会议
="Univ. Lyon CNRS Ecole Centrale de Lyon INSA Lyon Univ Claude Bernard Lyon I Laboratoire de Mecanique des Fluides et d'Acoustique UMR 5509 69310 Ecully France"
摘要 :
The coalescence of intersecting Mach waves has been proposed as a significant contributor to acoustic waveform steepening in the near field of a jet, thus providing a potential cause for the observation of more steepened waves in ...
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The coalescence of intersecting Mach waves has been proposed as a significant contributor to acoustic waveform steepening in the near field of a jet, thus providing a potential cause for the observation of more steepened waves in a laboratory-scale jet than predicted by effective Gol'dberg numbers [Baars et al., J. Fluid Mechanics 749 (2014); Fievet et al., AIAA Journal 54,254 (2016)]. Recent numerical simulations have demonstrated that the coalescence process can lead to increased steepening [Willis et al., AIAA SciTech Forum (2022)]. Schlieren imaging of a laboratory-scale, Mach 3 jet flow has been used for comparison with simulations based on reduced-order models, but additional examples of coalescing waves were desired that did not depend on turbulence for wave generation. Thus, two experiments have been designed using spark generated waveforms that intersect, either after reflection from a rigid surface or after emanating from a 3D-printed enclosure. Schlieren images and microphone measurements allow for analysis of these waveforms to examine steepening behavior. Additionally, large-eddy simulation (LES) of the same Mach 3 jet flow presents an opportunity to compare behavior of intersecting Mach waves with prior simulations and experimental results. A machine learning algorithm has been trained using transfer learning and then applied to the LES pressure data to identify waveforms of interest for further analysis of coalescence.
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