Browsing by Author "Subramaniyan, Arun"

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Citation - WoS: 5
Citation - Scopus: 4
A Reduced Order Modeling Methodology for Steam Turbine Clearance Control Design
(American Society of Mechanical Engineers (ASME) infocentral@asme.org, 2017) Emrah Biyik; Fernando Javier D'Amato; Arun K. Subramaniyan; Changjie Sun; Subramaniyan, Arun; Sun, Changjie; Biyik, Emrah; D'Amato, Fernando J.
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 on 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. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 1
Reduced order modeling for clearance control in turbomachinery
(Institute of Electrical and Electronics Engineers Inc., 2016) Emrah Biyik; Fernando Javier D'Amato; Arun K. Subramaniyan; Changjie Sun; Subramaniyan, Arun; Sun, Changjie; Biyik, Emrah; D'Amato, Fernando J.
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.