Development of an Altitude Evaporation Model for Icing Tunnel Control
Bibliographic record
Abstract
In 2017 the National Research Council of Canada developed an evaporation model for controlling engine icing tunnels in real time. The model included simplifications to allow it to update the control system once per second, including the assumption of sea level pressure in some calculations. Recently the engine icing system was required in an altitude facility requiring operation down to static temperatures of -40°C, and up to an altitude of 9.1 km (30 kft) or 30 kPa. To accommodate the larger temperature and pressure range the model was modified by removing the assumption of sea level operation and expanding the temperature range. In addition, due to the higher concentration of water vapor that can be held by the atmosphere at lower pressures, the significance of the effect of humidity on the air properties and the effect on the model was investigated. The effect of humidity on the density, specific heat, viscosity, thermal conductivity and Prandtl number of air compared to assuming dry air was examined. The effect of humidity on the individual thermodynamic and transport properties could be significant but the overall effect on the liquid water content calculated by the model to be delivered to the engine was not. The error in using the property correlations from the original model over the expanded temperature range was found to be minimal. Finally, the numerical technique was modified to decrease the solution time under extreme operating conditions. This modification increased the solution time in some standard conditions but still kept it within the required time. The new model was compared to the previous model under sea level conditions and found to give practically the same results within the expected error allowed by the solver.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".