Development of a Non-Intrusive Ultrasound Ice Accretion Sensor to Detect and Quantify Ice Accretion Severity
Bibliographic record
Abstract
Supercooled liquid water and ice crystals are atmospheric icing conditions that degrade flight safety of aircraft by affecting the performance of the airframe, air data probes and engines. A key way this threat is mitigated is by using icing detectors on the aircraft to either detect the environment or to detect when ice is accreted on a surface. This information allows the aircraft anti-icing systems to be enabled or have the aircraft exit the environment. This paper discusses a non-intrusive technology developed at NRC that can detect ice accretion on a surface and quantify its severity. The technology features a custom thin-film ultrasound transducer made to withstand in-flight conditions, combined with proprietary signal processing algorithms. Initial development was carried out in environmental chamber and icing wind tunnel tests, exposing the prototype sensors to a range of temperature, humidity, altitude and icing conditions. This technology, referred to as the Ultrasound Ice Accretion Sensor (UIAS), was then implemented in a collaborative NASA/Honeywell/ICC ice crystal icing test of a Honeywell/Lycoming turbofan aircraft engine, model ALF502. This was a full scale, altitude engine test which simulated a wide range of inflight ice crystal icing conditions. The UIAS’s were not only effective in detecting when ice had accreted but were also able to estimate the severity of the icing. This detection happened in a very short period of time; much sooner than the early performance warning indicators seen from engine and test cell instrumentation, which is key in providing the aircraft and pilot time to address the icing threat.
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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".