Enhanced Computational Fluid Dynamics (CFD) Modeling of Gas Hydrate Dissociation Dynamics in Subsea Pipelines via Multi-Fidelity Surrogate Modeling and Ensemble Kalman Filtering

**Abstract:** This research introduces a novel approach to enhance the predictive accuracy and computational efficiency of Computational Fluid Dynamics (CFD) simulations of gas hydrate dissociation in subsea pipelines. Traditional CFD models struggle with the highly transient and multi-phase nature of hydrate dissociation, requiring computationally expensive simulations with fine spatial resolution. We propose a multi-fidelity surrogate modeling framework coupled with Ensemble Kalman Filtering (EnKF) to dynamically update the CFD model's parameters and improve the accuracy of long-term predictions. This approach leverages coarse-grid CFD simulations to generate training data for lower-fidelity surrogate models, which are then refined using higher-fidelity CFD validations and EnKF for continual posterior correction, resulting in a significantly improved representation of hydrate dissociation dynamics with reduced computational cost. The methodology is immediately applicable to flow assurance design and operational decision-making for subsea hydrate systems.