CO$_2$ sequestration hybrid solver using isogeometric alternating-directions and collocation-based robust variational physics informed neural networks (IGA-ADS-CRVPINN)
Askold Vilkha, Tomasz Służalec, Marcin Łoś, Maciej Paszyński
TLDR
This paper introduces a hybrid IGA-ADS-CRVPINN solver for CO2 sequestration, achieving over 3x speedup compared to traditional methods.
Key contributions
- Introduces IGA-ADS-CRVPINN, a novel hybrid solver for CO2 sequestration simulations.
- Combines IGA-ADS for saturation field updates and CRVPINN for pressure field calculations.
- CRVPINN is pretrained, enabling rapid pressure updates with only 100 Adam iterations per timestep.
- Achieves over 3x speedup compared to IGA-ADS coupled with the MUMPS direct solver.
Why it matters
This paper introduces a significantly faster hybrid solver for CO2 sequestration simulations, a critical step for climate change mitigation. The 3x speedup enables more efficient and complex modeling of CO2 behavior in porous structures, accelerating research in carbon capture and storage.
Original Abstract
This paper presents the hybrid solver for a $CO_2$ sequestration problem. The solver uses the IGA-ADS (IsoGeometric Analysis Alternating Directions solver) to compute the saturation scalar field update using the explicit method, and CRVPINN (Collocation-based Robust Variational Physics Informed Neural Networks solver) to compute the pressure scalar field. The study focuses on simulating the physical behavior of $CO_2$ in porous structures, excluding chemical reactions. The mathematical model is based on Darcy's Law. The CRVPINN is pretrained on the initial pressure configuration, and the time step pressure updates require only 100 iterations of the Adam method per time step. We compare our hybrid IGA-ADS solver, coupled with the CRVPINN method, with a baseline of the IGA-ADS solver coupled with the MUMPS direct solver. Our hybrid solver is over 3 times faster on a single computational node from the ARES cluster of ACK CYFRONET. Future work includes extensive testing, inverse problem solving, and potential application to $H_2$ storage problems.
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