Detection Of Shock Waves Research Articles

A cluster analysis-based shock wave pattern recognition method for two-dimensional inviscid compressible flows

Compressible flows typically exhibit multiple shock waves which interact with each other, making the detection of these shock waves crucial for various aspects of flow studies including construction of high-order numerical schemes (e.g., shock-fitting), adaptive grid refinement, and flow visualization. This study aims to effectively identify and localize multiple shock waves and their interaction points in two-dimensional inviscid steady and unsteady flows. A novel shock wave pattern recognition method based on cluster analysis is proposed, including three processes. First, a series of grid-cells located at the transition zones of captured shock waves are extracted using a shock wave detection approach based on local flow variation. Subsequently, these grid-cells are grouped into numerous clusters using the classical K-means clustering algorithm, with categorization based on nearest neighbor features. Finally, a strategy is introduced to merge relevant adjacent clusters and further localize the points where shock waves interact. The Bézier curve fitting technique is then employed to obtain the high-quality shock-lines. Several numerical cases demonstrate that this method achieves high localization accuracy for shock-lines while being minimally affected by grid type and scale variations. Moreover, it enables clear and effective identification of the shock interaction patterns in both steady and unsteady flows, providing an effective visualization means for analyzing the motion and evolution of shock wave configurations.

Shock Wave Detection for In-Process Depth Measurement in Laser Ablation Using a Photonic Nanojet

Three-dimensional micro- and submicrometer-scale structures exhibit unique functions that cannot be obtained with bulk materials. To create such three-dimensional microstructures with high precision and efficiency, we proposed laser ablation using a photonic nanojet. A photonic nanojet is an optical beam with both a small beam diameter and a large depth of focus, which is obtained by irradiating a dielectric microsphere using a laser beam. In this study, we proposed an in-process depth measurement method to improve the machining accuracy of laser ablation using a photonic nanojet. We focused on the propagation characteristics of the shock waves generated during laser ablation. Shock waves were generated at the deepest point of the machining area and reached the microspheres as the pressure decayed, showing that different machining depths exerted different pressures on the microspheres. The microspheres were displaced by the pressure of the shock wave, and the amount of displacement depended on the pressure. Therefore, microspheres can be used as probes for shock wave detection, and the machining depth can be determined by measuring the displacement of microspheres during photonic nanojet machining. In this study, the displacement of a microsphere was measured simultaneously during photonic nanojet machining using a confocal optical system. From the obtained microsphere vibration data, the effect of the shock wave pressure was extracted, and the displacement of the microsphere due to the shock wave was obtained. When the hole depth varied from 155 to 1121 nm, the displacement of the microspheres varied from 0.58 to 0.03 µm. The experimental results show that the displacement of the microspheres vibrated by the shock wave decreased as the machining depth increased. This was due to an increase in the shock wave propagation distance and a decrease in the pressure of the shock wave as the machining depth increased. In conclusion, in-process depth measurements are possible in laser ablation using a photonic nanojet with a microsphere as a probe to detect shock waves.

Three-Dimensional Shock Topology Detection Method via Tomographic Reconstruction

Shock waves and shock-shock interaction are typical phenomena in supersonic or hypersonic flows that have significant impacts on aerodynamic performance. To obtain a comprehensive understanding of the mechanism of shock wave interaction, shock wave detection (SWD) methods are required. However, it is often challenging for most current SWD methods to identify the relationship between shock waves (also known as shock topology). To address this issue, this paper proposes a novel three-dimensional shock topology detection method based on the tomographic reconstruction strategy. This method involves extracting parallel slices from the flow field, then utilizing a two-dimensional shock topology recognition algorithm to obtain shock lines. Shock bands are obtained by connecting shock lines for every two adjacent slices, and shock surfaces are generated by assembling shock bands. Interaction lines are also formed by connecting interaction points. The detected shock wave is a structure composed of “point-line-band-surface”, and the topology relationship with other shock waves is obvious. Numerical results show that the shock waves detected by the proposed method can be categorized into families. Moreover, the shock surfaces generated by this method are free of gaps, holes, and un-physical fragments, which is an improvement over existing SWD methods.

An improved shock wave capturing method in high Mach numbers

This study describes an improved scheme that can suppress numerical instabilities caused by calculating strong shock waves at high Mach numbers. The current scheme combines a clear detection of shock waves based on a sonic point condition with a hybrid numerical flux used in a dissipative scheme and a low-precision interpolation only near shock waves. This scheme is easily obtained by combining existing schemes often used in hypersonic flows. Its performance in a two-dimensional system is compared to existing schemes using two inviscid flows at Mach numbers 15 and 20. The results show that the scheme outperforms existing schemes in terms of improving accuracy around shock waves. It is also discovered that this scheme can prevent nonphysical numerical oscillations caused by a mismatch between grid and local-shock forms. The significance of the scheme is demonstrated in the interaction field between the normal shock wave and the boundary layer in competition between viscosity and numerical dissipations. Therefore, the current scheme is useful in a high-Mach-number problem.

Background-oriented schlieren technique in experimental research regarding the effect of explosion pressure

In the following paper, experimental results regarding the effect of explosion pressure are obtained from open field experiments with detonation of explosive charges. In addition, sensors that can be used for security applications for the detection of toxic and explosive compounds, as well as mobile systems for the detection of shock waves due to explosions were used to acquire more detailed results. Sensors are the main components in products and systems used to detect chemicals and volatile organic compounds (VOCs) targeting applications in several fields, such as: industrial production and the automotive industry (detection of polluting gases from cars, medical applications, indoor air quality control. The sensory characteristics of a robot depend very much on its degree of autonomy, the applications for which it was designed and the type of work environment. The sensors can be divided into two categories: internal status sensors (sensors that provide information about the internal status of the mobile robot); external status sensors (sensors that provide information about the environment in which the robot operates). Another classification of these could be: distance sensors, position sensors, environmental sensors - sensors that provide information about various properties and characteristics of the environment (example: temperature, pressure, color, brightness), inertial sensors.

Shock polar investigation in supersonic rarefied gas flows over a circular cylinder

Well-known polars in classical shock wave theory, that is, flow deflection angle-shock angle (θ-β), hodograph (u*,v*), and pressure deflection (θ-P*) diagrams, are investigated for the rarefied gas flows using a recently proposed shock wave detection technique by Akhlaghi and coworkers. The agreement between the obtained polars with the analytical relations in classical shock wave theory has been shown in the continuum limit for the cases of supersonic flow over the wedge and cylinder geometries. Investigations are performed using the RGS2D direct simulation Monte Carlo solver for supersonic gas flows over a circular cylinder at continuum limit and Kn = 10−4, 10−3, 0.01, 0.03, 0.07, and 0.10. Two species of nitrogen and argon at various Mach numbers of 1.5, 3.0, and 10.0 are considered. The shock polars are investigated along bow shock waves in front of the cylinder. The results indicate that rarefaction significantly affects the shock polars. As Knudsen number increases, shock angle, maximum flow deflection angle, and aft shock pressure increase. However, velocity components after the shock wave decrease as the flow becomes more rarefied. These effects are stronger for θ-β polar under the weak shock condition. Meanwhile, they are stronger for θ-P* and hodograph polars in strong shock situations.

Variable speed limit strategy with anticipatory lane changing decisions

This paper develops a novel anticipatory Variable Speed Limit (VSL) control strategy that incorporates driver behavior based on trajectory data from probe vehicles. In particular, the research examines how the speed limit controls can be coordinated and optimized to reduce lane changing and overall braking, thus to achieve greater traffic throughput, safety, and sustainability. Driver behavior, such as acceleration/deceleration and lane changing, as reflected in individual trajectory data from GPS enabled probe vehicles are crucial for early detection of shockwave formation and proactive selection of speed limits; and consequently delay, or even eliminate breakdown formation. The core of this approach is the incorporation of a lane changing model which provides a more robust integrated speed limit selection and shockwave detection framework than the one we have developed in an earlier paper. The control principle is formulated in a generic fashion that finds the optimal speed limit control variables for either separate or simultaneous reduction of travel time, crash rates, and fuel consumption over a prediction horizon. The findings from the paper suggested that the VSL strategy was able to result in fewer lane changing rate (LCR) compared to the No-VSL case. Also, the developed algorithm was able to produce higher frequency of lower acceleration and deceleration rates than the No-VSL case, which indicated a smoother acceleration and deceleration pattern that corresponded to lower emission and fuel consumption. The performance of the algorithm was also examined under different probe penetration rates and congestion levels to identify the % of probe needed to achieve simultaneous mobility, safety, and environmental objectives.

A simulation assessment of shockwave detection and damping algorithms based on magnetometers and probe vehicle data

Nowadays, connected cars are uncommon on our streets, but their percentage is expected to grow uninterruptedly. These devices would provide a lot of data in real-time that can be used for road operators to improve the traffic. This research is focused on one of these applications, which is shockwave damping on freeways. This work evaluates two shockwave detection methods that use probe vehicle data and fixed sensors data and one mitigation algorithm that uses variable speed limits to resolve shockwaves. This paper analyses through microscopic traffic simulation the performance of the selected algorithms and how their parameters affect them in several scenarios. Also, the effect of the penetration rate of probe vehicle data is evaluated. Finally, the best algorithm with the best parameters configuration is applied to a realistic model of the AP7 freeway in Girona (Spain). The obtained results show that the algorithms applied greatly reduce the total travel time in this network.

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

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.

Accurate detection of shock waves and shock interactions in two-dimensional shock-capturing solutions

This paper describes an innovative technique that is capable of automatically detecting shock-wave shapes and shock-interaction patterns by post-processing the results obtained from a shock-capturing calculation using any of the available, state-of-the-art, shock-detection techniques. This new technique makes use of the Hough transform, the least-squares fit and the fuzzy logic to post-process the data supplied by classical shock-detection techniques; it not only allows to significantly enhance the quality of the extracted shock-fronts, but, even more importantly, allows to recognize the different types of shock-shock and shock-wall interactions that may occur within the flowfield.

Development of a Shockwave Detection Method based on Continuous Wavelet Transform using Vehicle Trajectory Data

Development of a Shockwave Detection Method based on Continuous Wavelet Transform using Vehicle Trajectory Data

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

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.

Traffic Shockwave Detection in a Connected Environment using the Speed Distribution of Individual Vehicles

Traffic shockwaves reflect a transition from the free-flow traffic state to the congested state. They create potentially unsafe situations for drivers, increase travel time, and significantly reduce freeway capacity. Several shockwave detection methods based on loop detector data and other traditional databases have been around for years. However, these methods face certain accuracy and reliability issues, many of which are due to the nature and accuracy of available data. Connected-vehicles technology is expected to provide reliable and accurate data about individual vehicles that can be potentially used for shockwave detection. Accordingly, this paper presents a novel method to identify shockwave formation and track its propagation based on the speed distribution of individual vehicles available through connected-vehicles technology. In addition, this paper analyzes the impact of partial connectivity on shockwave identification and compares the accuracy of the proposed method to a wavelet transformation-based method. Vehicle trajectories from the Next Generation Simulation (NGSIM) US-101 dataset were analyzed. The analysis shows a consistent pattern in which shockwave formation, indicated by a drop in speed propagating over space and time, is associated with a sharp increase in the value of speed standard deviation (SSD). Furthermore, the analysis shows that shockwaves can be accurately identified using vehicle trajectory data from connected vehicles at minimum 30% market penetration rates. Finally, the results show that the SSD of individual vehicles is more responsive to shockwave formation than the mean speed wavelet transformation, which can lead to improved shockwave detection accuracy.

Verification of the Flow Regimes Based on High-fidelity Observations of Bright Meteors

Abstract Infrasound monitoring has proved to be effective in detection of meteor-generated shock waves. When combined with optical observations of meteors, this technique is also reliable for detecting centimeter-sized meteoroids that usually ablate at high altitudes, thus offering relevant clues that open the exploration of the meteoroid flight regimes. Since a shock wave is formed as a result of a passage of the meteoroid through the atmosphere, the knowledge of the physical parameters of the surrounding gas around the meteoroid surface can be used to determine the meteor flow regime. This study analyzes the flow regimes of a data set of 24 centimeter-sized meteoroids for which well-constrained infrasound and photometric information is available. This is the first time that the flow regimes for meteoroids in this size range are validated from observations. From our approach, the Knudsen and Reynolds numbers are calculated, and two different flow regime evaluation approaches are compared in order to validate the theoretical formulation. The results demonstrate that a combination of fluid dynamic dimensionless parameters is needed to allow a better inclusion of the local physical processes of the phenomena.

A general objective shock wave detection from a geometric singular perturbation approach

For the general unsteady multi-dimensional flow, the non-linear non-equilibrium nature of shock waves is investigated from the geometric singular perturbation theory. With the introduction of a pressure non-equilibrium term, the modified Euler equation can be reduced to systems of ordinary differential equations(ODEs) along carefully constructed curves. Along each curve, a slow-fast system is derived from the governing ODEs, and the geometric singular perturbation theory is then applied. The motion of the slow-fast system is decomposed to two parts, the quasi-equilibrium slow motion where the non-equilibrium effect is negligible and the fast motion where the non-equilibrium effect plays a dominating role. It is then shown that a shock wave can be recognized as the fast motion of a slow-fast system in an objective manner, and this shock detection method can serve as a rational foundation for practical shock detection problem.

Shock wave attenuation in a micro-channel

This work presents optical measurements of shock wave attenuation in a glass micro-channel. This transparent facility, with a cross section ranging from $$1\,\hbox {mm}\times 150\,\upmu \hbox {m}$$ to $$1\,\hbox {mm}\times 500\,\upmu \hbox {m}$$ , allowed for the use of high-speed schlieren videography to visualize the propagation of a shock wave within the entire micro-channel and to quantify velocity attenuation of the wave due to wall effects. In this paper, we present the experimental technique and the relevant data treatment we have used to increase the sensitivity of shock wave detection. Then, we compared our experimental results for different channel widths, lengths, and shock wave velocities with the analytical model for shock attenuation proposed by Russell (J Fluid Mech 27(2):305–314, 1967), which assumes laminar flow, and by Mirels (Attenuation in a shock tube due to unsteady-boundary-layer action, NACA Report 1333, 1957) for turbulent flow. We found that these models are inadequate to predict the observed data, owing to the presence of fully developed flow which violates the basic assumption of these models. The data are also compared with the empirical shock attenuation models proposed by Zeitoun (Phys Fluids 27(1):011701, 2015) and Deshpande and Puranik (Shock Waves 26(4):465–475, 2016), where better agreement is observed. Finally, we presented experimental data for the flow field behind the shock wave from measurements of the Mach wave angle which shows globally decreasing flow Mach numbers due to viscous wall effects.

Performance characterisation of a passive cavitation detector optimised for subharmonic periodic shock waves from acoustic cavitation in MHz and sub-MHz ultrasound

We describe the design, construction and characterisation of a broadband passive cavitation detector, with the specific aim of detecting low frequency components of periodic shock waves, with high sensitivity. A finite element model is used to guide selection of matching and backing layers for the shock wave passive cavitation detector (swPCD), and the performance is evaluated against a commercially available device. Validation of the model, and characterisation of the swPCD is achieved through experimental detection of laser-plasma bubble collapse shock waves. The final swPCD design is 20 dB more sensitive to the subharmonic component, from acoustic cavitation driven at 220 kHz, than the comparable commercial device. This work may be significant for monitoring cavitation in medical applications, where sensitive detection is critical, and higher frequencies are more readily absorbed by tissue.

A hybrid numerical scheme for aeroheating computation of hypersonic reentry vehicles

Hypersonic aeroheating simulations were conducted for three-dimensional (3D) reentry capsule for the Mars Science Laboratory (MSL) using the AUSM and the Roe schemes. The study confirms that AUSM family of schemes generate unphysical oscillations in the wall heat flux distributions due to the unstable simulation of the shock wave. And the wall heat flux is underpredicted by the Roe scheme due to excess dissipation, even though captures the shock wave accurately. In order to overcome these limitations, we develop a numerical scheme that can capture the shock wave stably and still maintain an accurate prediction of the heat flux. We propose a novel hybrid scheme that combines both the AUSM+ and Roe schemes. Two different hybrid techniques are proposed for switching between the two schemes, one based on the shock wave detection and other utilizing a criterion based on the distance from the wall. The implementation of the two hybrid schemes for the 3D MSL reentry capsule was studied for prediction accuracy compared to experimental data. It is found that the second hybrid scheme (based on the criterion for switching based on distance from the wall) can eliminate the unphysical oscillations in the wall heat flux distributions and improve the aeroheating prediction accuracy.

Platoon-Based Cooperative Adaptive Cruise Control for Achieving Active Safe Driving Through Mobile Vehicular Cloud Computing

The cooperative adaptive cruise control (CACC) aims to achieve active safe driving that avoids vehicle accidents or a traffic jam by exchanging the road traffic information (e.g., traffic flow, traffic density, velocity variation, etc.) among neighbor vehicles. However, in CACC the butterfly effect is happened while exhibiting asynchronous brakes that easily lead to backward shockwaves and difficult to be removed. Thus, the driving stability is degraded significantly by backward shockwaves and affects the safe driving performance in CACC. Several critical issues should be addressed in CACC, including: (1) difficult to adaptively control the inter-vehicle distances among neighbor vehicles and the vehicle speed, (2) suffering from the butterfly effect, (3) unstable vehicle traffic flow, etc. For addressing above issues in CACC, this paper thus proposes the cooperative adaptive driving (CAD) approach that consists of three contributions: cooperative vehicle platooning (CVP), shockwave-avoidance driving (SAD), and adaptive platoon synchronization (APS). First, a platoon-based cooperative driving among neighbor vehicles is proposed in CVP. Second, in SAD, the predictive shockwave detection is proposed to avoid shockwaves efficiently. Third, based on the traffic states, APS determines the adaptive platoon length and velocity for achieving synchronous control and reduces the butterfly effect when vehicles suddenly brake. Numerical results demonstrate that the proposed CAD approach outperforms the compared approaches in number of shockwaves, average affection range of shockwaves, average vehicle velocity, and average travel time. Additionally, the adaptive platoon length is determined according to the traffic information gathered from the global and local clouds.

Lyapunov exponents and adaptive mesh refinement for high-speed flows using a discontinuous Galerkin scheme

This paper proposes two important improvements to shock-capturing strategies using a discontinuous Galerkin scheme, namely, accurate shock identification via finite-time Lyapunov exponent (FTLE) operators and efficient shock treatment through a point-implicit discretization of a PDE-based artificial viscosity technique. The advocated approach is based on the FTLE operator, originally developed in the context of dynamical systems theory to identify certain types of coherent structures in a flow. We propose the application of FTLEs in the detection of shock waves and demonstrate the operator's ability to identify strong and weak shocks equally well. The detection algorithm is coupled with a mesh refinement procedure and applied to transonic and supersonic flows. While the proposed strategy can be used potentially with any numerical method, a high-order discontinuous Galerkin solver is used in this study. In this context, two artificial viscosity approaches are employed to regularize the solution near shocks: an element-wise constant viscosity technique and a PDE-based smooth viscosity model. As the latter approach is more sophisticated and preferable for complex problems, a point-implicit discretization in time is proposed to reduce the extra stiffness introduced by the PDE-based technique, making it more competitive in terms of computational cost.