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Fully automated multidisciplinary design optimization of a variable speed turbine

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Published under licence by IOP Publishing Ltd
, , Citation Erik Tengs et al 2021 IOP Conf. Ser.: Earth Environ. Sci. 774 012031 DOI 10.1088/1755-1315/774/1/012031

1755-1315/774/1/012031

Abstract

The future electricity market will have large contributions from renewable energy sources such as solar and wind. The intermittent nature of these energy sources creates a need for highly flexible operation of hydropower stations and changes the way we use hydraulic turbines to more off-design operation and more start-stop cycles. These changes challenge the structural integrity of the turbines in a way not seen before, and the next generation of hydro turbines will therefore have to be designed differently to meet this challenge. The goal of this article is to develop a framework for variable-speed Francis turbine design.

A fully automated multi-disciplinary design optimization procedure has been developed. As off-design operation is assumed, the runner do not only have to be optimized from a hydraulic point of view, the structural integrity is equally important. The design optimization is therefore based on a blending function of the hydraulic efficiency and the harmonic stress levels at a series of operating conditions. This is to ensure that the turbine is less prone to fatigue, even at off-design operation.

The process is fully automated, with no need for human interaction. A MATLAB design code is producing the raw design, every design is then meshed and tested at different operating points in a CFD solver. The pressure field from the fluid analysis is mapped onto the structure and evaluated in an acoustic-harmonic analysis to assess the fluctuating stresses in the turbine blades. A global optimization loop consisting of 15 design parameters is driving the process based on an overall optimization function.

The work has been performed as part of the HydroFlex project, and has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 764011.

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10.1088/1755-1315/774/1/012031