Temperature Calibration Measurements based on Laser-Induced Phosphorescence Technique for Combustion Applications
Bibliographic record
Abstract
Phosphor powder and phosphor-binder mixtures are successfully employed for temperature calibration measurements by using laser-induced phosphorescence (LIP) technique with an emphasis on higher precisions and accuracies than other non-intrusive methods. The phosphorescence intensities are used to perform these calibrations in three different strategies. The influence of laser power regular changes on particles heating and the calibration analyses is also carried out. A pulsed laser at 355 nm was used for exciting specimens of the phosphor powder as well as the phosphor-binder mixtures. The laser beam was directed onto the specimens and varied in three laser power levels (LPLs). The samples were kept in an oven with temperatures ranging from room temperature up to 1800 °C. The three strategies which are expressed in terms of non-dimensional intensity versus wavelength (NDI-W), normalised intensity (NI) and intensity ratio (IR) were used for the calibration assessments. A modified IR was compared with two different IRs. A precision of around ± (0.50-1.41)% was attained for different calibration methods. This research confirmed that these calibrations are possible using three different strategies, given high precisions and accuracies. The laser power alternations influenced the NI and do affect neither the NDI-W nor the IR curves. The laser radiation does not play any role for heating the particles of the studied powder.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".