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 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
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Applications of neural networks to the real-time prediction of metal temperatures in gas turbine engine components. / Widrich, Michael; Sinha, Alok; Suarez, Eva; Cassenti, Brice.
Proceedings of the ASME Turbo Expo 2006 - Power for Land, Sea, and Air. Vol. 2 2006. p. 561-569.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Applications of neural networks to the real-time prediction of metal temperatures in gas turbine engine components
AU - Widrich, Michael
AU - Sinha, Alok
AU - Suarez, Eva
AU - Cassenti, Brice
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33750831269&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33750831269&partnerID=8YFLogxK
U2 - 10.1115/GT2006-90317
DO - 10.1115/GT2006-90317
M3 - Conference contribution
AN - SCOPUS:33750831269
SN - 0791842371
SN - 9780791842379
VL - 2
SP - 561
EP - 569
BT - Proceedings of the ASME Turbo Expo 2006 - Power for Land, Sea, and Air
ER -