Publikationen

2022

• A symbolic framework to obtain mid-fidelity models of flexible multibody systems with application to horizontal-axis wind turbines

  Branlard, E., & Geisler, J. (2022). A symbolic framework to obtain mid-fidelity models of flexible multibody systems with application to horizontal-axis wind turbines. Wind Energy Science, 7, 2351–2371. http://doi.org/10.5194/wes-7-2351-2022

  Abstract

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  The article presents a symbolic framework (also called computer algebra program) that is used to obtain, in symbolic mathematical form, the linear and nonlinear equations of motion of a mid-fidelity multibody system including rigid and flexible bodies. Our approach is based on Kane's method and a nonlinear shape function representation for flexible bodies. The shape function approach does not represent the state of the art for flexible multibody dynamics but is an effective trade-off to obtain mid-fidelity models with few degrees of freedom, taking advantage of the separation of space and time. The method yields compact symbolic equations of motion with implicit account of the constraints. The general and automatic framework facilitates the creation and manipulation of models with various levels of complexity by adding or removing degrees of freedom. The symbolic treatment allows for analytical gradients and linearized equations of motion. The linear and nonlinear equations can be exported to Python code or dedicated software. There are multiple applications, such as time domain simulation, stability analyses, frequency domain analyses, advanced controller design, state observers, and digital twins. In this article, we describe the method we used to systematically generate the equations of motion of multibody systems and present the implementation of the framework using the Python package SymPy. We apply the framework to generate illustrative land-based and offshore wind turbine models. We compare our results with OpenFAST simulations and discuss the advantages and limitations of the method. The Python implementation is provided as an open-source project.

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  • Personenseiten

    • Geisler, Jens

  • Profilbereich

    • Erneuerbare Energien und nachhaltige Entwicklung

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

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  • 2022
  • Journal Article
  • • E. Branlard
    • Jens Geisler