Scholarlink

<p>The prediction of flow distribution in regenerative cooling channels of scramjet can provide valuable reference information for flow regulation. The non-intrusive monitoring method based on deep learning is a promising approach. In this work, a generative adversarial networks-like&#xa0;(GAN-like) model is proposed, where the generator and discriminator are employed for temperature field reconstruction and flow distribution prediction respectively. The generator utilizes the sensor data to reconstruct the temperature field of the combustor outer wall, while the discriminator employs the generated temperature field to forecast the flow distribution within the parallel channels. The trained GAN-like model exhibits a commendable capability in predicting temperature field features and flow distribution states under the&#xa0;current dataset. The generator attains remarkable proficiency in reconstruction, evidenced by a structural similarity index surpassing 0.95 and a correlation coefficient exceeding 0.96. Additionally, it showcases an unforeseen aptitude at the&#xa0;boundary location. The discriminator exhibits stable precision in flow rate prediction, as indicated by an absolute error below 0.02&#xa0;g/s and a relative error lower than 3%.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Study on non-intrusive monitoring method of flow rate distribution within parallel cooling channels based on GAN-like model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月18日

查看全文
  • Yujie Guo,
  • Xingyu Zhou,
  • Jingying Zuo,
  • Xin Li,
  • Jianfei Wei,
  • Silong Zhang

摘要

The prediction of flow distribution in regenerative cooling channels of scramjet can provide valuable reference information for flow regulation. The non-intrusive monitoring method based on deep learning is a promising approach. In this work, a generative adversarial networks-like (GAN-like) model is proposed, where the generator and discriminator are employed for temperature field reconstruction and flow distribution prediction respectively. The generator utilizes the sensor data to reconstruct the temperature field of the combustor outer wall, while the discriminator employs the generated temperature field to forecast the flow distribution within the parallel channels. The trained GAN-like model exhibits a commendable capability in predicting temperature field features and flow distribution states under the current dataset. The generator attains remarkable proficiency in reconstruction, evidenced by a structural similarity index surpassing 0.95 and a correlation coefficient exceeding 0.96. Additionally, it showcases an unforeseen aptitude at the boundary location. The discriminator exhibits stable precision in flow rate prediction, as indicated by an absolute error below 0.02 g/s and a relative error lower than 3%.