Affiliation:
1. Shanghai Jiao Tong University, Shanghai, China
2. Imperial College London, London, UK
Abstract
Variable geometry twin-entry turbine has advantages in fuel economy, low-end torque, emission and turbo-engine matching of internal combustion engine. The reliability of performance prediction method for nozzled twin-entry turbine is heavily dependent on the understanding of flow mechanism of the turbine which is always confronted by time-dependent unequal admissions. This paper establishes a new meanline model of a nozzled twin-entry turbine based on the internal flow features. Firstly, flow mechanism of the twin-entry turbine with a vaned nozzle under different admissions is investigated via an experimentally validated CFD method. Results clearly demonstrate that the flow distortion in spanwise direction caused by unequal admissions notably influences the turbine efficiency discrepancy between symmetric unequal admissions, especially the two partial admissions. The evolution of flow distortion in the nozzle passage is the key to the performance discrepancy, which is impossible to be considered by a conventional performance model of a nozzled twin-entry turbine. Inspired by this, a newly-designed meanline model, named ‘parallel nozzle’ is proposed. Specifically, a nozzle passage is divided into two parallel sub-passages in spanwise direction to consider the flow distortion in nozzle passage. The model is validated against the detailed CFD results in terms of both overall performance and detailed flow parameters over different admissions. Results prove that turbine performance and flow parameters are well predicted by the model. Specifically, the influence of admissions on nozzled twin-entry turbine performance and flow parameters can be predicted by the method in satisfying accuracy.
Funder
National Natural Science Foundation of China
Subject
Mechanical Engineering,Aerospace Engineering
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Contrastive Cross-scale Graph Knowledge Synergy;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04