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.

翻译：本研究旨在对肘形尾水管设计中的单目标和多目标优化算法进行全面评估，并引入一种计算高效的优化工作流程。所提出的工作流程利用基于数值模拟数据训练的深度神经网络代理模型。代理模型的使用使得对新设计的评估更加灵活且快速。基于线性缩减的成功历史自适应差分进化算法和基于分解的多目标进化算法被识别为性能最佳的算法，用于确定单目标优化中不同目标的影响及其在尾水管设计中的综合作用。单目标算法的结果与单独考虑目标时的多目标算法结果一致。然而，通常应选择多目标方法，尤其是对于计算成本较低的代理模型。采用多准则决策分析方法获得了最优的多目标结果，结果表明压力恢复系数和阻力系数分别提高了1.5%和17%。预测值与数值结果之间的差异在压力恢复系数上小于0.5%，在阻力系数上小于3%。随着对可再生能源需求的持续增长，本研究所探讨的数据驱动优化工作流程在全球可持续发展背景下将变得越来越重要。