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Performance Optimization of a Kirsten–Boeing Turbine by A Metamodel Based on Neural Networks Coupled with CFD

Author

Listed:
  • Jan-Philipp Küppers

    (Chair of Product Development, Universität Siegen, Paul-Bonatz-Str. 9-11, 57076 Siegen, Germany)

  • Jens Metzger

    (Research Institute for Water and Environment (fwu), Department of Hydraulic and Coastal Engineering, Universität Siegen, Paul-Bonatz-Str. 9-11, 57076 Siegen, Germany)

  • Jürgen Jensen

    (Research Institute for Water and Environment (fwu), Department of Hydraulic and Coastal Engineering, Universität Siegen, Paul-Bonatz-Str. 9-11, 57076 Siegen, Germany)

  • Tamara Reinicke

    (Chair of Product Development, Universität Siegen, Paul-Bonatz-Str. 9-11, 57076 Siegen, Germany)

Abstract

The supply of energy is sustainable only if it is predominantly based on renewable or regenerative energies. For this reason, the use of micro-hydropower plants on rivers and streams is considered recently. This is a particular challenge for the preservation of ecologically permeable streams, so that no dams or similar structures can be considered. While the axial turbine design has prevailed in wind power, there is still no consensus for the generation of energy in free water flow conditions. In this work, an existing prototype of an unusual vertical axis Kirsten–Boeing turbine was investigated. A multivariate optimization process was created, in which all important machine parameters were checked and improved. By using neural networks as a metamodel coupled with flow simulations in ANSYS CFX, a broadly applicable optimization strategy is presented that yielded a blade design that is 36% more efficient than its predecessor in experiments. During the process, it was shown how to set up a complex sliding mesh problem with ANSYS expressions while evaluating a free surface problem.

Suggested Citation

  • Jan-Philipp Küppers & Jens Metzger & Jürgen Jensen & Tamara Reinicke, 2019. "Performance Optimization of a Kirsten–Boeing Turbine by A Metamodel Based on Neural Networks Coupled with CFD," Energies, MDPI, vol. 12(9), pages 1-26, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1777-:d:229991
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