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Reduced order models for finite-volume simulations of turbulent flow around wind-turbine blades

The computational cost of the design optimisation of wind turbines, as well as the optimisation of the operation and maintenance of offshore wind farms represents a limitation to the application of conventional simulation methods. New techniques such as reduced order modelling (ROM) and technologies...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-09, Vol.2018 (1), p.12042
Main Authors: Tsiolakis, Vasileios, Kvamsdal, Trond, Rasheed, Adil, Fonn, Eivind, Brummelen, Harald van
Format: Article
Language:English
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Summary:The computational cost of the design optimisation of wind turbines, as well as the optimisation of the operation and maintenance of offshore wind farms represents a limitation to the application of conventional simulation methods. New techniques such as reduced order modelling (ROM) and technologies such as hybrid-analysis methods and digital twins have increased in popularity due to their ability to deliver numerical results at a significant speed-up with reasonable accuracy. This work presents a hybrid projection-based proper orthogonal decomposition (POD) strategy applied to transient turbulent flow problems. Key feature of this work is the applicability of the methodology to high Reynolds number cases and the stabilisation of pressure in the online phase via the assembly of the so-called pressure Poisson equation. Another significant part of this work is the implementation of an interpolation scheme for the eddy viscosity field in the classical POD-Galerkin strategy. The sampling procedure and the calculation of the reduced operators in the offline phase is carried out using the finite volume method (FVM), OpenFOAM’s libraries specifically, while the construction of the reduced basis and the solution of the online phase is carried out in Python. The capability of the resulting ROM is tested using the two-dimensional flow around a NACA0015 airfoil at 17° angle of attack with Reynolds number of approximately 300 000.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2018/1/012042