Identification of Losses in Turbomachinery With Machine Learning

Angelini, Gino; Corsini, Alessandro; Delibra, Giovanni; Giovannelli, Marco

doi:10.1115/1.0005505v

Cited by 1 publication

(1 citation statement)

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“…It is worth mentioning that neural networks have also been used to predict turbomachinery flow characteristics. Angelini et al 25 identified the loss in a cascade based on unsupervised learning and designed a neural network to regress the loss decomposition results. They confirmed that the algorithm is effective when the three-dimensional characteristics of the flow field are not obvious.…”

Section: Introductionmentioning

confidence: 99%

Adaptive prediction of turbine profile loss and multi-objective optimization in a wide incidence range

Wang

Zhao

Zhang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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The loss prediction model plays a crucial role in turbine design for fast performance prediction and a shorter design cycle. Owing to the design requirements of high-efficiency turbines under a wide range of operating conditions, the loss prediction of the off-design incidence is increasingly important. However, limited by the modeling database and traditional modeling methods, the accuracy and adaptability of the existing off-design incidence loss predictions are insufficient. This paper proposes an adaptive prediction method based on machine learning and develops a multi-objective optimization process based on adaptive prediction. Machine learning (neural network) is applied for more flexible and accurate loss predictions over a wide incidence range. Compared with two classic loss models (Ainley and Mathieson model and Benner model), the adaptive prediction model significantly improves the ability to predict turbine profile loss with off-design incidence, particularly under large incidence conditions. The prediction root mean square error can be reduced by up to 73.8% (absolute value: 0.063). Furthermore, the multi-objective optimization method based on adaptive prediction is applied to the aerodynamic optimization of the original cascades with a wide incidence range. The weighted objective of the optimized cascade (Cri = 0.211) is reduced by 8.7% compared with that of the original cascade (Cri = 0.231). Within the range of full incidence angle (−40° to +20°), the variation of profile loss is reduced by 24.0%. This study is a preliminary exploration aimed at establishing an accurate turbine loss prediction system based on machine learning, the feasibility, and superiority of this approach are confirmed.

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Section: Introductionmentioning

confidence: 99%