Collaborative Optimization of Flow Passage and Blades in a U-Shaped Impulse Turbine

ZHANG Nengxuan; GAO Tieyu; YAN Xiaojiang; MENG Xiangrui; GONG Jianying; XIE Xiaojun; SONG Zichen

doi:10.7652/xjtuxb202610017

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Collaborative Optimization of Flow Passage and Blades in a U-Shaped Impulse Turbine

—————————————————————— | 更新时间：2026-09-29

- Collaborative Optimization of Flow Passage and Blades in a U-Shaped Impulse Turbine
- Journal of Xi'an Jiaotong University Vol. 60, Issue 10, Pages: 197-208(2026)
- 作者机构：
  
  西安交通大学能源与动力工程学院，710049，西安
  西安热工研究院有限公司，710054，西安
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202610017
  CLC： TK14
- Received：22 January 2026，
  
  Online First：09 April 2026，
  
  Published：10 October 2026
- 稿件说明：
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ZHANG Nengxuan, GAO Tieyu, YAN Xiaojiang, et al. Collaborative Optimization of Flow Passage and Blades in a U-Shaped Impulse Turbine[J/OL]. Journal of Xi'an Jiaotong University,2026,60 (10):197-208. https://doi.org/10.7652/xjtuxb202610017.
DOI：

ZHANG Nengxuan, GAO Tieyu, YAN Xiaojiang, et al. Collaborative Optimization of Flow Passage and Blades in a U-Shaped Impulse Turbine[J/OL]. Journal of Xi'an Jiaotong University,2026,60 (10):197-208. https://doi.org/10.7652/xjtuxb202610017. DOI：

摘要

为提升U型冲击式透平的气动性能，开展了流道-叶片的协同多目标优化研究。首先，对冲击式透平转子叶片进行参数化建模，选取转子叶型和流道宽度作为设计变量。其次，采用最优拉丁超立方采样在高维决策空间生成样本集，获取样本响应值，基于样本数据构建Kriging代理模型。接着，以透平效率和输出功率为优化目标，采用非支配排序遗传算法Ⅱ（NSGA-Ⅱ）进行全局寻优并获得帕累托解集。然后，采用逼近理想解排序（TOPSIS）方法从解集中选取综合性能最优的几何参数组合，得到优化后的透平模型。最后，数值研究了优化方案与原始设计的内部流动特征，并结合全局敏感性分析了关键几何参数对性能的影响规律。研究结果表明：转子叶片中弧线几何角为影响效率的关键参数，导流叶片高度和转子叶片高度为影响功率的主要因素；优化后的冲击式透平效率和功率分别提升了3.35%、3.13%，且有效抑制了转子吸力面的流动分离及下游导叶区域的过冲损失，从而改善了内部流动稳定性并提升了能量转换效率。该研究结果可为冲击式透平的流道与叶片协同设计提供理论参考。

Abstract

To improve the aerodynamic performance of a U-shaped impulse turbine

a collaborative multi-objective optimization study on the flow passage and blades was conducted. First

the rotor blades of the impulse turbine were parametrically modeled

and the rotor blade profile and flow passage width were selected as design variables. Second

optimal Latin hypercube sampling was employed to generate a sample set in the high-dimensional decision space and obtain the sample response values

based on which a Kriging surrogate model was constructed. Third

with turbine efficiency and output power as the optimization objectives

the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) was utilized for global optimization to obtain the Pareto solution set. Then

the technique for order preference by similarity to an ideal solution (TOPSIS) method was adopted to select the geometric parameter combination with the best comprehensive performance from the solution set

yielding the optimized turbine model. Finally

numerical investigations were conducted on the internal flow characteristics of the optimized and original designs. Combined with global sensitivity analysis

the influence mechanisms of key geometric parameters on performance were clarified. The results indicate that the geometric angle of the rotor blade camber line is the key parameter affecting efficiency

while the heights of the guide vanes and rotor blades are the main factors influencing output power. The efficiency and output power of the optimized impulse turbine are increased by 3.35% and 3.13%

respectively. Moreover

the optimized design effectively suppresses flow separation on the rotor suction surface and reduces overshot losses in the downstream guide vane region

thereby improving internal flow stability and enhancing energy conversion efficiency. These results provide a theoretical reference for the collaborative design of flow passages and blades in impulse turbines.

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