Accelerating Pipeline Corrosion Modeling via Bayesian Active Learning

Abstract

Pipeline corrosion poses significant challenges and risks to the energy industry and its mitigation requires extensive and reliable predictive modeling. Corrosion models based on computational fluid dynamics (CFD) stands as a desirable candidate for its detailed physical characterization and modeling flexibility, but its applications in practical industrial settings is limited by the high computational cost and laborious manual operation in the modeling and sampling process. To address these challenges, we propose a Bayesian active learning method. The method consists of a surrogate model formulated using Gaussian process regression (GPR) to provide rapid model prediction as well as uncertainty quantification, and an adaptive sampling scheme to automate and accelerate the data collection process. Careful dimension reduction guided by both physics and data is also carried out to significantly simplify the sampling space. The capability of the overall method for efficient and automated sampling and surrogate modeling is demonstrated on an example case of corrosion predictive modeling and can be leveraged in industrial applications at a much larger scale.