Abstract
The global drive towards renewable energy is imposing challenging operating requirements on power turbines. Flexible load-leveling applications must accept more frequent and demanding start-stop cycles. Full transient analyses are too computationally expensive for real-time simulation across all operating regimes so monitoring relies on sparse physical measurements.
Alone, these sparse data lack the fidelity for real-time prediction of a complex thermal field.
A new hybrid methodology is proposed, coupling data across a range of fidelities to bridge the limitations in the individual analyses. Combining several fidelity methods in parallel: low-order models, corrected by real-time physical measurements, are calibrated with high-fidelity simulations. The multi-faceted hybrid approach enables the real-time speed of low-order analysis at high resolution. This paper series develops the critical enabling features of the hybrid method.
Real-time cross-fidelity data transition is fundamental to the hybrid methodology. A novel neural network autoencoder method is presented, facilitating complex thermal profile reconstruction.
Uncovering a compressed latent space, autoencoders leverage underlying data features for fast simulation. Coupled with a dynamic mask and top-k selection, thermal probe placement can be automatically optimized. The autoencoder method is demonstrated on a turbine casing, reconstructing over 500 hours of transient operation in real-time, whilst reducing the required number of measurements by half.
