Advanced generalized machine learning models for predicting hydrogen–brine interfacial tension in underground hydrogen storage systems
Abstract The global transition to clean energy has highlighted hydrogen (H2) as a sustainable fuel, with underground hydrogen storage (UHS) in geological formations emerging as a key solution. Accurately predicting fluid interactions, particularly interfacial tension (IFT), is critical for ensuring...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-02304-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|