Hostname: page-component-7c8c6479df-xxrs7 Total loading time: 0 Render date: 2024-03-29T04:44:09.468Z Has data issue: false hasContentIssue false

Potential of surrogate modelling in compressor casing design focussing on rapid tip clearance assessments

Published online by Cambridge University Press:  13 September 2021

T. Schmidt*
Affiliation:
Technische Universität München Institute for Turbomachinery and Flight Propulsion 85748 Garching Germany
V. Gümmer
Affiliation:
Technische Universität München Institute for Turbomachinery and Flight Propulsion 85748 Garching Germany
M. Konle
Affiliation:
MTU Aero Engines AG 80995 München Germany

Abstract

Losses induced by tip clearance limit decisive improvements in the system efficiency and aerodynamic operational stability of aero-engine axial compressors. The tendency towards even lower blade heights to compensate for higher fluid densities aggravates their influence. Generally, it is emphasised that the tip clearance should be minimised but remain large enough to prevent collisions between the blade tip and the casing throughout the entire mission. The present work concentrates on the development of a preliminary aero-engine axial compressor casing design methodology involving meta-modelling techniques. Previous research work at the Institute for Turbomachinery and Flight Propulsion resulted in a Two-Dimensional (2D) axisymmetric finite element model for a generic multi-stage high-pressure axial compressor casing. Subsequent sensitivity studies led to the identification of significant parameters that are important for fine-tuning the tip clearance via specific flange design. This work is devoted to an exploration of the potential of surrogate modelling in preliminary compressor casing design with respect to rapid tip clearance assessments and its corresponding precision in comparison with finite element results. Reputed as data-driven mathematical approximation models and conceived for inexpensive numerical simulation result reproduction, surrogate models show even greater capacity when linked with extensive design space exploration and optimisation algorithms.

Compared with high-fidelity finite element simulations, the reductions obtained in computational time when using surrogate models amount to 99.9%. Validated via statistical methods and dependent on the size of the training database, the precision of surrogate models can reach down to the range of manufacturing tolerances. Subsequent inclusion of such surrogate models in a parametric optimisation process for tip clearance minimisation rapidly returned adaptions of the geometric design variables.

Type
Research Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Hupfer, A. Spalte in Fluggasturbinen: Ursachen, Folgen und Optimierungs-strategien am Beispiel des Turboverdichters, Verlag Dr. Hut, Munich, Germany, 2019.Google Scholar
Kern, M., Horn, W., Hofbauer, A. and Loy, B. Proof of Concept of a Mechanical Active Clearance Control System, European Workshop on New Aero Engine Concepts, Munich, Germany, 2010.Google Scholar
Guinet, C. and Hupfer, A. Stufenentwurf mit stabilisierenden Gehäuseeinbauten, Internal Report, Institute for Turbomachinery and Flight Propulsion, Technische Universität München, Germany, 2015.Google Scholar
Schmidt, T., Gümmer, V.and Konle, M. Quantification of the Effect of Circumferential Repeated 3D Features on Radial Casing Displacement Focusing Model Simplification: Part I, ICMIE 122, 8th International Conference on Mechanics and Industrial Engineering (ICMIE’19), Lisbon, Portugal, 2019.CrossRefGoogle Scholar
Schmidt, T., Gümmer, V.and Konle, M. Quantification of the Effect of Circumferential Repeated 3D Features on Radial Casing Displacement Focusing Model Simplification: Part II, Internal Report, Institute for Turbomachinery and Flight Propulsion, Technische Universität München, Germany, 2019.CrossRefGoogle Scholar
Schmidt, T., Hupfer, A., Gümmer, V.and Konle, M. Sensitivity Analysis of Flange Design Parameters for Specific Radial Displacement Adaption of an Axial Compressor Casing, Internal Report, Institute for Turbomachinery and Flight Propulsion, Technische Universität München, Germany, 2019.Google Scholar
Waschka, W.K., Rüd, K., Humhauser, W., Metscher, M. and Michel, A. ATFI-HDV: Design of a new 7 stage innovative compressor for 10-18klbf thrust, ISABE-2005-1266, XVII International Symposium on Air Breathing Engines, Munich, Germany, 2005.Google Scholar
Bunell, S., Thelin, C., Gorell, S., Salmon, J., Ruoti, C. and Hepworth, A. Rapid Visualization of Compressor Blade Finite Element Models Using Surrogate Modeling, GT2018-77188, ASME Turbo Expo 2018, Oslo, Norway, 2018.CrossRefGoogle Scholar
Zhang, M., Guo, W., Li, L., Yang, F. and Yue, Z. Multidisciplinary Design and Multi-Objective Optimization on Guide Fins of Twin-Web Disk Using Kriging Surrogate Model. Structural and Multidisciplinary Optimization, 2017, 55 (1), pp 361373.CrossRefGoogle Scholar
Geller, M., Schemmann, C. and Kluck, N. Optimization of the Operation Characteristic of a Highly Stressed Centrifugal Compressor Impeller Using Automated Optimization and Metamodelling Methods, GT2017-63262, ASME Turbo Expo 2017, Charlotte, North Carolina, USA, 2017.CrossRefGoogle Scholar
Heap, R.C., Hepworth, A.I, and Jensen, C.G. Real-time visualization of finite element models using surrogate modeling methods. J. Comput. Inform. Sci. Eng., 2015, 15 (1), p 011007.CrossRefGoogle Scholar
Walther, B. and Nadarajah, S. Optimum shape design for multirow turbomachinery configurations using a discrete adjoint approach and an efficient radial basis function deformation scheme for complex multiblock grids. J. Turbomach., 2015, 137 (8), p 081006.CrossRefGoogle Scholar
Zimmermann, M. and Krischer, L. Multidisciplinary Design Optimization (MDO), Lecture Script, Laboratory of Product Development and Lightweight Design, Department of Mechanical Engineering, Technische Universität München, 2018.Google Scholar
Rasmussen, C.E. and Williams, C.K.I. Gaussian Processes for Machine Learning, The MIT Press, Cambridge, MA, USA, 2006.Google Scholar
Yang, X., Tartakovsky, G.D. and Tartakovsky, A.M. Physics-Informed Kriging: A Physics-Informed Gaussian Process Regression Method for Data-Model Convergence, arXiv preprint, arXiv:1809.03461, 2018.Google Scholar
Jin, R., Chen, W. and Simpson, T.W. Comparative studies of metamodeling techniques under multiple modelling criteria. Struct. Multidisc. Optim., 2001, 23 (1), pp 113.CrossRefGoogle Scholar
Matlab Documentation, Last Online Access: 22.01.2021, Available: https://de.mathworks.com/help/stats/gaussian-process-regression-models.htmlGoogle Scholar
Bretschneider, S., Rothe, F., Rose, M.G. and Staudacher, S. Compressor Casing Preliminary Design Based on Features, GT2008-50102, ASME Turbo Expo 2008, Berlin, Germany, June 913, 2008.CrossRefGoogle Scholar
(engl.: Develop-ment and Design of Aero Engines), , 1989.Google Scholar
Department of Defense, Military Handbook-Metallic Materials and Elements for Aerospace Vehicle Structures: MIL-HDBK-5H, USA, 1998.Google Scholar
Davis, J.R. Nickel, Cobalt, and Their Alloys, ASM international, Almere, The Netherlands, 2000.Google Scholar
Special Metals, Inconel® alloy, Last Online Access: 12.4.2019, Available: www.specialmetals.com/assets/smc/documents/alloys/inconel/inconel-alloy-718.pdf.Google Scholar