FENSAP-ICE: Numerical Prediction of Ice Roughness Evolution, and its Effects on Ice Shapes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Numerically predicted roughness distributions obtained in in-flight icing simulations with a beading model are used in a quasi-steady manner to compute ice shapes. This approach, called "Multishot," uses a number of steady flow and droplet solutions for computing short intervals (shots) of the total ice accretion time. The iced geometry, the grid, and the surface roughness distribution are updated after each shot, producing a better match with the unsteady ice accretion phenomena. Comparisons to multishot results with uniform roughness show that the evolution of the surface roughness distribution has a strong influence on the final ice shape. The ice horns that form are longer and thinner compared to constant roughness results. The constant roughness approach usually fails to capture the formation of the pressure side horns and under-predicts the thickness of the ice in this region. With the variable roughness produced by the beading model, the ice shape in general is captured more accurately, better matching the experimental ice shapes. Results are provided for NACA 0012 and SC 1095 airfoils for 2D icing. The flow conditions range between Mach numbers of 0.3 to 0.6, free stream static temperatures of -27°C to -6°C, free stream liquid water contents of 0.5 g/m₃ to 1.4 g/m₃, Reynolds numbers of 2.6 to 4.4 million, and droplet diameter of 20 microns. Total ice accretion times range between 45 seconds to 7 minutes. The FENSAP-ICE icing simulation system is utilized to carry out the CFD-Icing calculations in 2D and 3D.</div></div>
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it