This study investigates mechanism-based modeling and simulation of a single-shaft heavy-duty industrial gas turbine. Taking the PG9171E gas turbine as the case study, component-level steady-state and dynamic models are developed. The steady-state model is established using the constant mass flow (CMF) method. For dynamic modeling, both the CMF approach and the inter-component volume (ICV) approach are implemented to enable a comparative assessment of the two methods. On the basis of the steady-state model, an improved Dung Beetle Optimization (DBO) algorithm is proposed to perform model correction using measured operational data from the gas turbine. After model correction, the maximum relative error between the simulated results and the measured operating data is reduced to 1.01 × 10⁻⁵%. Following high-accuracy model correction, sensitivity analysis and a comparative dynamic study are conducted for the two dynamic modeling approaches. The results indicate that the most influential sensitivity parameter is the rotor rotational inertia, followed by the virtual volume of the combustor. Moreover, the primary discrepancy between the ICV and CMF approaches arises from differences in the operating trajectories on component characteristic maps. The ICV-based model exhibits a pronounced response lag; however, it requires less computational time than the CMF-based model, making it more suitable for rapid engineering simulation and practical applications.

To address the challenges of limited access to full-life-cycle data and insufficient labeled samples in gas turbine health management, a Bidirectional Long Short-Term Memory-Domain Adversarial Neural Network (BiLSTM-DANN) is adopted to achieve cross-domain health assessment for gas turbines. The model extracts temporal health features with a two-layer BiLSTM network and integrates DANN to achieve cross-domain feature alignment, thereby learning domain-invariant health representations. The simulation results demonstrate that the BiLSTM-DANN model outperforms the traditional BiLSTM and DCNN models on both the FD001 and FD003 datasets of C-MAPSS. Health assessment tests conducted on real gas turbine operation datasets indicate that the BiLSTM-DANN model can effectively depict the long-term operational health evolution trend of the entire unit and accurately reflect the health changes of the gas turbine before and after water washing. Therefore, the method studied in this paper provides a transferable solution for assessing the health of the entire gas turbine under conditions of scarce labels.