Machine learning-based surrogate model for accelerating simulation-driven optimisation of hydropower Kaplan turbine

In this work, a data-driven technique is proposed for efficient design exploration and optimisation of the Kaplan turbine. To avoid the curse of dimensionality, the proposed approach commences with the extraction of latent features of a parametric design space, which form a lower-dimensional subspace accumulating maximum geometric variability of designs. Afterwards, this subspace is exploited for the construction of a Gaussian Process-based surrogate model using an adaptive training strategy to infer the relative-tangential velocities at the leading and trailing edges of the turbine. The training strategy is structured on a high-fidelity sampling approach to ensure a notable prediction accuracy with a few training samples. After training, the surrogate model is integrated with an optimiser to explore the subspace for an optimal design and to determine the sensitivity of design parameters. The results showed that the optimal design generated with the proposed method increases the efficiency of the initial design from 56.98% to 90.73% at a significantly low computational cost. Finally, the convergence performance is verified with different experimentation and its accuracy to extract latent features and to predict the relative-tangential velocity is demonstrated via a comparative study in which different state-of-the-art approaches are compared with the proposed approach.