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

Author

Listed:
  • Dongliang Cui
  • Guoqi Feng
  • Ping Zhou
  • Yajun Zhang

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.

Suggested Citation

  • Dongliang Cui & Guoqi Feng & Ping Zhou & Yajun Zhang, 2017. "Parametric design-based multi-objective optimisation for high-pressure turbine disc," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 4847-4861, September.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:17:p:4847-4861
    DOI: 10.1080/00207543.2016.1259669
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