Characterization of a novel open-ended shock tube facility based on detonation transmission tubing

Improved traceable measurement of the reflected step pressure in shock tube with the compensation of shock wave attenuation

Role of overdriven state of ignition wave in pre-detonators on detonation transition modes in a flat chamber

On the structures of compressible vortex rings generated by the compressible starting jet from converging and diverging nozzles

Schlieren, shadowgraph and other types of refraction-based techniques have been often used to study gas flow structures. They can capture strong density gradients, such as shock waves. Shock wave detection is a very important task in analyzing unsteady gas flows. High-speed imaging systems, including high-speed cameras, are widely used to record large arrays of shadowgraph images. To process large datasets of the high-speed shadowgraph images and automatically detect shock waves, convective plumes and other gas flow structures, two computer software systems based on the edge detection and machine learning with convolutional neural networks (CNN) were developed. The edge-detection software utilizes image filtering, noise removing, background image subtraction in the frequency domain and edge detection based on the Canny algorithm. The machine learning software is based on CNN. We developed two neural networks working together. The first one classifies the image dataset and finds images with shock waves. The other CNN solves the regression task and defines shock wave position (single number) based on image pixels tensor (3-D array of numbers) for each image. The supervised learning code based on example input-output pairs was developed to train models. It was shown, that the machine learning approach gives better results in shock wave detection accuracy, especially for low-quality images with a strong noise level. Software system for automated shadowgraph images processing and x-t curves of the shock wave and convective plume movement plotting was developed.

/pdf/edge-detection-and-machine-learning-approach-to-identify-4hoitahle4.pdf

Edge Detection and Machine Learning Approach to Identify Flow Structures on Schlieren and Shadowgraph Images

Schlieren and shadowgraph photography has been widely used to offer insight into the flow field in aerospace engineering due to their ease of application. The high-speed schlieren and shadowgraph techniques are typically applied to investigation of unsteady shock wave structure including shock reflection patterns and shock wave/boundary layer interactions, etc. Generally, qualitative analysis is performed based on the schlieren and shadowgraph image sequences. To process and analyze the large data set of the high-speed schlieren and shadowgraph images quantitatively, especially for the shock wave detection and tracking, a software was developed based on MATLAB GUI and its image processing toolbox. In this study, the image processing techniques exploited in the software, such as background subtraction, filter, threshold, edge detection, and shock tracking are presented. A case study on the phenomena of shock wave reflection from a solid surface was conducted. The results show that the proper filter method and the background image subtraction can effectively eliminate the image noises in frequency domain, which makes it easier to analyze the flow structure. Moreover, the instantaneous locations of shock waves are detected accurately, and the shock wave propagation speed calculated using the developed software are consistent with those of previous studies.

https://iopscience.iop.org/article/10.1088/1742-6596/1215/1/012021/pdf

Image Processing Techniques for Shock Wave Detection and Tracking in High Speed Schlieren and Shadowgraph Systems

Automatic shock detection, extraction and fitting in schlieren and shadowgraph visualization

To visualize the flow in the test section of an indraft supersonic wind tunnel in the University of Glasgow as long as possible, a background-oriented schlieren system was built up preliminarily. A MATLAB program based on a random dot algorithm developed in this study provides a fully customizable tool to generate background patterns with different sizes and dot densities. Background patterns produced by the in-house developed program then can be printed by a common ink-jet printer. To enhance the signal-noise ratio of the measurement system, white reflective film sheets, or semi-transparent paper can be employed. The correlation algorithm base on fast Fourier transform that is also applicable for PIV was chosen to process background oriented schlieren images. A validation test was performed to visualize the flow structure around a Pitot tube at M = 2.0. The experimental result proves that the BOS system established in this study is capable of visualizing the supersonic flow structure around the Pitot tube and sensitive enough to reveal weak density changes produced by the boundary layer, expansion waves, and weak oblique shock waves. Next, the current BOS system will be improved further by increasing the intensity of light sources to shorten the exposure time, using new cameras with better spatial resolution, and optimizing the background pattern.

Guoshuai Li

Papers

Characterization of a novel open-ended shock tube facility based on detonation transmission tubing

Image Processing Techniques for Shock Wave Detection and Tracking in High Speed Schlieren and Shadowgraph Systems

Automatic shock detection, extraction and fitting in schlieren and shadowgraph visualization

Development of unsteady background-oriented schlieren system in an indraft supersonic wind tunnel