Abstract
Several methods for predicting metal temperatures in a turbine engine are presented. Proper Orthogonal Decomposition (POD) is used to determine the system modes from temperature data sets from an engine mission. The coefficients of the system POD modes are used to identify the system dynamics. The linear state space model in conjunction with a multi-layer feedforward neural network is shown to produce superior prediction values for untrained temperature data when compared to those values produced by the state space model alone.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the ASME Turbo Expo 2006 - Power for Land, Sea, and Air |
| Pages | 561-569 |
| Number of pages | 9 |
| Volume | 2 |
| DOIs | |
| State | Published - 2006 |
| Event | 2006 ASME 51st Turbo Expo - Barcelona, Spain Duration: May 6 2006 → May 11 2006 |
Other
| Other | 2006 ASME 51st Turbo Expo |
|---|---|
| Country | Spain |
| City | Barcelona |
| Period | 5/6/06 → 5/11/06 |
All Science Journal Classification (ASJC) codes
- Engineering(all)
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Cite this
Widrich, M., Sinha, A., Suarez, E., & Cassenti, B. (2006). Applications of neural networks to the real-time prediction of metal temperatures in gas turbine engine components. In Proceedings of the ASME Turbo Expo 2006 - Power for Land, Sea, and Air (Vol. 2, pp. 561-569) https://doi.org/10.1115/GT2006-90317