A comparative study of CFD and empirical approaches for environmental load coefficients

Abstract

Accurate estimation of wind and current loads is essential for the maneuvering and station-keeping performance of modern vessels. Existing empirical approaches, such as Fujiwara's regression and DNV-ST-0111, are widely used during the early stages of design; however, their reliability for multihull vessels, particularly those with asymmetric superstructures, has not yet been fully verified. This study evaluates these methods using a 19.7 m Crew Transfer Vessel (CTV) catamaran as a case study. Surge (Cwx​/Ccx​), sway (Cwy​/Ccy​), and yaw moment (Cwn​/Ccn​) coefficients are computed over drift angles from 0° to 180° using steady RANS double-body simulations, complemented by URANS multiphase simulations to assess free-surface effects. Numerical accuracy is verified through mesh convergence and ITTC-based uncertainty analysis, yielding GCI values below 1–2%.

The results show that Fujiwara's method significantly overestimates the longitudinal wind load (Cwx​) by up to ∼70% due to its inability to capture asymmetric separation and three-dimensional flow interactions, while also underpredicting stern-wind behavior. Although lateral forces and yaw trends are generally reproduced, notable deviations occur near peak loading conditions. For current loads, DNV-ST-0111 predicts overall trends reasonably well but underestimates yaw moments due to neglect of tunnel flow interaction and asymmetric pressure distribution. Free-surface effects are found to be negligible at the considered low Froude numbers, supporting the use of the double-body approach for efficient load prediction.

Overall, this study provides a quantitative assessment of the limitations of widely used empirical approaches for multihull vessels and demonstrates the importance of resolving three-dimensional flow interactions for accurate load prediction. The results show CFD as a reliable tool for estimating environmental loads on catamaran configurations and provide a validated dataset that can support the development of improved prediction models. The CFD-based coefficients presented in this study provide a reference dataset for future model development and practical design applications.