scholarly journals Pressure-informed velocity estimation in a subsonic jet

2022 ◽  
Vol 7 (1) ◽  
Author(s):  
Songqi Li ◽  
Lawrence Ukeiley
Author(s):  
Songqi Li ◽  
Wenyan Li ◽  
Lawrence Ukeiley

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.


2020 ◽  
Vol 140 (9) ◽  
pp. 1082-1090
Author(s):  
Hiroyuki Nakagomi ◽  
Yoshihiro Fuse ◽  
Yasuki Nagata ◽  
Hironaga Miyamoto ◽  
Masashi Yokotsuka ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dennis Pantke ◽  
Florian Mueller ◽  
Sebastian Reinartz ◽  
Fabian Kiessling ◽  
Volkmar Schulz

AbstractChanges in blood flow velocity play a crucial role during pathogenesis and progression of cardiovascular diseases. Imaging techniques capable of assessing flow velocities are clinically applied but are often not accurate, quantitative, and reliable enough to assess fine changes indicating the early onset of diseases and their conversion into a symptomatic stage. Magnetic particle imaging (MPI) promises to overcome these limitations. Existing MPI-based techniques perform velocity estimation on the reconstructed images, which restricts the measurable velocity range. Therefore, we developed a novel velocity quantification method by adapting the Doppler principle to MPI. Our method exploits the velocity-dependent frequency shift caused by a tracer motion-induced modulation of the emitted signal. The fundamental theory of our method is deduced and validated by simulations and measurements of moving phantoms. Overall, our method enables robust velocity quantification within milliseconds, with high accuracy, no radiation risk, no depth-dependency, and extended range compared to existing MPI-based velocity quantification techniques, highlighting the potential of our method as future medical application.


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