22–25 May 2023
Europe/London timezone

Adaptive node adjustment for real-time subsurface flow modeling

23 May 2023, 10:30
1h 30m
Poster Presentation (MS15) Machine Learning and Big Data in Porous Media Poster

Speaker

Dr Shang-Ying Chen (National Cheng Kung University)

Description

Real-time subsurface flow simulation is desirable for managing groundwater resources, geothermal exploitation, carbon dioxide geological sequestration, or underground hydrogen storage. Data assimilation methods are developed to achieve this goal. However, assimilation models usually use mesh-based numerical methods. Remeshing is frequently required whenever new data to be integrated into the model are not located at the existing computational nodes. This study aims to develop an adaptation algorithm to accommodate node layout to the exact positions of additional data. For flexibility, we chose a mesh-free numerical method. We combined it with a fast node generation technique called the advancing front method to adjust meshless node placement before assimilation by ensemble Kalman filter. A hypothetical flow problem was used to test the proposed approach. The results show that the adaptive node adjustment works effectively for the real-time updating model. The accuracy and precision of modeling states and parameters were improved when integrating additional data.

Participation In-Person
Country Taiwan
MDPI Energies Student Poster Award No, do not submit my presenation for the student posters award.
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Primary author

Dr Shang-Ying Chen (National Cheng Kung University)

Co-author

Prof. Kuo-Chin Hsu (National Cheng Kung University)

Presentation materials

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