应用不同深度学习代理模型的灯笼型扰流柱通道换热性能分布预测方法比较

高尚鸿; 张韦馨; 杨克峰; 汪翔宇; 丰镇平

doi:10.7652/xjtuxb202402004

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应用不同深度学习代理模型的灯笼型扰流柱通道换热性能分布预测方法比较

更新时间：2025-05-13

- 应用不同深度学习代理模型的灯笼型扰流柱通道换热性能分布预测方法比较
- Comparison of Different Prediction Methods for Heat Transfer Distribution of Lantern-Shaped Pin-Fin Channel with Different Deep Learning Surrogate Models
- 西安交通大学学报 2024年58卷第2期页码：31-42
- 作者机构：
  
  西安交通大学能源与动力工程学院,西安,710049
- 作者简介：
  
  : 2023-08-02。作者简介: 高尚鸿(1995—),男,博士生
  丰镇平(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(52276037)。
- 基金信息：
  
  : 2023-08-02。作者简介: 高尚鸿(1995—),男,博士生;丰镇平(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(52276037)。
- DOI：10.7652/xjtuxb202402004
  中图分类号： TK474.7
- 网络首发：2024-02-10，
  
  纸质出版：2024
- 稿件说明：
移动端阅览
高尚鸿, 张韦馨, 杨克峰, 等. 应用不同深度学习代理模型的灯笼型扰流柱通道换热性能分布预测方法比较[J]. 西安交通大学学报, 2024,58(2):31-42.

GAO Shanghong, ZHANG Weixin, YANG Kefeng, et al. Comparison of Different Prediction Methods for Heat Transfer Distribution of Lantern-Shaped Pin-Fin Channel with Different Deep Learning Surrogate Models[J]. 2024, 58(2): 31-42.
高尚鸿, 张韦馨, 杨克峰, 等. 应用不同深度学习代理模型的灯笼型扰流柱通道换热性能分布预测方法比较[J]. 西安交通大学学报, 2024,58(2):31-42. DOI： 10.7652/xjtuxb202402004.

GAO Shanghong, ZHANG Weixin, YANG Kefeng, et al. Comparison of Different Prediction Methods for Heat Transfer Distribution of Lantern-Shaped Pin-Fin Channel with Different Deep Learning Surrogate Models[J]. 2024, 58(2): 31-42. DOI： 10.7652/xjtuxb202402004.

摘要

为提高对灯笼型扰流柱通道端壁面的换热分布预测能力

构建并比较了几种深度学习代理模型的预测性能。应用数据驱动的思想

建立了多项式响应面模型(RSM)、径向基函数模型(RBF)、径向基神经网络模型(RBFNN)、Kriging模型和总体平均近似模型(Ensemble)共5种传统代理模型以及5种基于生成对抗网络的深度学习代理模型

包括有残差网络的pix2pix模型、pix2pixHD模型、CycleGAN模型、StarGAN模型以及单一无残差网络的pix2pix模型

以扰流柱截面参数为设计变量

通过拉丁超立方抽样分别得到了样本数为50、25、12、6和3的训练集

并根据扰流柱尾迹高换热区分布特点

将训练样本数为50的数据集分为宽样本数据集和窄样本数据集

比较了不同代理模型对端壁面换热性能的预测精度、计算成本和泛化能力。结果表明:有残差网络的pix2pix模型相比于无残差网络的pix2pix模型

预测精度得到有效提高

在样本数为50的情况下

面平均值预测误差从0.68%降低到0.32%

平均相对误差从6.89%降低到6.41%

而且当训练样本数减少时

有残差网络的模型预测能力更加突出; 传统代理模型的时间成本可忽略不计

但深度学习模型的单卡训练时间较长

且增加残差网络后的模型计算成本更高; 当训练样本数为50时

传统代理模型和深度学习模型之间预测精度差异不大; 当训练样本数逐渐减少时

深度学习代理模型展示出更高的预测精度和泛化能力; Kriging模型虽然泛化能力强

但是预测结果趋同; RSM模型、RBF模型和Ensemble模型泛化能力最差

训练样本数较少时

预测结果严重失真。可见在换热性能预测方面

深度学习模型在预测精度与泛化能力上均有显著优势

尤其适合于小样本问题

对提高灯笼型扰流柱截面设计效率具有参考价值。

Abstract

In order to improve the prediction ability of heat transfer performance on lantern-shaped pin-fin endwall

several deep learning surrogate models are constructed and their prediction performance compared. Based on data-driven thinking

five traditional surrogate models

namely polynomial response surface model(RSM)

radial basis function model(RBF)

radial basis function neural network(RBFNN)

Kriging model and ensemble of surrogate model(Ensemble)are proposed. Additionally

five generative adversarial network surrogate models based on deep learning

namely pix2pix model

pix2pixHD model

CycleGAN model

StarGAN model all with residual network

and another pix2pix model without residual network are proposed. The training sets with various sample numbers of 50

6 and 3 were obtained by Latin hypercube sampling

respectively

using the pin-fin cross-sectional shape as the design variables. According to the distribution characteristics of high heat transfer zone

the dataset with a training sample number of 50 is divided into a wide sample dataset and a narrow sample dataset. On this basis

the various heat transfer performance of different surrogate models are compared in terms of prediction accuracy

computational cost and generalization ability. The results show that

compared with the pix2pix model without residual network

the prediction accuracy of the pix2pix model with residual network is effectively improved

and the prediction error of the surface mean is reduced from 0.68% to 0.32%

and the averaged relative error is reduced from 6.89% to 6.41% when the sample number is 50. When the number of training samples decreases

the prediction performance of the models with residual network becomes more prominent. The time cost of traditional surrogate models is negligible

but the training time of deep learning models is long especially for models with residual network. When the number of training samples is 50

there is little difference in prediction accuracy between the traditional surrogate models and deep learning models. With gradual decrease in the number of training samples

deep learning surrogate models show higher prediction accuracy and generalization capabilities. Although the Kriging model has strong generalization ability

the prediction results tend to be similar. RSM model

RBF model

and Ensemble model have the lowest generalization ability

and the prediction results are severely distorted when there are fewer training samples. For the heat transfer distribution prediction of the pin-fin endwall

the deep learning models possess significant advantages in terms of both prediction accuracy and generalization ability

which are more suitable for small batch sample problems. This study has important reference value for improving the cross-sectional shape design efficiency of lantern-shaped pin-fin.

关键词

Keywords

references

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