A Prediction of Thermal Stress Profiles in the Steam Turbine Startup Phase Using Fourier Neural Operator

Abstract

Frequent startup and shutdown of steam turbines in recent operations have increased the importance of analyzing thermal stress induced by rapid temperature changes. In turbine startup, particularly during the synchronization phase, the surface temperature rises sharply while the core temperature lags behind, creating thermal gradients that lead to stress accumulation. However, the limited availability of measured data during these transient intervals poses a significant challenge for data-driven temperature prediction models, which typically require large-scale training datasets.

To address this issue, we propose a Fourier Neural Operator (FNO)-based framework to predict four temperature sequences during the synchronization phase using limited warm and hot startup data. The input consists of statistical features derived from two temperature-related and two steam-related variables observed during the preceding 3000 RPM holding phase. To ensure temporal consistency, all samples are padded to a unified sequence length.

The proposed FNO architecture leverages spectral convolution to capture global dependencies while maintaining local temporal resolution. Comparative evaluations with CNN, DNN, and LSTM models under identical training conditions demonstrate that the FNO consistently achieves higher predictive accuracy and robustness in five-fold cross-validation. These results indicate that the FNO-based framework is well-suited for modeling thermal dynamics in transient turbine operations where high-resolution data is scarce.