| Autor: |
Stefano Scarso, Stephan Staudacher, Christian Keller, Jürgen Mathes |
| Rok vydání: |
2022 |
| Zdroj: |
Volume 10B: Turbomachinery — Axial Flow Turbine Aerodynamics; Deposition, Erosion, Fouling, and Icing; Radial Turbomachinery Aerodynamics. |
| DOI: |
10.1115/gt2022-80923 |
| Popis: |
The severity of a flight mission depends on a wide range of operating conditions caused by aircraft, pilot and engine control system interaction. However, today’s availability of continuously sampled data offers the chance to relate the severity of a mission to the details of operation. In this work, 1 Hz measurements are taken from the DASHlink dataset of NASA. Other measurements not directly available in the dataset are synthesized through an engine model. Those are then used to calculate a severity parameter and a set of non-dimensional groups which are averaged for each flight. Based on those, a severity classification is performed on a sample of flight missions. The severity classification algorithm consists of a principal component analysis and a support-vector machine model. The accuracy and the robustness of the severity classification algorithm are evaluated for several flight missions. The contribution of each flight segment to the severity of the entire flight mission is discussed. The mean flight mission severity for the engine of highest and lowest operating time on a particular aircraft are compared and results are discussed. Finally, the ability to identify more than two severity groups is discussed. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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