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Assessment of statistical sampling methods and approximation models applied to aeroacoustic and vibroacoustic problems

  • Biedermann, Till M. (Institute of Sound and Vibration Engineering ISAVE, University of Applied Sciences Duesseldorf) ;
  • Reich, Marius (Centre of Innovative Energy Systems ZIES, University of Applied Sciences Duesseldorf) ;
  • Kameier, Frank (Institute of Sound and Vibration Engineering ISAVE, University of Applied Sciences Duesseldorf) ;
  • Adam, Mario (Centre of Innovative Energy Systems ZIES, University of Applied Sciences Duesseldorf) ;
  • Paschereit, C.O. (Institute of Fluid Dynamics and Technical Acoustics ISTA, Technical University Berlin)
  • Received : 2018.09.05
  • Accepted : 2019.01.28
  • Published : 2019.11.25
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Abstract

The effect of multiple process parameters on a set of continuous response variables is, especially in experimental designs, difficult and intricate to determine. Due to the complexity in aeroacoustic and vibroacoustic studies, the often-performed simple one-factor-at-a-time method turns out to be the least effective approach. In contrast, the statistical Design of Experiments is a technique used with the objective to maximize the obtained information while keeping the experimental effort at a minimum. The presented work aims at giving insights on Design of Experiments applied to aeroacoustic and vibroacoustic problems while comparing different experimental designs and approximation models. For this purpose, an experimental rig of a ducted low-pressure fan is developed that allows gathering data of both, aerodynamic and aeroacoustic nature while analysing three independent process parameters. The experimental designs used to sample the design space are a Central Composite design and a Box-Behnken design, both used to model a response surface regression, and Latin Hypercube sampling to model an Artificial Neural network. The results indicate that Latin Hypercube sampling extracts information that is more diverse and, in combination with an Artificial Neural network, outperforms the quadratic response surface regressions. It is shown that the Latin Hypercube sampling, initially developed for computer-aided experiments, can also be used as an experimental design. To further increase the benefit of the presented approach, spectral information of every experimental test point is extracted and Artificial Neural networks are chosen for modelling the spectral information since they show to be the most universal approximators.

Keywords

Acknowledgement

The authors gratefully acknowledge the kind support of M.Sc. students Nils Hintzen and Matthias Rother as well as B.Eng. student Nina Balde in setting up the test rig and conducting the experiments.

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