High-Speed 2-D Raman and Rayleigh Imaging of a Hydrogen Jet Issued from a Hollow-Cone Piezo Injector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This paper reports high-speed (10 kHz and 100 kHz) 2-D Raman/Rayleigh measurements of a hydrogen (H<sub>2</sub>) jet issued from a Bosch HDEV4 hollow-cone piezo injector in a high-volume constant pressure vessel. During the experiments, a <i>P<sub>a</sub></i> = 10 bar ambient environment with pure nitrogen (N<sub>2</sub>) is created in the chamber at <i>T</i> = 298 K, and pure H<sub>2</sub> is injected vertically with an injection pressure of <i>P<sub>i</sub></i> = 51 bar. To accommodate the transient nature of the injections, a kHz-rate burst-mode laser system with second harmonic output at <i>λ</i> = 532 nm and high-speed CMOS cameras are employed. By sequentially separating the scattered light using dichroic mirrors and bandpass filters, both elastic Rayleigh (<i>λ</i> = 532 nm) and inelastic N<sub>2</sub> (<i>λ</i> = 607 nm) and H<sub>2</sub> (<i>λ</i> = 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 H<sub>2</sub> 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 H<sub>2</sub> injectors.</div></div>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it