Parametric design-based multi-objective optimisation for high-pressure turbine disc

Dongliang Cui, Guoqi Feng, Ping Zhou and Yajun Zhang

International Journal of Production Research, 2017, vol. 55, issue 17, 4847-4861

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.

Date: 2017
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DOI: 10.1080/00207543.2016.1259669

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