Numerical Demonstration of the Humidity Effect in Engine Icing
Bibliographic record
Abstract
The importance of the variation of relative humidity across turbomachinery engine components for in-flight icing is shown by numerical analysis. A species transport equation for vapor has been added to the existing CFD methodology for the simulation of ice growth and water flow on engine components that are subject to ice crystal icing. This entire system couples several partial differential equations that consider heat and mass transfer between droplets, crystals and air, adding the cooling of the air due to particle evaporation to the icing simulation, increasing the accuracy of the evaporative heat fluxes on wetted walls. Three validation cases are presented for the new methodology: the first one compares with the numerical results of droplets traveling inside an icing tunnel with an existing evaporation model proposed by the National Research Council of Canada (NRC). The second one compares humidity and the reduction in the outflow total temperature to the experimental data from NASA Glenn Research Center’s Propulsion Systems Laboratory (PSL). The third case shows that the vapor model improves our icing validation of the crowned cylinder case compared to the NRC experimental data. For the simulation technology demonstration, turbofan icing scenarios with inflow relative humidity varying between 30 and 100% are simulated using a generic engine intake that includes the first stages of the compressor. The inclusion of vapor transport and local relative humidity provide important additional modeling functionalities and increased simulation accuracy.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".