Textured panel for ice-induced vibration mitigation and reduction of associated average load
Bibliographic record
Abstract
Based on understandings of the mechanisms that constitute ice-spallation phenomena, gleaned from various ice-crushing lab tests conducted at NRC, it was determined that it should be possible to incorporate low-profile textural patterns/components into the design of a structure's faces that could disrupt the spalling process. This was confirmed in earlier small-scale lab tests that showed that during ice crushing when using NRC's Blade Runners technology (Patent US 9,181,670 B2), instead of a series of large-amplitude spallation-induced sawtooth load spikes, a considerably greater number of much smaller ice spallations, and associated load-spikes, were produced due to the textured surface's ability to initiate spallation. While the average load in that case basically remained the same with or without the surface technology, the amplitude of the sawteeth load spikes was greatly reduced. Consequently, due to the invariance of ice properties in the brittle regime over a wide range of scale, the Blade Runners technology has the potential capability to reduce the amplitude of the sawtooth load pattern that develops due to repetitive ice spallations that occur when a moving ice sheet crushes against an offshore platform, such as a wind turbine or oil/environmental-monitoring platform. Here, we present a new technology that uses an essential aspect of the physics underlying the earlier Blade Runners technology, i.e., spallation initiation/disruption, but uses it in a manner requiring less surface modification than the former technology stipulates, to not only reduce the amplitude of the spallation-induced sawtooth load pattern, but also to reduce the average ice load. Data from lab tests using the new technology are discussed.
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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".