Multi-Objective Modeling of Leading-Edge Serrations Applied to Low-Pressure Axial Fans

  • A novel modeling strategy is proposed which allows high-accuracy predictions of aerodynamic and aeroacoustic target values for a low-pressure axial fan, equipped with serrated leading edges. Inspired by machine learning processes, the sampling of the experimental space is realized by use of a Latin hypercube design plus a factorial design, providing highly diverse information on the analyzed system. The effects of four influencing parameters (IP) are tested, characterizing the inflow conditions as well as the serration geometry. A total of 65 target values in the time and frequency domains are defined and can be approximated with high accuracy by individual artificial neural networks. Furthermore, the validation of the model against fully independent test points within the experimental space yields a remarkable fit, even for the spectral distribution in 1/3-octave bands, proving the ability of the model to generalize. A metaheuristic multi-objective optimization approach provides two-dimensional Pareto optimal solutions for selectedA novel modeling strategy is proposed which allows high-accuracy predictions of aerodynamic and aeroacoustic target values for a low-pressure axial fan, equipped with serrated leading edges. Inspired by machine learning processes, the sampling of the experimental space is realized by use of a Latin hypercube design plus a factorial design, providing highly diverse information on the analyzed system. The effects of four influencing parameters (IP) are tested, characterizing the inflow conditions as well as the serration geometry. A total of 65 target values in the time and frequency domains are defined and can be approximated with high accuracy by individual artificial neural networks. Furthermore, the validation of the model against fully independent test points within the experimental space yields a remarkable fit, even for the spectral distribution in 1/3-octave bands, proving the ability of the model to generalize. A metaheuristic multi-objective optimization approach provides two-dimensional Pareto optimal solutions for selected pairs of target values. This is particularly important for reconciling opposing trends, such as the noise reduction capability and aerodynamic performance. The chosen optimization strategy also allows for a customized design of serrated leading edges, tailored to the specific operating conditions of the axial fan.show moreshow less

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Metadaten
Author:Till M. BiedermannORCiD, M. Reich, C. O. Paschereit
DOI:https://doi.org/10.1115/1.4048599
ISSN:0742-4795
Parent Title (English):Journal of Engineering for Gas Turbines and Power
Publisher:ASME International
Document Type:Article
Language:English
Date of first Publication:2020/10/26
Reviewed:Begutachtet/Reviewed
Release Date:2024/08/12
Tag:Acoustics; Artificial neural networks; Blades; Design; Fans; Flow (Dynamics); Modeling; Noise (Sound); Noise control; Optimization; Pareto optimization; Pressure; Rotors; Signals; Turbulence; Wavelength
Volume:142
Issue:11
Article Number:GTP-20-1406
Pagenumber:13
institutes:Fakultät Maschinenbau und Versorgungstechnik
Research Themes:Energie & Ressourcen
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