Aircraft/Aeroengine Icing Physics and Innovative Strategies for Inflight Icing Mitigation
Bibliographic record
Abstract
Aircraft/Aero-engine icing is widely recognized as a significant hazard to aircraft operations in cold weather. The speak will introduce his recent research in conducting a comprehensive experimental campaign to elucidate the underlying aircraft/aeroengine icing physics. By leveraging the unique Icing Research Tunnel available at Iowa State University (i.e., ISU-IRT), comprehensive investigations are conducted to examine the important micro-physical processes pertinent to aircraft/aeroengine icing phenomena. A suite of advanced flow diagnostic techniques, including molecular tagging velocimetry and thermometry (MTV&T), digital image projection (DIP), and high-speed infrared (IR) imaging thermometry techniques, are developed and applied to quantify water droplet impinging dynamics, transient behaviors of wind-driven water runback flows, unsteady heat transfer and dynamic solidification processes over airfoil/wing surfaces. Anti-/de-icing performances of various "state-of-the-art" hydro-/ice-phobic coatings/surfaces, including a lotus-inspired superhydrophobic surface (SHS) and a pitcher-plant-inspired Slippery Liquid-Infused Porous Surfaces (SLIPS), are evaluated quantitatively under different icing conditions (i.e., ranged from dry rime icing to wet glaze icing conditions). The recent research efforts on unmanned-aerial-system (UAS) icing will also be introduced briefly. The findings derived from the icing physics studies are extremely helpful to improve current icing accretion models and to develop novel, effective anti-/de-icing strategies to ensure safer and more efficient operation of aircraft/aeroengined in
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".