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High-Order CFD Modeling of Rotating Actuator Line Systems

Imran, Abdullah Al et al. · arxiv_all
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fluid-dynamics
High-order computational fluid dynamics (CFD) methods combined with actuator-based blade representations provide an attractive approach for simulating rotating energy systems. In this work, we develop a high-order computational framework that couples the flux reconstruction/correction procedure via
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[2608.27943] High-Order CFD Modeling of Rotating Actuator Line Systems Skip to main content Search Submit Donate Log in Physics > Fluid Dynamics arXiv:2608.27943 (physics) [Submitted on 28 Aug 2026] Title: High-Order CFD Modeling of Rotating Actuator Line Systems Authors: Abdullah Al Imran , Meilin Yu View PDF HTML (experimental) Abstract: High-order computational fluid dynamics (CFD) methods combined with actuator-based blade representations provide an attractive approach for simulating rotating energy systems. In this work, we develop a high-order computational framework that couples the flux reconstruction/correction procedure via reconstruction (FR/CPR) formulation with a rotating actuator line model (ALM). Blade rotation and unsteady aerodynamic forces are implemented through time-dependent source terms applied on a fixed cartesian grid. This approach allows simulations of a fully rotating turbine without using explicit blade geometry. Aerodynamic loads are computed using quasi-steady airfoil data and distributed into the flow field with Gaussian smoothing to maintain numerical stability in the high-order scheme. The framework is tested using a two-bladed vertical-axis wind turbine (VAWT) operating at a low tip speed ratio (TSR). The phase-averaged blade motion and overall load are analyzed to study variations with azimuthal angle. In addition, instantaneous and mean flow fields are examined to describe wake structure and unsteady vortex shedding. Normalized mean wake velocity profiles are used for validation through comparison with experimental measurements and blade-resolved large-eddy simulations (LES) reported in the literature. The results show that the proposed high-order virtual-body framework can reliably capture major blade-loading trends, flow organization, and wake features under strongly unsteady conditions, while maintaining geometric simplicity. Comments: Accepted and presented at Fluids Engineering Division Summer Meeting (FEDSM), 2026 Subjects: Fluid Dynamics (physics.flu-dyn) Cite as: arXiv:2608.27943 [physics.flu-dyn] (or arXiv:2608.27943v1 [physics.flu-dyn] for this version) https://doi.org/10.48550/arXiv.2608.27943 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Abdullah Al Imran [ view email ] [v1] Fri, 28 Aug 2026 05:34:29 UTC (8,013 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: physics.flu-dyn < prev | next > new | recent | 2026-08 Change to browse by: physics References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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