Time-resolved Velocity Estimation from Inflow Pressure Measurements in a Subsonic Jet Using Machine-Learning Methods


  • Songqi Li University of Florida, United States of America
  • Wenyan Li Comcast Applied AI Research Lab
  • Lawrence Ukeiley University of Florida, United States of America




Flow Field Estimation, Machine Learning


The goal of this study is to estimate aspects of the time-resolved (TR) velocity field that is associated with pressure fluctuations measured in a subsonic jet using machine learning (ML) approaches. The experiments were conducted in the Anechoic Jet Test Facility at the University of Florida using a round converging nozzle operated at at a Mach number of 0.3 and ReD = 3.8 × 105. Planar PIV was utilized to record nonTR, 2D velocity snapshots on the streamwise plane. A B&K 4138 1/8” microphone and a GRAS 46DD 1/8” microphone were employed to measure inflow pressure fluctuations synchronously with the PIV. Both microphones were equipped with aerodynamically-shaped nosecones and were placed on the upper and lower jet liplines. The nosecone tips were streamwisely aligned and were placed just downstream of the PIV window (see Figure 1(a)). Pressure signals were recorded synchronously with PIV, but at different sampling rates, 80 kHz and 12 Hz, respectively. A total of 8000 PIV snapshots were  acquired in the experiment.

Author Biography

Lawrence Ukeiley, University of Florida, United States of America

Larry Ukeiley is currently a Professor in the Mechanical and Aerospace Engineering Department at the University of Florida.






Deep Learning and Data Assimilation