報告題目：Different Perspectives on the Application of Deep Learning for Pore-Scale Two-Phase Flow

報告人： 蔣澤云（Heriot-Watt大學，教授）

時間：2023年6月8日 16:30-18:30

地點：明理樓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.

       報告人簡介：蔣澤云教授從2004年以后主要從事孔隙介質（如巖石、土壤等）異質多尺度結構分析和流體滲流模型研究, 在Water Resource research, Transport in Porous Media, Fuel等期刊和國際會議上發表論文40多篇，在國內外參與并主導若干重大科研項目。主要從事微觀空隙結構分析及網絡滲流模型的研究, 獨立開發軟件系統PAT–Pore Analysis Tools（這一軟件在學術和工業界得到廣泛的使用）。擅長于巖石微觀圖像幾何拓撲屬性的分析,并建立多尺度孔隙模型和實施數值模擬,建立其微觀結構（如孔隙度、孔尺寸、形狀、連通性、孔壁粗糙度等）與宏觀流體屬性（如滲透率、毛細管壓力、性、相對滲透率、電阻率等）間的理論或經驗公式。

主辦單位: 理學院?人工智能研究院?非線性動力系統研究所?數理力學研究中心 ?科學技術發展研究院