High-Speed 2-D Raman and Rayleigh Imaging of a Hydrogen Jet Issued from a Hollow-Cone Piezo Injector
Bibliographic record
Abstract
This paper reports high-speed (10 kHz and 100 kHz) 2-D Raman/Rayleigh measurements of a hydrogen (H2) jet issued from a Bosch HDEV4 hollow-cone piezo injector in a high-volume constant pressure vessel. During the experiments, a Pa = 10 bar ambient environment with pure nitrogen (N2) is created in the chamber at T = 298 K, and pure H2 is injected vertically with an injection pressure of Pi = 51 bar. To accommodate the transient nature of the injections, a kHz-rate burst-mode laser system with second harmonic output at λ = 532 nm and high-speed CMOS cameras are employed. By sequentially separating the scattered light using dichroic mirrors and bandpass filters, both elastic Rayleigh (λ = 532 nm) and inelastic N2 (λ = 607 nm) and H2 (λ = 683 nm) Raman signals are recorded on individual cameras. With the help of the wavelet denoising algorithm, the detection limit of 2-D Raman imaging is greatly expanded. The H2 mole fraction distribution is then derived directly from scattering signals at 10 kHz for Raman and 100 kHz for Rayleigh, with a spatial resolution of approximately 200 μm (5.0 lp/mm). The current work successfully demonstrates the feasibility of high-speed 2-D Raman and Rayleigh imaging in gaseous fuel injection and the experimental technique could potentially contribute to the design of next-generation high-pressure, high-flowrate H2 injectors.
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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 | 0.000 | 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) | 0.002 | 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".