Optimization of one-dimensional characteristic prediction algorithm based on neural network model
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摘要:
基于CFD叶栅数据集构建了适用于多种叶型普遍工况的全连接神经网络落后角预测代理模型，对压气机特性预测HARIKA算法中的原有经验模型进行替换。通过拉丁超立方采样构建了涵盖NACA65、双圆弧、多圆弧三种主流叶型的
58300 组数据的数据集。对比全连接网络模型和支持向量机等8种机器学习回归模型基于NACA65叶型数据集的落后角预测学习能力，全连接模型落后角预测平均误差为0.06°，优于其他回归模型。在一维特性预测领域对多种叶型的落后角计算，使用不同叶型训练出的模型组合比多叶型数据训练出来的一个模型预测精度更高。在跨声速工况下与AI222-25型发动机两级风扇的实验数据对比，优化后HARIKA算法的压比特性预测相对误差平均下降9.06%，最高下降20.43%，证实该优化方法对提升HARIKA特性预测能力有一定帮助。Abstract:Based on the large cascade data set of CFD, a fully connected neural network deviation angle prediction model suitable for a variety of blade profiles is constructed. The original empirical model in HARIKA algorithm is replaced. A large data set covering
58300 data sets of NACA65, double arc and multi-arc main blade profiles was constructed by Latin hypercube sampling. The average error of deviation angle prediction of the fully connected model is 0.06°, which is better than other regression models. In the field of one-dimensional property prediction, the model combination trained with different blade profiles has higher prediction accuracy than one model trained with multiple blade profiles.Under transonic conditions, compared with experimental data of the AI222-25 engine’s two-stage fan, the optimized HARIKA algorithm demonstrated an average reduction of 9.06% in relative error for pressure ratio prediction, with a maximum reduction of 20.43%. This confirms the effectiveness of the optimization method in enhancing the performance prediction capability of the HARIKA algorithm.-
Key words:
- characteristics predict /
- HARIKA algorithm /
- deviation angle model /
- deep learning
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图 10 AI222-25二级风扇结构示意图
Figure 10. Schematic diagram of AI222-25 two-stage fans structure
图 11 AI222-25二级风扇特性计算结果
Figure 11. Calculation results of AI22-25 two-stage fan characteristics
表 1 数据集中各参数范围
Table 1. Range of parameters in the data set
参数 取值范围 NACA65 双圆弧 多圆弧 攻角 −2～8 −5～8 −5～8 进口马赫数 0.3～0.7 0.7～1.2 1.2～1.6 叶型弯角 0～73 5～61 7～63 稠度 0.5～2 1～2.5 1～2.2 最大相对厚度 0.04～0.14 0.03～0.1 0.06～0.11 前缘几何角 23～70 23～73 24～72 
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表 2 模型学习能力比较
Table 2. Comparison of model learning capabilities
模型 评价指标 LMSE LMAE R2 线性回归 4.142 1.546 0.577 决策树 0.662 0.364 0.939 随机森林 0.141 0.107 0.973 梯度提升 0.932 0.666 0.905 支持向量机 0.596 0.344 0.942 极端提升树 0.107 0.174 0.981 多项式回归 2.006 0.336 0.814 自适应增强 3.498 1.614 0.643
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表 3 AI222-25二级风扇设计参数
Table 3. AI222-25 two-stage fan design parameters
参数 设计值 动叶 静叶 转速/（r/min） 13200 0 入口外径/mm 622, 604 613, 602 出口外径/mm 613, 602 604, 600 入口轮毂比 0.4305 ,0.6798 0.5589 ,0.6828 出口轮毂比 0.5589 ,0.6828 0.6298 , 0.723叶片数 19, 29 42, 54 稠度 1.61, 1.25 1.5, 1.39 展弦比 1.244, 0.955 1.442, 1.147
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表 4 模型在各转速工况特性预测误差
Table 4. Prediction error of model characteristics at different speed conditions
折合转速 绝热效率$ \eta $ 总压比$ \pi $ EMP FCNN EMP FCNN 70 3.74 3.85 10.25 5.62 80 4.04 4.45 12.91 0.94 90 1.77 0.27 2.36 3.18 100 2.94 1.38 35.29 14.86
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