Solving characteristic parameters of heavy-duty gas turbines using parameter estimation method
Jinling Chi,
Chang Wang,
Yangxue He,
Chenxu Gou and
Zhao Wang
PLOS ONE, 2025, vol. 20, issue 10, 1-20
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.
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0333661
DOI: 10.1371/journal.pone.0333661
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