blade element momentum equations, robust solution methodology, guaranteed convergence, BEM
The blade element momentum equations, though conceptually simple, can be challenging to solve reliably and efficiently with high precision. These requirements are particularly important for efficient rotor blade optimization that utilizes gradient-based algorithms. Many solution approaches exist for numerically converging the axial and tangential induction factors. These methods all generally suffer from a lack of robustness in some regions of the rotor blade design space, or require significantly increased complexity to promote convergence. The approach described here allows for the blade element momentum equations to be parameterized by one variable: the local inflow angle. This reduction is mathematically equivalent, but greatly simplifies the solution approach. Namely, it allows for the use of one-dimensional root-finding algorithms for which very robust and efficient algorithms exist. This paper also discusses an appropriate arrangement of the equation and corresponding bounds for the one-dimensional search—intervals that bracket the solution and over which the function is well-behaved. The result is a methodology for solving the blade element momentum equations with guaranteed convergence and at a superlinear rate.
Original Publication Citation
Ning, A., “A Simple Solution Method for the Blade Element Momentum Equations with Guaranteed Convergence,” Wind Energy, Vol. 17, No. 9, Sep. 2014, pp. 1327–1345. doi:10.1002/we.1636
BYU ScholarsArchive Citation
Ning, Andrew, "A Simple Solution Method for the Blade Element Momentum Equations with Guaranteed Convergence" (2014). Faculty Publications. 1673.
Ira A. Fulton College of Engineering and Technology
Copyright © 2016 John Wiley & Sons, Ltd. This is the peer reviewed version of the article cited above, which has been published in final form at http://dx.doi.org/10.1002/we.1636. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
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