No 79/2009 requirements for hydrogen vehicle type approval. A long short-term memory (LSTM) network accelerates valve development and validation by simulating endurance tests. The LSTM model, with three inputs and one output, predicts valve outlet pressure responses using experimental data collected at 25 °C, 85 °C, and −40 °C, simulating a 20-year lifecycle of 75,000 cycles. At 25 °C, the model achieves optimal performance with 40,000 training cycles and an R 2 of 0.969, with R 2 values exceeding 0.960 across all temperatures. This efficient, robust approach accelerates testing, enabling real-time diagnostics and advancing hydrogen technologies for a sustainable future.
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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) | 0.000 | 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".