Machine learning-driven identification and predictive mapping of clogging regimes in porous media
<p>Clogging in porous media critically limits the performance of subsurface and filtration systems, yet conventional models often rely on oversimplified, single-parameter thresholds to predict its behavior. This study develops a unified, machine learning–based framework to identify, characteri...
محفوظ في:
| المؤلف الرئيسي: | Ahmed Elrahmani (17128837) (author) |
|---|---|
| مؤلفون آخرون: | Riyadh I. Al-Raoush (2366107) (author), Harris Sajjad Rabbani (14489205) (author), Thomas D. Seers (8759187) (author) |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
مواد مشابهة
-
Modeling of permeability impairment dynamics in porous media: A machine learning approach
حسب: Ahmed, Elrahmani
منشور في: (2023) -
Clogging and permeability reduction dynamics in porous media: A numerical simulation study
حسب: Ahmed Elrahmani (17128837)
منشور في: (2023) -
Pore-scale simulation of fine particles migration in porous media using coupled CFD-DEM
حسب: Ahmed Elrahmani (17128837)
منشور في: (2022) -
Fines effect on gas flow in sandy sediments using μCT and pore networks
حسب: Jamal A. Hannun (14779078)
منشور في: (2022) -
Convergence rate of regime-switching trees
حسب: Leduc, Guillaume
منشور في: (2016)