Effective density and volatility of particles sampled from a helicopter gas turbine engine
Bibliographic record
Abstract
The effective density and size-resolved volatility of particles emitted from a Rolls-Royce Gnome helicopter turboshaft engine are measured at two engine speed settings (13,000 and 22,000 RPM). The effective density of denuded and undenuded particles was measured. The denuded effective densities are similar to the effective densities of particles from a gas turbine with a double annular combustor as well as a wide variety of internal combustion engines. The denuded effective density measurements were also used to estimate the size and number of primary particles in the soot aggregates. The primary particle size estimates show that the primary particle size was smaller at lower engine speed (in agreement with transmission electron microscopy analysis). As a demonstration, the size-resolved volatility of particles emitted from the engine is measured with a system consisting of a differential mobility analyzer, centrifugal particle mass analyzer, condensation particle counter, and catalytic stripper. This system determines the number distributions of particles that contain or do not contain non-volatile material, and the mass distributions of non-volatile material, volatile material condensed onto the surface of non-volatile particles, and volatile material forming independent particles (e.g., nucleated volatile material). It was found that the particulate at 13,000 RPM contained a measurable fraction of purely volatile material with diameters below ∼25 nm and had a higher mass fraction of volatile material condensed on the surface of the soot (6%–12%) compared to the 22,000 RPM condition (1%–5%). This study demonstrates the potential to quantify the distribution of volatile particulate matter and gives additional information to characterize sampling effects with regulatory measurement procedures.Copyright © 2017 American Association for Aerosol Research
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.
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 | 0.001 | 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 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".