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research-article

A Reduced Order Modeling Methodology for Steam Turbine Clearance Control Design

[+] Author and Article Information
Emrah Biyik

Assistant Professor Department of Energy Systems Engineering Yasar University Izmir, Turkey
emrah.biyik@yasar.edu.tr

Fernando J. D'Amato

Principal Engineer GE Global Research Niskayuna, NY
damato@ge.com

Arun Subramaniyan

Manager, Structures Lab GE Global Research Niskayuna, NY
subramaa@ge.com

Changjie Sun

Senior Engineer GE Global Research Niskayuna, NY
sunc@ge.com

1Corresponding author.

ASME doi:10.1115/1.4036062 History: Received January 11, 2017; Revised February 07, 2017

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 4\% loss of accuracy with respect to the full order FEMs.

Copyright (c) 2017 by ASME
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