Pulse volume discharge behind shock wave in channel flow with obstacle

Znamenskaya, I. A.; Tatarenkova, D. I.; Иванов, И. Э.; Kuli-Zade, T.A.; Sysoev, N. N.

doi:10.1016/j.actaastro.2022.03.031

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…A rectangular obstacle was placed on the bottom wall of the test section in a supersonic flow. At a specific point in time, an electric discharge was switched on the lower wall of the test section initiating a shock wave spreading from the discharge [27]. Figure 3…”

Section: Methodsmentioning

confidence: 99%

Gas Flow Structures Detection on Shadowgraph Images and Their Matching to CFD Using Convolutional Neural Networks

Doroshchenko

Znamenskaya

Lutsky³

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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Shadowgraph imaging has been widely used to study flow fields in experimental fluid dynamics. Nowadays high-speed cameras allow to obtain millions of frames per second. Thus, it is not possible to analyze and process such large data sets manually and automatic image processing software is required. In the present study a software for automatic flow structures detection and tracking was developed based on the convolutional neural network (the network architecture is based on the YOLOv2 algorithm). Auto ML techniques were used to automatically tune model and hyperparameters and speed-up model development and training process. The neural network was trained to detect shock waves, thermal plumes, and solid particles in the flow with high precision. We successfully tested out software on high-speed shadowgraph recordings of gas flow in shock tube with shock wave Mach number M = 2-4.5. Also, we performed CFD to simulate the same flow. In recent decades, the amount of data in numerical simulations has grown significantly due to the growth in performance of computers. Thus, machine learning is also required to process large arrays of CFD results. We developed another ML tool for experimental and simulated by CFD shadowgraph images matching. Our algorithm is based on the VGG16 deep neural network for feature vector extraction and k-nearest neighbors algorithm for finding the most similar images based on the cosine similarity. We successfully applied our algorithm to automatically find the corresponding experimental shadowgraph image for each CFD image of the flow in shock tube with a rectangular obstacle in the flow channel.

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Section: Methodsmentioning

confidence: 99%

Gas Flow Structures Detection on Shadowgraph Images and Their Matching to CFD Using Convolutional Neural Networks

Doroshchenko

Znamenskaya

Lutsky³

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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“…The resulting quasi-two-dimensional flow around an obstacle can be split by a set of unsteady gas-dynamic structures: a bow shock, an oblique shock, a recirculation zone in the obstacle leeward region, an expanding wave fan (Prandtl-Meyer fan), reattachment shock, etc. [14]. In front of the reflected shock wave, a bifurcated structure is formed due to the propagation of the reflected shock along the boundary layer that developed behind the incident shock wave (Fig.…”

Section: Numerical Simulationmentioning

confidence: 99%

Heat Fluxes Visualization in High Speed Flow behind the Shock Wave

Znamenskaya,

Muratov,

Karnozova

et al. 2023

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The paper presents the thermographic studies of unsteady heat fluxes behind a plane shock wave in the rectangular 24x48 mm shock tube test section. Consecutive panoramic visualization of the heat fluxes plots on streamlined walls after the plane shock wave interaction with a rectangular obstacle fixed on the channel wall are obtained. The duration of the recorded thermal processes is up to 40 milliseconds after the shock wave passage. The heating and cooling of the test chamber walls streamlined by supersonic flow are visualized using the Telops FAST M200 high-speed infrared camera (operating range 1.5 – 5.1 microns) through the quartz windows transparent to infrared radiation. Visualization of the thermal fields were compared with the shadow images and results of 2D numerical simulation of a nonstationary gas dynamic process after the diffraction of a shock wave with Mach numbers M=2.0-4.5.

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Section: Figure 2 Shock Tube Scheme and X-t Plot Of Gas Flow Developm...mentioning

confidence: 99%

High-speed Flow Structures Detection and Tracking in Multiple Shadow Images with Matching to CFD using Convolutional Neural Networks

Doroshchenko¹,

Znamenskaya²,

Sysoev³

et al. 2022

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Shadowgraph imaging has been widely used to study flow fields in experimental fluid dynamics. Nowadays high-speed cameras allow to obtain millions of frames per second. Thus, it is not possible to analyze and process such large data sets manually and automatic image processing software is required. In the present study a software for automatic flow structures detection and tracking was developed based on the convolutional neural network (the network architecture is based on the YOLOv2 algorithm). Auto ML techniques were used to automatically tune model and hyperparameters and speed-up model development and training process. The neural network was trained to detect shock waves, thermal plumes, and solid particles in the flow with high precision. We successfully tested out software on high-speed shadowgraph recordings of gas flow in shock tube with shock wave Mach number M = 2-4.5. Also, we performed CFD to simulate the same flow. In recent decades, the amount of data in numerical simulations has grown significantly due to the growth in performance of computers. Thus, machine learning is also required to process large arrays of CFD results. We developed another ML tool for experimental and simulated by CFD shadowgraph images matching. Our algorithm is based on the VGG16 deep neural network for feature vector extraction and knearest neighbors algorithm for finding the most similar images based on the cosine similarity. We successfully applied our algorithm to automatically find the corresponding experimental shadowgraph image for each CFD image of the flow in shock tube with a rectangular obstacle in the flow channel.

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Pulse volume discharge behind shock wave in channel flow with obstacle

Cited by 8 publications

References 34 publications

Gas Flow Structures Detection on Shadowgraph Images and Their Matching to CFD Using Convolutional Neural Networks

Gas Flow Structures Detection on Shadowgraph Images and Their Matching to CFD Using Convolutional Neural Networks

Heat Fluxes Visualization in High Speed Flow behind the Shock Wave

High-speed Flow Structures Detection and Tracking in Multiple Shadow Images with Matching to CFD using Convolutional Neural Networks

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