Parametric design-based multi-objective optimisation for high-pressure turbine disc
针对高压涡轮盘质量和径向变形的多目标优化问题，提出一种基于参数化模板和噪声虚拟样本的数据驱动框架，用神经网络作为适应度函数加速优化，降低工程难度。
Mass and radial deformation are of great importance for a high-pressure turbine disc (HPTD). However, computational cost of computer-aided engineering (CAE) is too high to optimise the mutually restricted objectives. A parameterisation-based method is proposed to speed the optimisation process of HPTD: ‘body-flange’-based parametric template is used to generate CAE samples; noise-based virtual samples are implemented to enlarge the training set, a cost-effective neural network is used as fitness function of non-dominated sorting genetic algorithm-II for optimisation whose initial population is the combination of different sample sets. Experiment results show that the proposed data-driven framework reduces the engineering difficulty of multi-objective optimisation, and it has high popularisation value for optimisation of other complex products.