Bibliographic record
Abstract
Ice protection systems were installed on DC-3s, B-29s and other aircraft during World War II. Initially ice detection was not considered a requirement for aircraft safety. One of the earliest ice detectors installed on a production aircraft (C-130) was pneumatic. Subsequently many other technologies have been applied to detect the presence of meteorological icing. Ice detectors employing visible light, infrared light, alpha radiation, natural resonance frequency, capacitance, speed of sound, heat of fusion, and microwaves have all been developed over the years. This paper will review the different ice detection technologies and explore some of the similarities, differences, benefits and drawbacks for the purposes of performing their intended function. One of the drivers for developing new ice detection technology has been aircraft icing accidents and incidents. For example, a number of MD-80 accidents caused by engine flame-outs during take-off have prompted development of surface-based ice detection solutions that could detect ice accretion on wing surfaces prior to take-off. Other accidents such as the Comair CRJ crash in Fredericton, New Brunswick, Canada and the American Eagle ATR-42/72 crash in Roselawn, Indiana have spurred research into different ice detection technologies related to the Ludlam Limit and Supercooled Large Droplets (SLD) respectively. This paper will review various aircraft accidents and incidents that have had an impact on ice detection technology. While no Federal Aviation Regulations (FARs) exist today which deal specifically with ice detection systems, there has been some industry guidance material published which provides information on the design of ice detection systems. Due to the increased industry awareness of aircraft icing issues, a number of industry committees such as EUROCAE and IPHWG have been established to develop new guidance material and proposed regulation changes related to ice detection systems. The release of the ED-103/104 Minimum Operational Performance Specifications and other guidance material such as the recent updates to AC 20 73 published by the FAA have had an impact on the development and certification of ice detection systems. This paper will review the regulation changes that impact ice detection system design and certification, including a discussion regarding the ability of ice detection technologies to meet these requirements and to be certified as a Primary system.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".