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.

本研究通过使用深度神经网络替代物和多目标优化算法，评估了单目标和多目标优化算法在弯头式导管设计中的应用，并提出了一种计算效率高的优化工作流。结果表明，在单目标优化中，基于成功历史的自适应差分进化算法与基于分解的多目标进化算法是最佳选择；而多目标优化中，采用多标准决策分析方法能够获取最佳多目标结果，对于压力恢复系数和阻力系数分别提高了 1.5% 和 17%。预测值与数值结果的差异分别小于 0.5% 和 3%。随着可再生能源需求的增加，数据驱动的优化工作流在全球可持续发展的背景下将变得越来越重要。