Identification of gas-solid flow regimes using convolutional neural network techniques

• Published in: Powder technology Vol. 442; p. 119848
• Main Authors: Zhang, Dian, Ouyang, Bo, Luo, Zheng-Hong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2024
• Subjects: Computational fluid dynamics; Convolutional neural network; Flow regime identification; Gas-solid fluidization; Highly resolved simulation; Gas-solid fluidization; Highly resolved simulation; Flow regime identification; Computational fluid dynamics; Convolutional neural network
• ISSN: 0032-5910; 1873-328X
• DOI: 10.1016/j.powtec.2024.119848

Flow regime identification is crucial for design and optimization of gas-solid fluidized bed devices. However, how to quickly and accurately identify flow regimes remains a formidable challenge. This study presents a machine learning-aided method using convolutional neural network (CNN) techniques aiming to identify gas-solid flow regimes efficiently and precisely. A database of images depicting various flow regimes is constructed through highly resolved simulations. Subsequently, we compare and evaluate the performance of six commonly used CNNs, namely LeNet-5, AlexNet, VGG-16, InceptionV1, ResNet-50, and MobileNetV2, in identifying typical gas-solid flow regimes. The results demonstrate that, compared to conventional methods, CNNs can identify flow regimes quickly and accurately. Among them, AlexNet achieves an identification accuracy of 100% and an identification speed of 55.86 ms per image. This study enhances the comprehension of gas-solid flow mechanisms, thereby fostering the advancement of reactor engineering. [Display omitted] •The convolutional neural network (CNN) techniques are adopted.•Six convolutional neural network models are used to identify flow regimes.•The highly resolved fine-grid simulations are used to generated images.•Flow regimes can be identified quickly and accurately by using CNN models.