Shock Waves最新文献

The results of an experimental and kinetic modeling study of the ignition of (hbox {H}_{{2}}{-}hbox {CO}{-}hbox {O}_{{2}}{-}hbox {Ar}) mixtures behind the reflected shock wave are reported. The experiments were performed with test mixtures containing (0.75{-}3.0{%},hbox {H}_{{2}}), (0{%}{-}3.0{%},hbox {CO}), and (1.5{%},hbox {O}_{{2}}) in argon at temperatures from 950 to 1650 K and a total gas concentration of ({sim }10^{{-5}}~ hbox {mol}/hbox {cm}^{{3}}). The reaction was monitored by recording the time evolution of the pressure behind the reflected shock wave, intensity of the chemiluminescence of electronically excited OH* radicals at 308.0 ± 2.0 nm, and the absorption by ground-state OH radicals at a 306.772-nm bismuth atomic line. The measured parameters were the time (uptau _{{1}}) it took to reach a ground-state OH concentration of (2.0 times 10^{{-9}}~hbox {mol}/hbox {cm}^{{3}}) and the time (uptau _{{2}}) to reach the maximum OH* emission intensity. Kinetic simulations demonstrated that (uptau _{{1}}) corresponds to the beginning of fuel consumption, and (uptau _{{2}}) to the time for most of the fuel to be consumed. Therefore, the process of ignition was treated as consisting of two stages: the induction period (uptau _{{1}}) and the burnout time (uptau _{{2}}-uptau _{{1}}). These two time intervals demonstrate different sensitivity to the elementary reactions of the kinetic mechanism. A numerical model capable of predicting the effects of the presence of hydrocarbon impurity, oxygen vibrational relaxation, and pressure rise was used to simulate the experiment. The best agreement between experimental and theoretical results is achieved when these additional factors are taken into account. In addition to the sensitivity coefficient analysis for identifying the most important reactions, a new criterion, referred to as the relative integrated production, was proposed, which compliments the sensitivity coefficient analysis through its ability to identify the most productive reactions.

The factors that influence the symmetry of an imploding detonation are investigated experimentally and theoretically. Detonations in sub-atmospheric acetylene–oxygen were initiated and made to converge in an apparatus that followed that of Lee and Lee (Phys Fluids 8:2148–2152, 1965). The width of the test section was controlled with a wave-shaping insert, which formed the test section against the viewing window, creating an effectively two-dimensional problem with a channel width comparable to the detonation cell size. The convergence of the detonation was observed via self-luminous open-shutter photography and high-speed videography. The resulting videos were analyzed to quantify the wave speed, degree of asymmetry, and direction and magnitude of the offset in the center of convergence. To determine the experimental parameters that influence the symmetry of the imploding wave, the wave-shaping insert was intentionally canted by (0.3 ^{circ } {text {--}} 0.6^{circ }), accentuating the asymmetry of the imploding detonation. The experiment was modeled using a Huygens construction wherein the detonation is treated as a collection of wavelets, each assumed to propagate locally at a velocity determined by the channel width. The results of the model reproduced the observed offsets in detonation convergence from the center of the apparatus, confirming that velocity deficits resulting from the narrow channel width control the observed asymmetry.

High-speed imaging and digital signal processing are used to measure temperatures and pressures produced by explosions of solid chemical energetic materials. These measurements are used to enhance understanding of hazards faced by personnel working or training near explosions. The techniques described provide a complement to point measurements. Peak incident pressures studied are between 21 and 138 kPa, a region important for injury studies of personnel exposed to airborne shock.

Rotating detonation combustor (RDC) is a form of pressure gain combustion, which is thermodynamically more efficient than the traditional constant-pressure combustors. In most RDCs, the fuel–air mixture is not perfectly premixed and results in inhomogeneous mixing within the domain. Due to discrete fuel injection locations, local pockets of rich and lean mixtures are formed in the refill region. The objective of the present work is to gain an understanding of the effects of reactant mixture inhomogeneity on detonation wave structure, wave velocity, and pressure profile. To study the effect of mixture inhomogeneity, probability density functions of fuel mass fractions are generated with varying standard deviations. These distributions of fuel mass fractions are incorporated in 2D reacting simulations as a spatially/temporally varying inlet boundary condition. Using this methodology, the effect of mixture inhomogeneity is independently investigated to determine the effects on detonation wave propagation and RDC performance. As mixture inhomogeneity is increased, detonation wave speed, detonation efficiency, and potential for pressure gain all decrease, ultimately leading to the separation of the reaction zone from the shock wave.

Explosive breaching is a tactic operational professionals use to gain rapid entry and tactical advantage. This tactic exposes individuals to repeated low-level blasts (LLB), overpressure exposure generally occurring from user-directed munitions. The experimentation described in this paper highlights the need for further research into implementing explosives in tactical situations, specifically in confined areas, and the effects on individuals exposed. While current safety calculations predict peak pressures from an open-air detonation, this study incorporates the impulse of the total explosive event in a confined space. Sixteen explosive events were conducted to measure peak overpressures of the total duration of the event using pencil probes and flush mount-type sensors. These pressure sensors measured detonations at distances greater than or equal to the calculated minimum safe distances (MSD). The study compares these data with the Hopkinson–Cranz scaling law, the Weibull formula, and Kingery–Bulmash (KB) predictions. Additionally, a scaled mouse-to-human model for developing mild traumatic brain injury (mTBI) using pressure vs. impulse (P–I) graphs demonstrates areas of concern in the collected data. Results show that at distances exceeding the MSD, with personal protective equipment (PPE), and at pressures lower than those considered safe, mTBI is possible. Peak overpressures were measured to be 2.5 times higher than safety thresholds and impulses as high as 274 kPa ms. Confined area detonations produced 1.2–1.4 times greater pressures than open-air detonation measurements. Individuals who undergo breaching training will likely experience multiple exposures of this nature throughout their career, often occurring in rapid succession.

Application of microwave radar is a useful approach to gauge piston motion in a free-piston driver. One difficulty associated with conventional microwave technique is its spatial resolution during rapid velocity shifts at diaphragm rupture timings. This study, while departing from the standard practice of analyzing standing wave peaks, introduces an alternative by examining the phase shift of the microwave in-phase and quadrature signals. A compact free-piston-driven expansion tube, MX6.0, is used as the test bed for this technique. A microwave frequency of 4.2 GHz is used to take measurements in a compression tube with a diameter of 50 mm and a length of 2.0 m, tracking the motion of the piston. After arranging the microwave radar systems, the piston velocity and displacement trajectory are measured. Compared to the lower-resolution measurements using conventional microwave wavelength intervals, the use of microwave phase allowed for an exceptionally high spatial resolution in analyzing the piston motion.

The back pressure rise in a supersonic air intake could affect the engine performance and, in extreme conditions, result in a catastrophic unstart phenomenon. The present study compares different back pressure states that occur during an unstart of a mixed-compression air intake at Mach 3 using a fast-response pressure-sensitive paint, with an emphasis on the isolator flow. At low back pressure, the isolator dynamics is strongly correlated with the unsteadiness around the external compression corner. At high back pressure, a normal shock train dictates the isolator flowfield from its leading shock foot downstream. At the onset of unstart, an oblique shock train transpires involving large-scale flow separation, boundary layer thickening, and mitigated unsteadiness at the isolator floor. Like in previous studies, the prominence of low-frequency unsteadiness and upstream wave propagation is observed at high back pressure. However, in addition, the present study shows strong upstream communication of back pressure in a narrow frequency band through acoustic mechanisms, that eventually leads to the intake unstart. At the onset of unstart, the prominent frequency varies linearly along the isolator length, matching closely with the half-wave resonator model. Suppressing the oscillations at the preferred frequencies could be a promising control strategy to mitigate or delay intake unstart. When the intake unstarts, a 3D bifurcated shock stands at the inlet and the unsteady flow spillage takes place around oblique shocks off the sidewalls at low frequencies.

The Beirut port explosion on August 4, 2020, caused extensive destruction and significant casualties, prompting inquiries into its scale and impact on neighboring structures. Speculation arose regarding the role of the nearby port silos in shielding western Beirut from the blast. This study leverages insights from previous research and uses a tailored blast wave propagation model to settle the debate on the silos’ effectiveness in mitigating blast impacts. The analysis challenges prevailing notions: firstly, that the silos offered substantial protection, and secondly, the assumption linking the transient “window” phenomenon in the Wilson cloud to a similar opening in the preceding pressure front. Contrary to expectation, the pressure at the shock front remains continuous, albeit lower on the leeward side behind the silos. Downstream lateral regions experience pressure amplification due to the constructive interference of waves diffracted around the silos, with significant attenuation observed close (10 m) behind them—approximately 12%, 58%, and 2% of free-air values for overpressure, specific impulse, and specific energy, respectively. However, this shielding effect diminishes with distance, with the blast wave intensity largely restored at 450 m. Consequently, the silos’ shadowing effect was limited to nearby port structures and part of the Lebanese navy base, which still incurred severe damage. The lesser impact on western Beirut is attributed to its greater distance from the explosion rather than the silos’ protective influence. These findings suggest a reevaluation of urban disaster mitigation strategies, emphasizing geographical positioning over structural barriers and advocating for a holistic approach to urban resilience.

Understanding the mechanisms of shock-induced pore collapse is of great interest in various disciplines in sciences and engineering, including materials science, biological sciences, and geophysics. However, numerical modeling of the complex pore collapse processes can be costly. To this end, a strong need exists to develop surrogate models for generating economic predictions of pore collapse processes. In this work, we study the use of a data-driven reduced-order model, namely dynamic mode decomposition, and a deep generative model, namely conditional generative adversarial networks, to resemble the numerical simulations of the pore collapse process at representative training shock pressures. Since the simulations are expensive, the training data are scarce, which makes training an accurate surrogate model challenging. To overcome the difficulties posed by the complex physics phenomena, we make several crucial treatments to the plain original form of the methods to increase the capability of approximating and predicting the dynamics. In particular, physics information is used as indicators or conditional inputs to guide the prediction. In realizing these methods, the training of each dynamic mode composition model takes only around 30 s on CPU. In contrast, training a generative adversarial network model takes 8 h on GPU. Moreover, using dynamic mode decomposition, the final-time relative error is around 0.3% in the reproductive cases. We also demonstrate the predictive power of the methods at unseen testing shock pressures, where the error ranges from 1.3 to 5% in the interpolatory cases and 8 to 9% in extrapolatory cases.