Pumping Power Reduction in Crude-Oil Pipeline Transportation: CFD Validation and Kolmogorov–Arnold Network Surrogate Modelling

Precise prediction of pressure drop and drag-reduction performance is essential for improving the hydraulic efficiency and reducing the energy demand of crude-oil pipeline transportations. Therefore, this study aims to develop an integrated computational fluid dynamics (CFD)–machine learning (ML) framework for predicting pressure drop (∆p), drag reduction (DR), pumping power reduction (PPR), energy savings (ES), and flow-rate enhancement (Q) in turbulent crude-oil pipeline flow containing drag-reducing agents (DRAs). The investigated system considers the effect of pipeline length (L), diameter (D), surface roughness (ε), operating temperature (T), and DRA concentration (25–200 ppm). The Reynolds-average Navier–Stokes equations (RANS) were solved using the shear stress transport (SST) k-ω turbulence model approaching near-wall resolution of y+ ≈ 1 for DRA3 at 20 ppm. The CFD modelling was first used to validate an experimental benchmark and subsequently used to expand the available dataset over the investigated operating conditions. The combined experimental–CFD dataset was then employed to develop a multi-output Kolmogorov–Arnold network (KAN) surrogate model. The proposed framework predicted DR up to 44.2%, PPR of approximately 55 W, ES of 30%, and flow-rate enhancement up to 5–10(Lday). The KAN model effectively captured the nonlinear relationships among DRA characteristics, pipeline geometry, and operating conditions, achieving R2 = 0.9318 for PPR prediction. The novelty of the proposed work lies in integrating a validated, near-wall-resolved SST k-ω CFD model with a multi-output KAN surrogate model, combining physics-based flow analysis with rapid data-driven prediction of hydraulic and energy-performance indicators.