Particles In Jet Research Articles

Shrouding Gas Plasma Deposition Technique for Developing Low-Friction, Wear-Resistant WS2-Zn Thin Films on Unfilled PEEK: The Relationship Between Process and Coating Properties

In this study, tungsten disulfide–zinc (WS2-Zn) composite films were generated on polyether ether ketone (PEEK) disks by an atmospheric pressure plasma jet (APPJ) equipped with a shrouding attachment. The friction and wear properties of the WS2-Zn coatings were intensively investigated by using a rotational ball-on-disk setup under dry sliding and ambient room conditions. In order to gain more information about the lubrication mechanism, the coating areas as deposited and the worn areas (i.e., in the wear track) were analyzed with a scanning electron microscope (SEM) with regard to their chemical composition in depth by energy-dispersive X-ray spectroscopy (EDS). X-ray photoelectron spectroscopy (XPS) was conducted to obtain precise chemical information from the surface. The results indicated that WS2-Zn coatings significantly improved the tribological properties, exhibiting a coefficient of friction (COF) of <0.2. However, the tribological performance of the coatings is strongly dependent on the plasma process settings (i.e., plasma current, dwell time of the powder particles in the plasma jet), which were tuned to reduce the oxidation by-products of WS2 to a minimum. The COF values achieved of the dry lubricant films were significantly reduced in contrast to the uncoated PEEK by a factor of four.

Ultra-high-energy gamma-ray bubble around microquasar V4641 Sgr.

Microquasars are laboratories for the study of jets of relativistic particles produced by accretion onto a spinning black hole. Microquasars are near enough to allow detailed imaging of spatial features across the multiwavelength spectrum. The recent extension measurement of the spatial morphology of a microquasar, SS 433, to TeV gamma rays1 localizes the acceleration of electrons at shocks in the jet far from the black hole2. V4641 Sagittarii (V4641 Sgr) is a similar binary system with a black hole and B-type main-sequence companion star and has an orbit period of 2.8 days (refs. 3,4). It stands out for its super-Eddington accretion5 and for its radio jet, which is one of the fastest superluminal jets in the Milky Way. Previous observations of V4641 Sgr did not report gamma-ray emission6. Here we report TeV gamma-ray emission from V4641 Sgr that reveals particle acceleration at similar distances from the black hole as SS 433. Furthermore, the gamma-ray spectrum of V4641 Sgr is among the hardest TeV spectra observed from any known gamma-ray source and is detected above 200 TeV. Gamma rays are produced by particles, either electrons or protons, of higher energies. Because energetic electrons lose energy more quickly the higher their energy, such a spectrum either very strongly constrains the electron-production mechanism or points to the acceleration of high-energy protons. This suggests that large-scale jets from microquasars could be more common than previously expected and that they could be a notable source of galactic cosmic rays7-9.

Characteristics of Shaped Charge Jets by the Shape of the Inhibitor Inserted into the Liner

The performance of a shaped charge bomb depends on the explosive performance, liner precision machining and manufacturing quality. The key performance is how uniformly the liner transforms into a jet. In order to reduce the performance of the shaped charge bomb from a protection point of view, this study investigated the characteristics of the jet formation and progression by inserting inhibitors of different shapes into the liner using flash X-ray experimental analysis techniques. The larger the volume filled inside the liner, the lower the rate of high-speed jet generation, which was well confirmed by experiments. Due to the effect of the inhibitor, it takes a considerable amount of time delay to form a jet after explosion compared to a normal shot, and quantity and mass of jet particles that can contribute to penetration are decreased, and the penetration power is also greatly reduced due to the scattering of segmented jets.

Proton acceleration in plasma turbulence driven by high-energy lepton jets

Abstract The interaction of high energy lepton jets composed of electrons and positrons with background electron-proton plasma is investigated numerically based upon particle-in-cell simulation, focusing on the acceleration processes of background protons due to the development of electromagnetic turbulence. Such interaction may be found in the universe when energetic lepton jets propagate in the interstellar media. When such a jet is injected into the background plasma, the Weibel instability is excited quickly, which leads to the development of plasma turbulence into the nonlinear stage. The turbulent electric and magnetic fields accelerate plasma particles via the Fermi II type acceleration, where the maximum energy both for electrons and protons can be accelerated to much higher than that of the incident jet particles. Because of background plasma acceleration, a collisionless electrostatic shock wave is formed, where some pre-accelerated protons are further accelerated when passing through the shock wave front. Dependence of proton acceleration on the beamplasma density ratio and beam energy is investigated. For a given background plasma density, the maximum proton energy generally increases both with the density and kinetic energy of the injected jet. Moreover, for a homogeneous background plasma, the proton acceleration both via turbulent fields and collisionless shocks is found to be significant. In the case of an inhomogeneous plasma, the proton acceleration in the plasma turbulence is dominant. Our studies illustrate a scenario where protons from background plasma can be accelerated successively by the turbulent fields and collisionless shocks.

Parametric study of free turbulent slurry jet: influence of particle size and particle concentration on the flow dynamics and thermal behavior of high-speed slurry jet

A detailed computational investigation was conducted to explore the dynamics of high-speed free slurry jets, with a focus on how variations in abrasive size and concentration affect their behavior. The study yielded several significant observations: Firstly, it was observed that slurry jets containing larger particles exhibited notably higher average velocities, attributed to their inherent self-similar characteristics. Specifically, at the far field of the jet, slurry jets with larger particle sizes demonstrated a 15% increase in velocity compared to those with smaller particles. Additionally, an analysis of turbulent intensity revealed that beyond a certain axial distance (x/D = 10), turbulence levels progressively increased. However, intriguingly, for slurry jets with a high concentration (Co = 15%), there was a notable decrease (28%) in turbulent intensity compared to water jets, indicating a complex interplay between particle size and concentration. Secondly, the study found that as the concentration of particles in the slurry jet increased, there was a corresponding rise in the bulk temperature of the jet. This phenomenon was primarily attributed to the heightened thermal conductivity resulting from the increased density of particles in the water. Furthermore, an examination of the Nusselt number revealed interesting trends. While the Nusselt number exhibited a peak near the nozzle exit, indicative of enhanced heat transfer in this region, it showed a decremental trend along the axial direction of the jet, attributable to jet divergence. In summary, the computational analysis provided valuable insights into the behavior of high-speed slurry jets, highlighting the intricate relationships between abrasive size, concentration, velocity distribution, and thermal characteristics. These findings contribute to a deeper understanding of slurry jet dynamics and have significant implications for various industrial applications.

Laser Powder Bed Fusion of Copper-Tungsten Powders Manufactured by Milling or Co-Injection Atomization Process.

The processing of pure copper (Cu) has been a challenge for laser-based additive manufacturing for many years since copper powders have a high reflectivity of up to 83% of electromagnetic radiation at a wavelength of 1070 nm. In this study, Cu particles were coated with sub-micrometer tungsten (W) particles to increase the laser beam absorptivity. The coated powders were processed by powder bed fusion-laser beam for metals (PBF-LB/M) with a conventional laser system of <300 watts laser power and a wavelength of 1070 nm. Two different powder manufacturing routes were developed. The first manufacturing route was gas atomization combined with a milling process by a planetary mill. The second manufacturing method was gas atomization with particle co-injection, where a separate W particle jet was sprayed into the atomized Cu jet. As part of the investigations, an extensive characterization of powder and additively manufactured test specimens was carried out. The specimens of Cu/W powders manufactured by the milling process have shown superior results. The laser absorptivity of the Cu/W powder was increased from 22.5% (pure Cu powder) to up to 71.6% for powders with 3 vol% W. In addition, a relative density of test specimens up to 98.2% (optically) and 95.6% (Archimedes) was reached. Furthermore, thermal conductivity was measured by laser flash analysis (LFA) and thermo-optical measurement (TOM). By using eddy current measurement, the electrical conductivity was analyzed. In comparison to the Cu reference, a thermal conductivity of 88.9% and an electrical conductivity of 85.8% were determined. Moreover, the Vickers hardness was measured. The effect of porosity on conductivity properties and hardness was investigated and showed a linear correlation. Finally, a demonstrator was built in which a wall thickness of down to 200 µm was achieved. This demonstrates that the Cu/W composite can be used for heat exchangers, heat sinks, and coils.

Azimuthal anisotropy of jet particles in p-Pb and Pb-Pb collisions at sNN\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\sqrt{{\ extrm{s}}_{\ extrm{NN}}} $$\\end{document} = 5.02 TeV

The azimuthal anisotropy of particles associated with jets (jet particles) at midrapidity is measured for the first time in p-Pb and Pb-Pb collisions at sNN\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\sqrt{{\ extrm{s}}_{\ extrm{NN}}} $$\\end{document} = 5.02 TeV down to transverse momentum (pT) of 0.5 GeV/c and 2 GeV/c, respectively, with ALICE. The results obtained in p-Pb collisions are based on a novel three-particle correlation technique. The azimuthal anisotropy coefficient v2 in high-multiplicity p-Pb collisions is positive, with a significance reaching 6.8σ at low pT, and its magnitude is smaller than in semicentral Pb-Pb collisions. In contrast to the measurements in Pb-Pb collisions, the v2 coefficient is also found independent of pT within uncertainties. Comparisons with the inclusive charged-particle v2 and with AMPT calculations are discussed. The predictions suggest that parton interactions play an important role in generating a non-zero jet-particle v2 in p-Pb collisions, even though they overestimate the reported measurement. These observations shed new insights on the understanding of the origin of the collective behaviour of jet particles in small systems such as p-Pb collisions, and provide significant stringent new constraints to models.

Measurement of Energy Correlators inside Jets and Determination of the Strong Coupling α_{S}(m_{Z}).

Energy correlators that describe energy-weighted distances between two or three particles in a hadronic jet are measured using an event sample of sqrt[s]=13 TeV proton-proton collisions collected by the CMS experiment and corresponding to an integrated luminosity of 36.3 fb^{-1}. The measured distributions are consistent with the trends in the simulation that reveal two key features of the strong interaction: confinement and asymptotic freedom. By comparing the ratio of the measured three- and two-particle energy correlator distributions with theoretical calculations that resum collinear emissions at approximate next-to-next-to-leading-logarithmic accuracy matched to a next-to-leading-order calculation, the strong coupling is determined at the Z boson mass: α_{S}(m_{Z})=0.1229_{-0.0050}^{+0.0040}, the most precise α_{S}(m_{Z}) value obtained using jet substructure observables.

Thermally Sprayed Coatings for the Protection of Industrial Fan Blades.

This paper presents a study on thermally sprayed coatings. Coatings produced by high-velocity oxygen-fuel spraying HVOF and plasma spraying deposited on the A03590 aluminum casting alloy are tested. The subject of this research concerns coatings based on tungsten carbide WC, chromium carbide Cr3C2, composite coatings NiCrSiB + 2.5%Fe + 2.5%Cr, mixtures of tungsten and chromium powders WC-CrC-Ni, mixtures of carbide powders with the Cr3C2-NiCr + the composite 5% NiCrBSi and WC-Co + 5% NiCrBSi. The aim of this research is to find a coating most resistant to the erosive impact of particles contained in the medium centrifuged by industrial rotors. The suitability of the coating is determined by its high level of microhardness. The hardest coatings are selected from the coatings tested and subjected to abrasion tests against a sand particle impact jet and the centrifugation of a medium with corundum particles. It is found that the most favorable anti-erosion properties are demonstrated by a coating composed of a mixture of tungsten carbide and chromium carbide WC-CrC-Ni powders. It is concluded that the greatest resistance of this coating to the erosive impact of the particle jet results from the synergistic enhancement of the most favorable features of both cermets.

OmniJet-α: the first cross-task foundation model for particle physics

Abstract Foundation models are multi-dataset and multi-task machine learning methods that once pre-trained can be fine-tuned for a large variety of downstream applications. The successful development of such general-purpose models for physics data would be a major breakthrough as they could improve the achievable physics performance while at the same time drastically reduce the required amount of training time and data. We report significant progress on this challenge on several fronts. First, a comprehensive set of evaluation methods is introduced to judge the quality of an encoding from physics data into a representation suitable for the autoregressive generation of particle jets with transformer architectures (the common backbone of foundation models). These measures motivate the choice of a higher-fidelity tokenization compared to previous works. Finally, we demonstrate transfer learning between an unsupervised problem (jet generation) and a classic supervised task (jet tagging) with our new OmniJet-α model. This is the first successful transfer between two different and actively studied classes of tasks and constitutes a major step in the building of foundation models for particle physics.

Study on the key parameters of ice particle air jet ejector structure

Existing ice particle jet surface treatment technology is prone to ice particle adhesion during application, significantly affecting surface treatment efficiency. Based on the basic structure of the jet pump, the ice particle air jet surface treatment technology is proposed for the instant preparation and utilization of ice particles, solving the problem of ice particle adhesion and clogging. To achieve efficient utilization of ice particles and high-speed jetting, an integrated jet structure for ice particle ejection and acceleration was developed. The influence of the working nozzle position (Ld), expansion ratio (n), and acceleration nozzle diameter ratio (Dn) length-to-diameter ratio (Ln) on the ice particle ejection and acceleration was systematically studied. The structural parameters of the ejector were determined using the impact kinetic energy of ice particles as the comprehensive evaluation index, and the surface treatment test was conducted to verify the results. The study shows that under 2 MPa air pressure, the ejector nozzle parameters of n = 1.5, Dn = 4.0, Ld = 4, and Ln = 0 mm can effectively eject and accelerate the ice particles. The aluminum alloy plate depainting test obtained a larger paint removal radius and resulted in a smoother aluminum alloy plate surface, reducing the surface roughness from 3.194 ± 0.489 μm to 1.156 ± 0.136 μm. The immediate preparation and utilization of ice particles solved the problems of adhesion and storage in the engineering application of ice particle air jet technology, providing a feasible technical method in the field of material surface treatment.

Nozzle Wear in Abrasive Water Jet Based on Numerical Simulation.

Particle diameters and jet pressure in abrasive water jet (AWJ) are significant jet properties which deserve a better understanding for improving AWJ machining performance. Some influence factors have been verified regarding nozzle wear in abrasive water jet polishing application. A three-dimensional model of a nozzle is established to analyze the influence of internal multi-phase flow field distribution, which is based on Euler-Lagrange methodology. With the increase of jet pressure, the erosion rate decreases; with the increase of the diameter and mass flow rate of the erosion particles, the erosion speed increases as well. When the diameter of the outlet is worn to 1.6 mm, the pressure on the work piece caused by the abrasive water jet increases by more than double compared to the non-worn nozzle; when the diameter of the nozzle outlet is worn to 1.6 mm, the shear force is 2.5 times higher than the shear force when the diameter is 1.0, which means that the jet force is divergent when the diameter is 1.6 mm, and the damage of the work piece is very serious. The obtained results could improve polishing efficiency on the work piece, extend nozzle lifetimes, and guide the future design of AWJ nozzles.

Optimizing High-Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server

With machine learning applications now spanning a variety of computational tasks, multi-user shared computing facilities are devoting a rapidly increasing proportion of their resources to such algorithms. Graph neural networks (GNNs), for example, have provided astounding improvements in extracting complex signatures from data and are now widely used in a variety of applications, such as particle jet classification in high energy physics (HEP). However, GNNs also come with an enormous computational penalty that requires the use of GPUs to maintain reasonable throughput. At shared computing facilities, such as those used by physicists at Fermi National Accelerator Laboratory (Fermilab), methodical resource allocation and high throughput at the many-user scale are key to ensuring that resources are being used as efficiently as possible. These facilities, however, primarily provide CPU-only nodes, which proves detrimental to time-to-insight and computational throughput for workflows that include machine learning inference. In this work, we describe how a shared computing facility can use the NVIDIA Triton Inference Server to optimize its resource allocation and computing structure, recovering high throughput while scaling out to multiple users by massively parallelizing their machine learning inference. To demonstrate the effectiveness of this system in a realistic multi-user environment, we use the Fermilab Elastic Analysis Facility augmented with the Triton Inference Server to provide scalable and high-throughput access to a HEP-specific GNN and report on the outcome.

End-to-end simulation of particle physics events with flow matching and generator oversampling

The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows looking for rare deviations that can be due to new phenomena not previously observed. We show that novel machine learning algorithms, specifically Normalizing Flows and Flow Matching, can be used to replicate accurate simulations from traditional approaches with several orders of magnitude of speed-up. The classical simulation chain starts from a physics process of interest, computes energy deposits of particles and electronics response, and finally employs the same reconstruction algorithms used for data. Eventually, the data are reduced to some high-level analysis format. Instead, we propose an end-to-end approach, simulating the final data format directly from physical generator inputs, skipping any intermediate steps. We use particle jets simulation as a benchmark for comparing both discrete and continuous Normalizing Flows models. The models are validated across a variety of metrics to identify the most accurate. We discuss the scaling of performance with the increase in training data, as well as the generalization power of these models on physical processes different from the training one. We investigate sampling multiple times from the same physical generator inputs, a procedure we name oversampling, and we show that it can effectively reduce the statistical uncertainties of a dataset. This class of ML algorithms is found to be capable of learning the expected detector response independently of the physical input process. The speed and accuracy of the models, coupled with the stability of the training procedure, make them a compelling tool for the needs of current and future experiments.

Jet substructure

Jet substructure observables hold the keys to identifying the inner working of the quark–gluon plasma through its imprints in jet modification patterns. Because of the multi-scale nature of jet evolution, the strategy has focused on developing jet reclustering-based and jet shape-based observables to specifically probe different stages of jet evolution, which provide multi-dimensional quantification of jet particle distribution phase spaces. The developments of jet substructure in the past few years allowed us to explore the correlations among jet observables. The richer information beyond single-observable modifications could give provides a crucial step beyond the energy loss paradigm. This review surveys the theory and phenomenology of modern jet substructure observables, which help constraining the energy scales of medium interactions and identifying heavy flavor quarks within jets. Also, the peculiar soft activities point to more refined studies of medium responses to jets.

TO HIGHLY PRODUCTIVE SYNTHESIS OF ALUMINUM NANOPARTICLES IN PLASMA FLOW AT ATMOSPHERIC PRESSURE

Purpose. To study the highly productive evaporation of aluminum micron powder in an atmospheric pressure plasma jet for the synthesis of nanoaluminum. Using special plasma technology, nanoparticles can be produced by rapid melting and evaporation of the initial micrometer particles and their subsequent re-nucleation. Research methods. Methods of mathematical and computer modeling of subsonic plasma turbulent jets at atmospheric pressure and experimental studies of two-phase processes during thermal plasma treatment using an arc plasma torch. Results. Based on computer modeling, a special reactor system was designed and developed, which includes a plasma-jet reactor with an electric arc plasma torch for the synthesis of aluminum nanoparticles. Numerical modeling makes it possible to determine the position of the melting point, evaporation and crushing of a molten particle, the evolution of the fractional composition of the dispersed phase, and find the speed and temperature of the particle in the area from its melting point to the crushing point. An experimental test of the operation of the reactor system was carried out using arc plasma torches with a power of 30 and 150 kW. It has been shown that intensifying the fragmentation of dispersed raw materials in a plasma jet can be useful in technologies for producing nanomaterials. The consequence of the fragmentation process is the redistribution of the fractional composition of the powder along the plasma jet and the accompanying changes in the dynamics of movement, heating and evaporation of particles. It has been determined that when the temperature of the largest aluminum particles reaches 2500 C, the total amount of evaporated mass is theoretically equal to 100%. The main parameters influencing the behavior of particles in a plasma jet are particle diameter, powder injection rate, flow rate, temperature and composition of the plasma gas. Taking these parameters into account will allow the process to operate at increased productivity. Scientific novelty. A mathematical description of the process of fragmentation a polydisperse powder, based on a continuum approach, has been obtained, which makes it possible to determine the position of the crushing point of a molten particle, the fractional composition of the dispersed phase, and find the speed and temperature of the particle in the area from its melting point to the point of crushing and evaporation. It was shown for the first time that it is possible to carry out a process in which complete evaporation of a molten drop is achieved due to the high enthalpy of the plasma before the end of mixing with steam. Practical value. A special reactor system has been designed and developed, which includes a plasma-jet reactor with an electric arc plasmatron for the synthesis of aluminum nanoparticles. The operating parameters of the reactor system have been determined, which will allow the synthesis of aluminum nanoparticles to be carried out with high productivity.

End-to-end codesign of Hessian-aware quantized neural networks for FPGAs

We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) hardware. Our approach leverages Hessian-aware quantization of NNs, the Quantized Open Neural Network Exchange intermediate representation, and the hls4ml tool flow for transpiling NNs into FPGA firmware. This makes efficient NN implementations in hardware accessible to nonexperts in a single open sourced workflow that can be deployed for real-time machine-learning applications in a wide range of scientific and industrial settings. We demonstrate the workflow in a particle physics application involving trigger decisions that must operate at the 40-MHz collision rate of the CERN Large Hadron Collider (LHC). Given the high collision rate, all data processing must be implemented on FPGA hardware within the strict area and latency requirements. Based on these constraints, we implement an optimized mixed-precision NN classifier for high-momentum particle jets in simulated LHC proton-proton collisions.

Mechanical mechanism of shear cavitating water jet dissociation of flake graphite

Abstract To obtain the mechanical mechanism of shear-type cavitation water jet dissociating flake graphite to improve the grinding technology of flake graphite, the motion state of flake graphite particles in shear-type cavitation water jet, the micro-jet impact process, the high-pressure elastic potential energy pressure release process, and the water wedge stretching process have been analyzed using the theory of hydraulic transport of liquid-solid two-phase, the theory of jets, the cavitation primordial principle, the principle of capacity conversion, and the water wedge action. The primary mechanical mechanism of shear cavitation water jet dissociation of flake graphite is collision friction effect, micro-jet impact, pressure release, and water wedge stretching effect, under these four kinds of loads individually or jointly, to achieve the purpose of removing the surface of flake graphite particles of the veinstone minerals to realize the dissociation of flake graphite. Moreover, the collision friction, pressure release, and water wedge stretching effects destroy the material under tensile stress to remove the veinstone minerals, and the micro-jet impact effect is the destruction of the material under compressive stress to remove the veinstone minerals.

Enhancing CO2 Injection Efficiency: Rock-Breaking Characteristics of Particle Jet Impact in Bottom Hole

Storing CO2 in oil and gas reservoirs offers a dual benefit: it reduces atmospheric CO2 concentration while simultaneously enhancing oil displacement efficiency and increasing crude oil production. This is achieved by injecting CO2 into producing oil and gas wells. Employing particle jet technology at the bottom of CO2 injection wells significantly expands the bottom hole diameter, thereby improving CO2 injection efficiency and storage safety. To further investigate the rock-breaking characteristics and efficiency, a finite element model for particle jet rock breaking is established by utilizing the smoothed particle hydrodynamics (SPH) method. Specifically, this new model considers the high temperature and confining pressure conditions present at the bottom hole. The dynamic response and fracturing effects of rock subjected to a particle jet are also revealed. The results indicate that particle jet impact rebound significantly influences the size of the impact crater, with the maximum first principal stress primarily concentrated on the crater’s surface. The impact creates a “v”-shaped crater on the rock surface, with both depth and volume increasing proportionally to jet inlet velocity and particle diameter. However, beyond a key particle concentration of 3%, the increase in depth and volume becomes less pronounced. Confining pressure is found to hinder particle impact rock-breaking efficiency, while high temperatures contribute to larger impact depths and breaking volumes. This research can provide theoretical support and parameter guidance for the practical application of particle impact technology in enhancing CO2 injection efficiency at the bottom hole.

Pressure propagation mechanism of open-cell aluminium foam within a pipe under hypervelocity impact

Low-density open-cell aluminium (Al) foam exhibits a different deformation mode under hypervelocity impacts compared to being subjected to medium-low speed and quasi-static compression. In this case, material flows between cells in the form of metal jets and propagates forward. This paper establishes the open-cell Al foam model based on Voronoi tessellation. Using numerical methods, one-dimensional hypervelocity constant velocity impact experiments of 1–6 km/s inside a closed pipe are conducted on Al foam with different internal structure parameters (average cell diameter: 2–3.5 mm; ligament width: 0.251–1 mm). Propagation mechanism of pressure waves in foams and the key factors affecting the propagation velocity are investigated. Three zones are divided according to the condition of jet distribution along the impact direction: unaffected zone, jet influence zone, and particle accumulation zone. The variation of the length of different zones with impact velocity is discussed: higher velocities result in longer jet influence zones but shorter particle accumulation zones. Meanwhile, length of zones is closely related to the width of Al foam ligament and cell pore diameter: the larger the diameter of cell pores and the smaller the width of ligaments, the longer the length of the jet influence zone. Time dependence is exhibited in the length of jet influence zone for the large cell pore diameter specimens(≥0.03 cm). Particle pressures are extracted using the one-dimensional averaging method, which propagates forward significantly lower than in dense materials. Dominant factors affecting the pressure wave propagation of open-cell Al foam, i.e., the internal unloading space, are obtained by combining two-dimensional hypervelocity impact simulation experiments with different boundary conditions. A strong correlation between metal jet distribution and particle pressure propagation is observed.