Machine learning-driven high-fidelity ensemble surrogate modeling of Francis turbine unit based on data-model interactive simulation

Abnormal mechanical properties of Francis turbine units (FTUs) lead to unstable output power and operation fault, and may cause catastrophic hazards. At present, computational fluid dynamics (CFD) and machine learning (ML) methods are popular in predicting FTUs ' mechanical behaviors, but there are limitations as follows: 1) CFD simulations focus on rated power. The water head and active power variability are neglected, leading to sparse coverage of FTUs operation conditions. 2) Numerous data are required to generate high-fidelity prediction results, occupying vast resources with low efficiency. 3) Computation software and statistical tools may develop inherent errors in generated values, leading to a considerable deviation in final prediction results. In this study, a high-fidelity data-model interactive ensemble surrogate model for FTUs ' mechanical behaviors prediction is proposed. First, to form a comprehensive operation conditions sample space, the monitoring data of various operation conditions are collected and clustered by using density-based spatial clustering of application with noise (DBSCAN). Next, to reduce the resource consumption, a small sample space is formed by Latin hypercube sampling (LHS) based on the cluster weights, then sent into the physical model for data-model interactive simulation. Subsequently, on the premise of maintaining the data characteristic, simulated and monitoring data are mixed to weaken the effect of inherent errors. Thus, an ensemble surrogate model, capable of feature extraction and regression analysis, is proposed to predict FTUs ' mechanical behaviors. The experimental results show that the proposed method obtains great prediction accuracy in various conditions, and it outperforms in resource consumption while enables the high-fidelity prediction. Finally, a comparison experiment shows that the ensemble model exhibits significantly lower relative prediction errors and converges more rapidly.