Solving characteristic parameters of heavy-duty gas turbines using parameter estimation method

J Jinling Chi C Chang Wang Y Yangxue He C Chenxu Gou Z Zhao Wang (State Key Laboratory of Bioinspired Interfacial Materials Science, State and Local Joint Engineering Laboratory for Novel Functional Polymeric Materials, Jiangsu Key Laboratory of Advanced Functional Polymer Materials, Suzhou Key Laboratory of Macromolecular Design and Precision Synthesis, College of Chemistry, Chemical Engineering and Materials Science)

Abstract

In gas turbine simulation, precise parameterization of components is essential for reliable performance prediction, yet manufacturers usually provide only limited operational data. To address this issue, this study proposes a modeling approach based on limited operational parameters and applies it to a 9FA heavy-duty gas turbine. The framework employs maximum likelihood parameter estimation within an inverse problem formulation, combined with a modular methodology to reconstruct compressor characteristic curves and establish a full-condition mathematical model. The maximum relative error between the predicted and actual values for the discharge flow rate and discharge temperature of the compressor under steady-state standard conditions is no greater than 0.2%. Simulation results show that the relative errors for compressor isentropic efficiency and combustion efficiency are 0.34% and 0.1%, with parameter prediction errors below 0.5%. The relative errors for combustion pressure loss coefficient is 2.86%. Additional cross-validation using inverse problem methods further confirms the accuracy of the proposed approach under limited data conditions. These findings demonstrate that the method provides a valuable approach for full-condition gas turbine modeling and performance analysis.

Article Details

Journal PLoS ONE
Volume / Issue Vol. 20, Issue 10
Published October 08, 2025
Pages e0333661
ISSN 1932-6203
Publisher Public Library of Science

Journal Info

PLoS ONE

Public Library of Science

ISSN: 1932-6203 Open Access Health Sciences

Authors (5)

J

Jinling Chi

C

Chang Wang

Y

Yangxue He

C

Chenxu Gou

Z

Zhao Wang

State Key Laboratory of Bioinspired Interfacial Materials Science, State and Local Joint Engineering Laboratory for Novel Functional Polymeric Materials, Jiangsu Key Laboratory of Advanced Functional Polymer Materials, Suzhou Key Laboratory of Macromolecular Design and Precision Synthesis, College of Chemistry, Chemical Engineering and Materials Science