Evaporator Design for an Isokinetic Total Water Content Probe in a Naturally Aspirating Configuration
Bibliographic record
Abstract
A number of recent aircraft turbofan power failure events have been linked to ice accretion in the initial compressor stages while the aircraft is traversing the anvil region of storm clouds. The water content in such cloud regions is not well known and the accuracy of most existing water content probes is likely to be poor under such conditions. A new cloud water content probe is being developed for airborne characterisation of such clouds and a critical feature of the probe is the evaporator. In this work we develop some analytical expressions to assist in the design and characterisation of the evaporator. In particular, we consider the issue of convective heat transfer to the ice and water particles moving with the air flow through the evaporator. For the particular evaporator design we are considering, it is shown that ice particles larger than 100 µm are unlikely to have sufficient residence time to evaporate if they remain suspended in the heated air. Although these larger ice particles are likely to impact on the evaporator walls so there is also an opportunity for direct conduction heating, the present analysis indicates that particles larger than 100 µm may not adhere to the walls. However, there are many uncertainties in the present analysis and experiments are needed to determine the actual performance of the evaporator.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".