涡轮机械
不确定度量化
空气动力学
计算机科学
替代模型
稳健性(进化)
稳健优化
涡轮机
数学优化
最优化问题
计算流体力学
工程类
数学
机器学习
算法
航空航天工程
化学
基因
生物化学
作者
Jinxing Li,Tianyuan Liu,Guangya Zhu,Lili Li,Yonghui Xie
出处
期刊:Energy
[Elsevier]
日期:2023-06-01
卷期号:273: 127289-127289
被引量:2
标识
DOI:10.1016/j.energy.2023.127289
摘要
The actual operation of turbomachinery is inevitably affected by multi-source uncertainties. Such uncertainties are detrimental to the performance and reliability of energy systems. Based on graph learning methods, this work aims to provide a convenient and effective approach for aerodynamic robust optimization of turbomachinery. A radial inflow turbine is taken as the research target and Dual Graph Neural Network (DGNN) regression model is constructed for flow field prediction and performance discrimination. By comparing the accuracy and time consumption, the advantages of DGNN over classical surrogate models and computational fluid dynamics (CFD) are clarified. The proposed model is integrated into uncertainty quantification and aerodynamic robust optimization. The effect of multi-source uncertainties on performance is quantified. The stochastic response of flow fields is also obtained conveniently through DGNN. Robust optimization is performed for power and efficiency, respectively. The power robust optimization improves the power by 1.52% and reduces the standard deviation of power by 15.45%. The efficiency robust optimization achieves an efficiency improvement of 1.76% (increment) and an efficiency standard deviation reduction of 36.82%. The proposed approach is an efficient and competitive choice for uncertainty quantification and robust optimization. The present work contributes to constructing the digital twin of turbomachinery systems.
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