Measurement of Hydrogen Direct Injection Jet Equivalence Ratio under Elevated Ambient Pressure Condition
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Owing to climate change issues caused by global warming, the role of alternative fuels, such as low-carbon and non-carbon fuels, is becoming increasingly important, particularly in the transportation sector. Therefore, hydrogen has emerged as a promising fuel for internal combustion engines because it does not emit carbon dioxide. Direct injection is mandatory for hydrogen-based internal combustion engines to mitigate backfires and low energy density. However, there is a lack of measurement of the equivalence ratio methodology because hydrogen has a higher diffusion rate than conventional fuels. The objective of this research is a feasibility study of laser-induced breakdown spectroscopy (LIBs) for measuring the equivalence ratio. The second harmonic ND-YAG laser was implemented to induce the atomic emission of hydrogen via the breakdown phenomenon. Simultaneously, the hydrogen jet structure was visualized in a constant volume vessel using Schlieren imaging. Therefore, the experimental results have both measurement location and equivalence ratio information. High-speed Schlieren imaging indicated a highly contracted jet structure under elevated-ambient-pressure conditions. Meanwhile, the local-rich mixture was detected only when the ambient pressure was high due to jet contraction. By contrast, hydrogen does not exist in the core region of the jet because the nozzle has a hollow cone shape under low-ambient-pressure conditions. According to preliminary experimental results, the direct-injected hydrogen jet can be measured using LIBs. However, there was a clear limitation because only the local point area could be measured using LIBs. Despite this apparent limitation, LIBs can contribute to promoting hydrogen-based internal combustion engines to meet the carbon neutrality target by 2050.</div></div>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".