Rapid aerodynamic method for predicting the performance of interacting wing sailsShow others and affiliations
2024 (English)In: Ocean Engineering, ISSN 0029-8018, E-ISSN 1873-5258, Vol. 293, article id 116596Article in journal (Refereed) Published
Abstract [en]
Rapid performance prediction tools are required for the evaluation, optimization, and comparison of different wind propulsion systems (WPSs). These tools should capture viscous aerodynamic flow effects in 3D, particularly the maximum propulsion force, stall angles, and interaction effects between the lift-generating units. This paper presents a rapid aerodynamic calculation method for wing sails that combines a semi-empirical lifting line model with a potential flow-based interaction model to account for 3D interaction effects. The method was applied to a WPS that consisted of several wing sails with considerable interaction effects. The results were compared to CFD RANS simulations in 2D and in 3D. For the evaluated validation cases, the interaction model improved the prediction considerably compared to when the interaction was not accounted for. The method provided acceptable driving force, moments, and stall predictions, with negligible computational cost compared to 3D CFD simulations.
Place, publisher, year, edition, pages
Elsevier Ltd , 2024. Vol. 293, article id 116596
Keywords [en]
Aerodynamic stalling; Computational fluid dynamics; Lift; Ship propulsion; Vehicle performance; Wings, Interaction effect; Lifting line; Lifting line method; Line methods; Propulsion system; Sail interaction; Wind propulsion system; Wind-assisted propulsion; Wind-assisted ship propulsion; Wing sail, Forecasting, aerodynamics; comparative study; computational fluid dynamics; Navier-Stokes equations; performance assessment; potential flow; prediction; Reynolds number; structural component; vessel; wind field
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:ri:diva-71698DOI: 10.1016/j.oceaneng.2023.116596Scopus ID: 2-s2.0-85181147192OAI: oai:DiVA.org:ri-71698DiVA, id: diva2:1836472
Funder
Swedish Energy Agency, 2022/P2021-00275Swedish Research Council, 2018-05973
Note
This research was funded by the Swedish Energy Agency , grant number 2022/P2021-00275 . The 3D CFD simulations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) and the Swedish National Infrastructure for Computing (SNIC) at the Chalmers Centre for Computational Science and Engineering (C3SE), High Performance Computing Center North (HPC2N) and Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) partially funded by the Swedish Research Council through grant agreements no. 2022-06725 and 2018-05973
2024-02-092024-02-092024-05-27Bibliographically approved