Predictive Modeling of Friction-Resistant Advanced Coatings using Fluid Dynamics

Turbulent skin-friction resistance in industrial conduit transport and piping infrastructure accounts for substantial energy dissipation worldwide. Biomimetic shark-skin riblets offer an attractive passive drag-reduction mechanism by constraining cross-stream turbulent momentum transfer and pinning quasi-streamwise vortices aloft. However, optimizing these micro-topographies across high-dimensional parameter spaces (riblet spacing s, aspect ratio h/s, conduit diameter D, and Reynolds number Re) remains experimentally and numerically prohibitive owing to the computational expense of high-fidelity Computational Fluid Dynamics (CFD). In this study, we established a deterministic, high-throughput machine learning surrogate framework to accelerate the predictive design of friction-resistant advanced coatings. A space-filling 4-dimensional design matrix spanning s ∈ [10, 120] μm, h/s ∈ [0.20, 1.20], D ∈ [25.1, 249.8] mm, and Re ∈ [10,000, 200,000] was generated via Quasi-Monte Carlo (QMC) Latin Hypercube Sampling (N = 1,000 geometries). An automated execution pipeline coupled Reynolds-Averaged Navier-Stokes (RANS) k-ω SST turbulence modeling with near-wall Navier slip boundary conditions, benchmarked against classical Petukhov analytical theory with relative baseline discretization errors below ±0.4%. An ensemble Random Forest regressor (300 bootstrapped decision trees) was trained to predict the continuous Darcy-Weisbach friction factor (f). The surrogate model demonstrated exceptional predictive accuracy on an unseen 20% holdout test set (N = 200), achieving a Coefficient of Determination of R2 = 0.99566 (explaining 99.57% of physical variance), a Mean Absolute Error of 1.487 × 10-4, and a Mean Absolute Percentage Error of 0.819%, with 100% of test predictions strictly bounded within a ±2% error envelope. Gini feature importance decomposition revealed that while macro-scale hydraulics (Re: 71.42%, D: 21.78%) govern global baseline dissipation, microstructural coating dimensions (s: 5.18%, h/s: 1.62%) act as the critical local governing switches determining the drag-reduction margin. The CFD data revealed an optimal drag reduction peak of ηDR,max = +9.253% centered at dimensionless wall spacing s+opt ≈ 16.5 and h/s = 0.55 – 0.65, whereas coarse riblets (s+ > 25) triggered rough-wall vortex penetration penalties down to -24.81%. Crucially, the trained surrogate predicted friction factors in 0.82 milliseconds—achieving an acceleration factor of 4,390,000× (~4.4 million times faster) compared to conventional meshed CFD (3,600 s)—enabling instantaneous 3D response surface exploration. When deployed in continuous industrial transport, the resulting head loss reduction yields electrical savings of up to 14.5 kWh/year per 10 meters of coated pipe, establishing an accurate, ultra-fast computational paradigm for next-generation energy-efficient surface engineering.