Knowledge-based turbomachinery design system via a deep neural network and multi-output Gaussian process

作者：

Highlights：

• A knowledge-based turbine design method is developed based on machine learning.

• A new knowledge-based self-expansion design procedure is proposed.

• DNN is combined with the multi-output Gaussian process to improve accuracy.

• The accuracy of the method is verified by CFD and flat plate test rig.

摘要

•A knowledge-based turbine design method is developed based on machine learning.•A new knowledge-based self-expansion design procedure is proposed.•DNN is combined with the multi-output Gaussian process to improve accuracy.•The accuracy of the method is verified by CFD and flat plate test rig.

论文关键词：Turbomachinery design system,Deep neural network,Multi-output Gaussian process,Knowledge-based modeling,Bayesian optimization

论文评审过程：Received 25 March 2022, Revised 25 June 2022, Accepted 27 June 2022, Available online 2 July 2022, Version of Record 15 July 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.109352