Predicting Wake-Induced Interactions of Tandem Flettner Rotors

Abstract

The growing demand for sustainable marine transportation has revived interest in Flettner rotors as auxiliary wind-assisted propulsion devices capable of significantly reducing fuel consumption and emissions. While the aerodynamic performance of single rotors has been widely studied, the complex wake-induced interactions between multiple rotors remain insufficiently understood, limiting reliable performance prediction for practical installations. This thesis investigates these wake interactions of tandem finite Flettner rotors through high-fidelity computational fluid dynamics (CFD) simulations supported by data-driven reduced-order modeling (ROM). The study begins with a validation campaign against benchmark experimental data from Shehata and Medina (2021) and numerical data from Liu et al. (2025) for rotating cylinders to ensure the reliability of the employed Biot–Savart bound-ary condition implementation in the WaterLily.jl CFD framework. The validated setup is then extended to perform a systematic parametric study of two interacting rotors by varying spin ratios and incidence angle at a Reynolds number of Re = 1000. The simulations reveal distinct wake regimes governed by up-wash and down-wash interactions, demonstrating how the forces on both rotors change with induced interactions. Additionally, the results indicate that asymmetric spin-ratio combinations can locally enhance the aerodynamic efficiency of the system by mitigating wake interference. To analyze the vast three-dimensional flow database efficiently, Proper Orthogonal Decomposition (POD) is employed. The first few POD modes capture the dominant wake structures responsible for the majority of the flow energy, providing physical insight into the coherent vortical features and their parametric dependence. The POD coefficients serve as compact representations of the flow fields and are used to construct a machine-learning based reduced order model. A multi-layer perceptron (MLP) and a Bayesian regression (BR) model is trained to learn the nonlinear mapping between the global parameters (spin ratio and angle of incidence) and the corresponding POD coefficients. The resulting models are capable of predicting the time-averaged velocity and pressure fields for unseen parameter combinations with reasonable accuracy and low computational cost. The combined POD-ML framework demonstrates the feasibility of predicting complex wake-interaction behavior using limited high-fidelity data. While the models successfully reproduce dominant flow patterns and aerodynamic trends, their accuracy can be further improved by incorporating additional high Reynolds number data and broader parameter coverage. The thesis concludes that reduced-order modeling based on POD and machine learning provides a promising pathway toward fast, data-driven performance prediction tools for wind-assisted propulsion systems featuring multiple interacting Flettner rotors.