Measurement of black carbon emissions from multiple engine and source types using laser-induced incandescence: sensitivity to laser fluence
Bibliographic record
Abstract
Abstract. A new regulatory standard for non-volatile particulate matter (nvPM) mass-based emissions from aircraft engines has been adopted by the International Civil Aviation Organisation. One of the instruments used for the regulatory nvPM mass emissions measurements in aircraft engine certification tests is the Artium Technologies LII 300, which is based on laser-induced incandescence. The LII 300 response has been shown in some cases to vary with the type of black carbon particle measured. Hence it is important to identify a suitable black carbon emission source for instrument calibration. In this study, the relationship between the nvPM emissions produced by different engine sources and the response of the LII 300 instrument utilising the auto-compensating laser-induced incandescence (AC-LII) method was investigated. Six different sources were used, including a turboshaft helicopter engine, a diesel generator, an intermediate pressure test rig of a single-sector combustor, an auxiliary power unit gas turbine engine, a medium-sized diesel engine, and a downsized turbocharged direct-injection gasoline engine. Optimum LII 300 laser fluence levels were determined for each source and operating condition evaluated. It was found that an optimised laser fluence can be valid for real-time measurements from a variety of sources, where the mass concentration was independent of laser fluence levels covering the typical operating ranges for the various sources. However, it is important to perform laser fluence sweeps to determine the optimum fluence range as differences were observed in the laser fluence required between sources and fuels. We discuss the measurement merits, variability, and best practices in the real-time quantification of nvPM mass concentration using the LII 300 instrument and compare that with other diagnostic techniques, namely absorption-based methods such as photoacoustic spectroscopy (using a photoacoustic extinctiometer, PAX, and a micro soot sensor, MSS) and thermal-optical analysis (TOA). Particle size distributions were also measured using a scanning mobility particle sizer (SMPS). Overall, the LII 300 provides robust and consistent results when compared with the other diagnostic techniques across multiple engine sources and fuels. The results from this study will inform the development of updated calibration protocols to ensure repeatable and reproducible measurements of nvPM mass emissions from aircraft engines using the LII 300.
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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) | 0.000 | 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".