Browse Topic: Elastomers
This paper presents an original method that takes advantage of existing large in-service flight data, damper load Machine Learning models as well as the inventory of degraded dampers (elastomeric part), to link the estimated loads and operational conditions to damper degradation cases. The Machine Learning models are trained on flight test campaigns data, and then applied on in-service helicopter data to estimate damper loads as a function of flight parameters. The estimated load history is then used as an input to generate engineering load indicators. These latter, jointly with operational and usage data, are correlated with the reported dampers' degradation observations. Finally, an explainability mechanism is investigated to better understand the Machine Learning models inferences, opening perspectives towards precise damper degradation root causes identification. The obtained results are promising, showing that the occurrence of damper degradation correlates with load history and
In the winter months of January-March 2019, two Bell 525 test aircraft completed cold weather testing at Yellowknife Canada, some 1900 nm from Bell’s Flight Research Center in Arlington, TX. Testing was aimed at demonstrating aircraft stability, performance, and flight characteristics at extreme temperatures as required by CFR Part 29. Since regulations only permit limited temperature extrapolation, the cold temperature tests must include the limit of forward speed in a dive (VNE), and assessments of performance, controllability, autorotation, and static stability. This paper describes some of the unique environmental conditions and factors that any rotorcraft development program could experience in cold weather testing. The paper also gives a technical description of the required testing, where arctic conditions reached as low as -40° F or C (the temperature scales are the same at this temperature). Testing exposed the aircraft to overnight cold-soaks that brought fluids, seals
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