Robust anti-icing curved surfaces based on buckling metallicplates
Bibliographic record
Abstract
Ice accretion remains a decade-old challenge due to the degradation in the performance of iced surfaces.Prior icephobic coatings have faced challenges in terms of durability due to exposure to severe icing conditions.Buckling Elastomer-like Anti-icing Metallic Surfaces (BEAMS) were previously presented as a robust system with low ice adhesion strength in realistic icing conditions.BEAMS consist of suspending partially confined, thin, flat metal sheets on an array of adhesives, facilitating a buckling instability in the sheet that causes easy ice removal from the surface.However, the gap of air between the metal sheet and substrate increases the flexibility of the system, and high-velocity droplet impact can then cause the metal sheet to contact the substrate.This leads to an order of magnitude increase in ice adhesion strength.To withstand high-velocity droplet impact, the flexural rigidity can be increased by making BEAMS curved, which is also more similar to realistic surfaces such as the leading edges of aircraft wings and wind turbine blades.However, increasing the flexural rigidity of BEAMS using curvature limits the out-of-plane deformation as compared to flat sheets, and this increases the sheet buckling resistance and consequently alters the ice removal mechanism.Here, we investigate the effect of BEAMS curvature on its ice adhesion strength in an icing wind tunnel.An ice adhesion strength of ice 3 kPa was observed due to increasing the compliance of the material used for suspending the thin metal sheet.It was also numerically studied how lateral torsional buckling in the suspension material of BEAMS altered the radius of curvature in the metal sheet and initiated an interfacial crack as the curvature of accreted ice remained unchanged.Further, the configuration of BEAMS was investigated to understand its effect on lateral torsional buckling.As a result, increasing the number of strips decreased the change in the radius of curvature.However, elastomer deformation would serve as a crack initiation site at the ice/metal interface above each strip.This numerical result was validated by the spin rig experiment measurements, where a higher number of strips results in lower ice adhesion strength.Accordingly, the extremely low ice adhesion strength of BEAMS, even on curved surfaces, was due to buckling instabilities either within the thin metal plates or the suspension material.Overall, BEAMS with extreme durability and low ice adhesion strength would become a promising anti-icing candidate material even for complex geometries in the aerospace industry.
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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 | 0.000 | 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".