Validating far-field wake predictions

Predicting the development of ship wakes over large distances is important for multiple processes relevant to ship signatures, such as persistence of entrained air and surfactant transport leading to cross-wake surface-tension gradients affecting is damping of short waves visible in synthetic aperture radar (SAR). Many existing numerical studies validate only final observables (air fraction, SAR images, acoustic signatures) even though these depend on the detailed hydrodynamics generated by the hull, propulsor and appendages. This paper presents a validation approach that combines a dedicated stereo-PIV dataset for a generic twin-screw frigate model measured up to 12 L_PP downstream, and CFD simulations at matching conditions using both a conventional 3D URANS set-up and an efficient 2D+t wake-propagation framework. The focus of the work is to establish a viable measurement methodology, obtain experimental data for several sailing configurations, and use the data to validate the numerical approaches. It is shown that with sufficient grid refinement and careful numerical settings, far wake velocity distributions can be accurately predicted to within several percent of freestream velocity. The combined dataset and workflow directly support predictive studies of wake‑related signatures for operational awareness and design decisions.