An Improved CFD Approach for Ice-Accretion Prediction Using the Discrete Element Roughness Method
Bibliographic record
Abstract
Ice-shape prediction results are shown wherein Discrete-Element Roughness Method (DERM)-based CFD solutions are coupled with LEWICE to supplement the built-in heat transfer prediction module. This coupling produces multi-step ice-shape predictions. The effect of using the newer roughness-height distribution model of Han and Palacios rather than the roughness-height prediction of LEWICE is also gauged. DERM is used in an attempt to improve heat transfer predictions beyond the capability of a sand-grain-roughness model while only slightly increasing the computation time. LEWICE is the industry-standard ice growth prediction tool maintained by NASA. LEWICE is known to predict ice shapes very accurately within its validation envelope, but suffers lowered accuracy for icing conditions in the glaze regime. The predictions that result from the DERM-LEWICE coupling are compared with ice shapes generated in experiments from the Penn State Adverse Environment Rotor Test Stand (AERTS). It is observed that ice-shape predictions in the glaze-icing regime can be highly sensitive to the convective heat-transfer predictions.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 teacher head, 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".