Session: 09-02-08 Wind Energy: System Testing

Submission Number: 181595

Cross-Scale Performance Assessment of a 10 MW Floating Offshore Wind Turbine via CFD, Openfast, and Model-Scale Experiments 

ABSTRACT: Accurate prediction of full-scale floating offshore wind turbine (FOWT) performance remains challenging due to complex aero–hydrodynamic interactions and the scarcity of full-scale operational data. This study investigates a 10 MW semi-submersible FOWT using model-scale basin experiments, high-fidelity CFD simulations, and high-efficiency OpenFAST simulations to quantify scale effects and assess cross-scale performance prediction strategies.

Model tests were conducted at a 1:60 scale to measure turbine thrust, torque, and six degrees of freedom (6DOF) motions. CFD simulations at model scale employed unsteady Reynolds-Averaged Navier–Stokes equations with the γ–Re_θ turbulence model, appropriate for transitional flow, while OpenFAST simulations used model-scale lift and drag coefficients. Comparison with experimental measurements showed excellent agreement for thrust and torque, validating the numerical approach.

Validated model-scale results were used as a reference for full-scale performance assessment. Full-scale CFD and OpenFAST simulations were conducted to evaluate thrust, torque, power, and 6DOF motions. While full-scale thrust predictions closely matched Froude-scaled model-test estimates, torque and power exhibited notable differences. These discrepancies arise from Reynolds-dependent flow regime transitions: model-scale flow is transitional (~10^4), whereas full-scale flow is fully turbulent (~10^7). Streamline analysis revealed extensive separation at the model scale, in contrast to limited separation near the blade root at full scale.

The study highlights that turbulence model selection is critical for capturing scale-dependent effects accurately: γ–Re_θ for model-scale transitional flow, and k–ω for full-scale fully turbulent flow. OpenFAST simulations using full-scale lift and drag coefficients closely reproduced CFD full-scale results, confirming that proper parameterization can bridge some of the scale-induced discrepancies. Frequency-domain analysis showed that dominant oscillation modes, including wave peak and blade-passing frequencies, are well captured across CFD and experimental data.

Based on these cross-scale comparisons, the work provides quantitative insight for defining minimum Reynolds number requirements for model tests and informs scale-up methodologies to predict full-scale FOWT performance more reliably. By linking model-scale experiments, CFD, and aeroelastic simulations, this study establishes a framework to guide future model-test designs and support the development of validated scale-up strategies for large FOWTs.

Presenting Author: Xiuqing Xing Institute of High Performance Computing, A*STAR Research Entities

Presenting Author Biography: Dr. Xing is the Group Manager of the Materials and Physics-Based AI for Design and Discovery group at IHPC, A*STAR, Singapore. She leads a multidisciplinary team developing AI-driven methods for modeling, simulation, and design optimization across materials and engineering systems. Her own research focuses on data-driven approaches to high-fidelity modeling, performance prediction, and design optimization, with applications in marine offshore, renewable energy, and related fields.

Authors:

Xiuqing Xing Institute of High Performance Computing, A*STAR Research Entities
Xiaoqin Zhang Institute of High Performance Computing, A*STAR
Chang Wei Kang Institute of High Performance Computing, A*STAR