%0 Journal Article
%T 基于导叶端弯优化工业汽轮机的级性能<br>Optimization of Stage Performance of Industrial Steam Turbine Based on Bowed Nozzle Guide Vane
%A 贺陈晨
%A 刘庆龙
%A 林安妮
%A 陈榴
%A 戴韧
%J Modeling and Simulation
%P 387-397
%@ 2324-870X
%D 2024
%I Hans Publishing
%R 10.12677/MOS.2024.131037
%X 基于弯叶片的气动原理，应用人工神经网络代理模型和遗传优化算法，对某型工业汽轮机的高压级导叶进行气动性能优化。经CFD模拟评估，导叶流动损失降低了6.65%，按焓降折合级效率提升0.28%，但实际级效率仅提升0.16%。导叶出口流场分析表明，端弯导叶的出口气流角沿叶高在端区发生了显著变化，其后动叶端区的进口冲角增加了10？~20？。修改动叶两端叶型，与导叶气流角匹配后，消除了冲角损失，等熵效率提高0.4%。本文结果说明导叶经过端弯优化后，匹配动叶气动设计的必要性。<br />
Based on the working principle of the bowed blade, one guide vane of high-pressure stage of an in-dustrial steam turbine is optimized by means of a surrogate model of artificial neural network and genetic algorithm. After CFD evaluation, the flow loss through the optimized bowed guide vane is reduced by 6.65 percent which is expected to raise the stage efficiency by 0.28 percent. However, the actual stage efficiency is calculated to rise only 0.16 percent. Analyzing the exit flow of the guide vane reveals that the flow angles vary significantly at both ends of the rotor blade. The incidence of the rotor blade is raised from 10 to 20 degrees near the end wall. Blade profiles at the tip and hub sections are modified to match the inlet flow. Incidence is reduced to raise the stage efficiency by 0.4 percent. This paper demonstrates the necessity to retrofit the rotor blade to match a bowed guide vane.
%K 弯叶片，气动优化，人工神经网络，二次流损失，动叶匹配<br>Bowed Vane
%K Aerodynamic Optimization
%K Artificial Neural Network
%K Secondary Flow Loss
%K Rotor Blade Matching
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=79405