  This study aims to provide a comprehensive assessment of single-objective and
multi-objective optimisation algorithms for the design of an elbow-type draft
tube, as well as to introduce a computationally efficient optimisation
workflow. The proposed workflow leverages deep neural network surrogates
trained on data obtained from numerical simulations. The use of surrogates
allows for a more flexible and faster evaluation of novel designs. The success
history-based adaptive differential evolution with linear reduction and the
multi-objective evolutionary algorithm based on decomposition were identified
as the best-performing algorithms and used to determine the influence of
different objectives in the single-objective optimisation and their combined
impact on the draft tube design in the multi-objective optimisation. The
results for the single-objective algorithm are consistent with those of the
multi-objective algorithm when the objectives are considered separately.
Multi-objective approach, however, should typically be chosen, especially for
computationally inexpensive surrogates. A multi-criteria decision analysis
method was used to obtain optimal multi-objective results, showing an
improvement of 1.5% and 17% for the pressure recovery factor and drag
coefficient, respectively. The difference between the predictions and the
numerical results is less than 0.5% for the pressure recovery factor and 3% for
the drag coefficient. As the demand for renewable energy continues to increase,
the relevance of data-driven optimisation workflows, as discussed in this
study, will become increasingly important, especially in the context of global
sustainability efforts.
