Reduced order modeling for clearance control in turbomachinery

Loading...
Publication Logo

Date

2016

Authors

Emrah Biyik
Fernando Javier D'Amato
Arun K. Subramaniyan
Changjie Sun

Journal Title

Journal ISSN

Volume Title

Publisher

Institute of Electrical and Electronics Engineers Inc.

Open Access Color

Green Open Access

Yes

OpenAIRE Downloads

OpenAIRE Views

Publicly Funded

No
Impulse
Average
Influence
Average
Popularity
Average

Research Projects

Journal Issue

Abstract

Finite element models (FEMs) are extensively used in the design optimization of utility scale steam turbines. As an example by simulating multiple startup scenarios of steam power plants engineers can obtain turbine designs that minimize material utilization and at the same time avoid the damaging effects of large thermal stresses or rubs between rotating and stationary parts. Unfortunately FEMs are computationally expensive and only a limited amount of simulations can be afforded to get the final design. For this reason numerous model reduction techniques have been developed to reduce the size of the original model without a significant loss of accuracy. When the models are nonlinear as is the case for steam turbine FEMs model reduction techniques are relatively scarce and their effectiveness becomes application dependent. Although there is an abundant literature on model reduction for nonlinear systems many of these techniques become impractical when applied to a realistic industrial problem. This paper focuses in a class of nonlinear FEM characteristic of thermo-elastic problems with large temperature excursions. A brief overview of popular model reduction techniques is presented along with a detailed description of the computational challenges faced when applying them to a realistic problem. The main contribution of this work is a set of modifications to existing methods to increase their computational efficiency. The methodology is demonstrated on a steam turbine model achieving a model size reduction by four orders of magnitude with only 5% loss of accuracy with respect to the full order FEMs. These practical implementations enable the calculation of multiple additional design scenarios. © 2017 Elsevier B.V. All rights reserved.

Description

Keywords

Model Reduction, Nonlinear Models, Proper Orthogonal Decomposition, Steam Turbine, Computational Efficiency, Nonlinear Systems, Principal Component Analysis, Steam, Steam Power Plants, Steam Turbines, Computational Challenges, Material Utilization, Model Reduction, Model Reduction Techniques, Model Size Reductions, Non-linear Model, Proper Orthogonal Decompositions, Temperature Excursions, Finite Element Method, Computational efficiency, Nonlinear systems, Principal component analysis, Steam, Steam power plants, Steam turbines, Computational challenges, Material utilization, Model reduction, Model reduction techniques, Model size reductions, Non-linear model, Proper orthogonal decompositions, Temperature excursions, Finite element method, Model Reduction, Proper Orthogonal Decomposition, Nonlinear Models, Steam Turbine

Fields of Science

0209 industrial biotechnology, 02 engineering and technology, 0101 mathematics, 01 natural sciences

Citation

WoS Q

Scopus Q

OpenCitations Logo
OpenCitations Citation Count
1

Source

2016 IEEE Conference on Control Applications CCA 2016

Volume

Issue

Start Page

1143

End Page

1148
PlumX Metrics
Citations

Scopus : 0

Captures

Mendeley Readers : 7

Google Scholar Logo
Google Scholar™
OpenAlex Logo
OpenAlex FWCI
0.0

Sustainable Development Goals