Emrah BiyikFernando Javier D'AmatoArun K. SubramaniyanChangjie SunSubramaniyan, ArunSun, ChangjieBiyik, EmrahD'Amato, Fernando J.2025-10-06201707424795, 152889190742-47951528-891910.1115/1.40360622-s2.0-85020054078https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020054078&doi=10.1115%2F1.4036062&partnerID=40&md5=8b625eb62e455368eb2a2857b248d3d0https://gcris.yasar.edu.tr/handle/123456789/9654https://doi.org/10.1115/1.4036062Finite 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.Englishinfo:eu-repo/semantics/closedAccessClearance Control, Model Reduction, Nonlinear Models, Proper Orthogonal Decomposition, Steam Turbine, Computational Efficiency, Finite Element Method, Nonlinear Systems, Principal Component Analysis, Steam, Steam Power Plants, Clearance Control, Computational Challenges, Model Reduction, Model Reduction Techniques, Model Size Reductions, Non-linear Model, Proper Orthogonal Decompositions, Temperature Excursions, Steam TurbinesComputational efficiency, Finite element method, Nonlinear systems, Principal component analysis, Steam, Steam power plants, Clearance control, Computational challenges, Model reduction, Model reduction techniques, Model size reductions, Non-linear model, Proper orthogonal decompositions, Temperature excursions, Steam turbinesModel ReductionProper Orthogonal DecompositionClearance ControlNonlinear ModelsSteam TurbineArticle