Compressors are one of the three major components of gas turbines, and their characteristic curves are used to analyze off-design performance. How to infer the characteristic curve based on different data is an important research topic. In this paper, PG9351FA gas turbine is taken as the research object. Two methods, artificial neural network and parameter estimation, are used to predict its characteristic curve, and the prediction accuracy and application conditions of the two methods are discussed. This article compares the two methods from the perspectives of known speed characteristic curve regression and unknown speed characteristic curve inference, analyzes the impact of sample size and sample error on their inference results, and quantitatively analyzes the error through statistical methods such as calculating the mean square deviation of the data. The application scope and conditions of different methods are provided. The research results show that the method based on neural network to infer the characteristic curve has high accuracy and is suitable for the prediction of known and unknown speed characteristic curves under sufficient data, but not for the prediction of unknown side curves. The elliptic equation fitting method based on parameter estimation has a slightly lower accuracy in processing the nearly vertical compressor characteristic curve, but it can be used as an effective and reliable method to infer the compressor characteristic curve in the case of a small amount of data. The modulization method based on parameter estimation has high accuracy and is applicable to the estimation of complete characteristic curve from partial data of known characteristic curve. In this paper, the application scope and conditions of these two methods are determined, which can provide reference for engineering practice.

Developing an advanced gas turbine (GT) can have a significant impact on the thermodynamic performance of the GT combined cycle (GTCC) and the integrated gasification combined cycle (IGCC). Owing to the importance of system evaluation, it is essential to find ways to accurately predict the performance of state-of-the-art GTs through an accurate and reliable turbine cooling model. Thus, a quasi-one-dimensional (quasi-1D) turbine cooling model, which considers the film cooling and thermal barrier coating (TBC), is established and verified in this work. The model parameters, η_c,∞, η_t,∞, Z, r_fc, Bi_tbc and T_bmx, which represent compressor and turbine efficiencies, convective cooling technology level, film cooling coefficient, Biot number of TBC and allowed maximal temperature of turbine blade, respectively, are confirmed. Reference is made to current advanced GTs during the modeling process. More importantly, the impacts of combustor exit temperature (T_cot), pressure ratio (PR), and cooling approaches on the thermodynamic performance of GTCC are all evaluated based on the quasi-1D turbine cooling model. Results show that the GTCC net efficiency does not always increase along with the increase of T_cot, under the premise of maintaining the cooling technology. The optimal PR, when the GTCC efficiency peaks, exists at each T_cot. In addition, the net efficiency of GTCC, which adopts a closed-loop steam cooling, is improved by about 2 to 3 percentage points compared with that of the conventional open-loop air cooling approach. Furthermore, the IGCC thermodynamic performance potential is greatly affected by GTCC. It is estimated that the net efficiency of IGCC integrated with 1700 °C-class GT has a total improvement of about 6 percentage points relative to that of IGCC integrated with a GE PG9351FA GT.

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.