1. Parametric design-based multi-objective optimisation for high-pressure turbine disc.
- Author
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Cui, Dongliang, Feng, Guoqi, Zhou, Ping, and Zhang, Yajun
- Subjects
TURBINE parts ,TURBINE design & construction ,RADIAL distortion ,COMPUTER-aided engineering ,GENETIC algorithms ,ARTIFICIAL neural networks ,FINITE element method - Abstract
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. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
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