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This study explores the application of Generative Adversarial Networks (GANs) in combustion science, utilizing a flame image dataset. By comparing the produced images with the original dataset, we qualitatively analyze the discriminator and generator loss to assess the performance of the GAN. The results show improvements in the discriminator’s ability to distinguish between real and generated images, as well as improvements in the generator’s ability to add missing details, leading to the generation of images that are more realistic. Fundamental properties of flames are well captured in the resulting images, despite the absence or distortion of minor details. The study advances the fields of AI, image processing, and combustion science by highlighting possible uses in the creation of synthetic images and data augmentation. Overall, our qualitative analysis enriches comprehension of combustion science.

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Generative Adversarial Networks in Combustion: Flame Image Generation for Clean and Predictive Combustion Modeling Using Real Data

  • 会议论文

  • 发表日期：

    2025年

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  • Chandan Singh,
  • Marta Chinnici,
  • Ah Lian Kor,
  • Michael J. Evans,
  • Paul R. Medwell,
  • Alfonso Chinnici

摘要

This study explores the application of Generative Adversarial Networks (GANs) in combustion science, utilizing a flame image dataset. By comparing the produced images with the original dataset, we qualitatively analyze the discriminator and generator loss to assess the performance of the GAN. The results show improvements in the discriminator’s ability to distinguish between real and generated images, as well as improvements in the generator’s ability to add missing details, leading to the generation of images that are more realistic. Fundamental properties of flames are well captured in the resulting images, despite the absence or distortion of minor details. The study advances the fields of AI, image processing, and combustion science by highlighting possible uses in the creation of synthetic images and data augmentation. Overall, our qualitative analysis enriches comprehension of combustion science.