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Different Perspectives on the Application of Deep Learning for Pore-Scale Two-Phase Flow

宣布日期:2023年06月07日    浏览量:[]

陈诉题目:Different Perspectives on the Application of Deep Learning for Pore-Scale Two-Phase Flow

陈诉人: 蒋泽云(Heriot-Watt大学,教授)

时间:2023年6月8日 16:30-18:30

所在:明理楼C302B

     摘要: The recent pore-scale literature is not short of compelling studies on deep learning applications. The prediction of two-phase flow fields, however, remains elusive. This is partly due to focusing on model architecture and data quality, rather than the quantity of data. This work presents an end-to-end and accurate deep learning workflow to predict phase distributions during steady-state two-phase drainage, directly from dry images and input features of pixel size, IFT, contact angle, and pressure. A highly diverse dataset is first constructed by subsampling CT scans of synthetic and real rocks. We then devise a new vision transformer (ViT) that drains pores solely based on their size, regardless of their spatial location, where the phase connectivity to inlet(s) is enforced as a post-processing step. With this setup, inference on images of any size with various pixel sizes can efficiently be made by patching input images and stitching results.

       陈诉人简介蒋泽云教授从2004年以后主要从事孔隙介质(如岩石、土壤等)异质多尺度结构分析和流体渗流模型研究, 在Water Resource research, Transport in Porous Media, Fuel等期刊和国际聚会会议上发表论文40多篇,在海内外加入并主导若干重大科研项目。主要从事微观空隙结构分析及网络渗流模型的研究, 独立开发软件系统PAT–Pore Analysis Tools(这一软件在学术和工业界获得广泛的使用)。擅长于岩石微观图像几何拓扑属性的分析,并建设多尺度孔隙模型和实施数值模拟,建设其微观结构(如孔隙度、孔尺寸、形状、连通性、孔壁粗拙度等)与宏观流体属性(如渗透率、毛细管压力、性、相对渗透率、电阻率等)间的理论或经验公式。

主办单元: 理学院?人工智能研究院?非线性动力系统研究所?数理力学研究中心 ?科学技术生长研究院

 

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