TY - GEN
T1 - Design of a Relational Database for Turbine Center Frames With Application for Geometry Optimization
AU - Staggl, Marian
AU - Sanz, Wolfgang
AU - Sterner, Thomas
N1 - Publisher Copyright:
Copyright © 2024 by ASME.
PY - 2024/8/28
Y1 - 2024/8/28
N2 - The flow path between high- and low-pressure turbines (TCF) is a key component of modern aero engines; the radial offset between the two stages and the small axial length result in a strong curvature and complex flow behavior. A solid database is an important foundation for design and further investigations, especially when applying modern artificial intelligence (AI) methods. Due to a long research history, the Institute for Thermal Turbomachinery and Machine Dynamics (ITTM) at Graz University of Technology owns an extensive TCF data collection. In order to reuse these data samples, a database application has been developed, providing the functionalities to normalize and store the data uniformly. The TCF data samples are mapped into a common parametric space during import, making them comparable and accessible to AI applications. Furthermore, statistical evaluation tools and an automated CFD export are available. In the second part of the paper, the database is used for an exemplary geometry optimization task. The approach is to train a surrogate model with data from the database and then use the model for optimization. The results agree with the findings of other authors, and the surrogate model's predictions coincide well with CFD results.
AB - The flow path between high- and low-pressure turbines (TCF) is a key component of modern aero engines; the radial offset between the two stages and the small axial length result in a strong curvature and complex flow behavior. A solid database is an important foundation for design and further investigations, especially when applying modern artificial intelligence (AI) methods. Due to a long research history, the Institute for Thermal Turbomachinery and Machine Dynamics (ITTM) at Graz University of Technology owns an extensive TCF data collection. In order to reuse these data samples, a database application has been developed, providing the functionalities to normalize and store the data uniformly. The TCF data samples are mapped into a common parametric space during import, making them comparable and accessible to AI applications. Furthermore, statistical evaluation tools and an automated CFD export are available. In the second part of the paper, the database is used for an exemplary geometry optimization task. The approach is to train a surrogate model with data from the database and then use the model for optimization. The results agree with the findings of other authors, and the surrogate model's predictions coincide well with CFD results.
KW - Artificial Intelligence
KW - Database
KW - Geometry Optimization
KW - Surrogate Model
KW - Turbine Center Frame
UR - http://www.scopus.com/inward/record.url?scp=85204292217&partnerID=8YFLogxK
U2 - 10.1115/GT2024-126557
DO - 10.1115/GT2024-126557
M3 - Conference paper
AN - SCOPUS:85204292217
T3 - Proceedings of the ASME Turbo Expo
BT - Turbomachinery - Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery
PB - American Society of Mechanical Engineers (ASME)
T2 - 69th ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition, GT 2024
Y2 - 24 June 2024 through 28 June 2024
ER -