Jet formation and penetration performance of a double-layer charge liner with chemically-deposited tungsten as the inner liner

This paper proposes a type of double-layer charge liner fabricated using chemical vapor deposition(CVD)that has tungsten as its inner liner.The feasibility of this design was evaluated through penetration tests.Double-layer charge liners were fabricated by using CVD to deposit tungsten layers on the inner surfaces of pure T2 copper liners.The microstructures of the tungsten layers were analyzed using a scanning electron microscope(SEM).The feasibility analysis was carried out by pulsed X-rays,slug-retrieval test and static penetration tests.The shaped charge jet forming and penetration law of inner tungsten-coated double-layer liner were studied by numerical simulation method.The results showed that the double-layer liners could form well-shaped jets.The errors between the X-ray test results and the numerical results were within 11.07%.A slug-retrieval test was found that the retrieved slug was similar to a numerically simulated slug.Compared with the traditional pure copper shaped charge jet,the penetration depth of the double-layer shaped charge liner increased by 11.4% and>10.8% respectively.In summary,the test results are good,and the numerical simulation is in good agreement with the test,which verified the feasibility of using the CVD method to fabricate double-layer charge liners with a high-density and high-strength refractory metal as the inner liner.

High-speed penetration of ogive-nose projectiles into thick concrete targets:Tests and a projectile nose evolution model

The majority of the projectiles used in the hypersonic penetration study are solid flat-nosed cylindrical projectiles with a diameter of less than 20 mm.This study aims to fill the gap in the experimental and analytical study of the evolution of the nose shape of larger hollow projectiles under hypersonic penetration.In the hypersonic penetration test,eight ogive-nose AerMet100 steel projectiles with a diameter of 40 mm were launched to hit concrete targets with impact velocities that ranged from 1351 to 1877 m/s.Severe erosion of the projectiles was observed during high-speed penetration of heterogeneous targets,and apparent localized mushrooming occurred in the front nose of recovered projectiles.By examining the damage to projectiles,a linear relationship was found between the relative length reduction rate and the initial kinetic energy of projectiles in different penetration tests.Furthermore,microscopic analysis revealed the forming mechanism of the localized mushrooming phenomenon for eroding penetration,i.e.,material spall erosion abrasion mechanism,material flow and redistribution abrasion mechanism and localized radial upsetting deformation mechanism.Finally,a model of highspeed penetration that included erosion was established on the basis of a model of the evolution of the projectile nose that considers radial upsetting;the model was validated by test data from the literature and the present study.Depending upon the impact velocity,v0,the projectile nose may behave as undistorted,radially distorted or hemispherical.Due to the effects of abrasion of the projectile and enhancement of radial upsetting on the duration and amplitude of the secondary rising segment in the pulse shape of projectile deceleration,the predicted DOP had an upper limit.

Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.

The penetration depth of atomic radicals in tubes with catalytic surface properties

Catalysis of molecular radicals is often performed in interesting experimental configurations.One possible configuration is tubular geometry.The radicals are introduced into the tubes on one side,and stable molecules are exhausted on the other side.The penetration depth of radicals depends on numerous parameters,so it is not always feasible to calculate it.This article presents systematic measurements of the penetration depth of oxygen atoms along tubes made from nickel,cobalt,and copper.The source of O atoms was a surfatron-type microwave plasma.The initial density of O atoms depended on the gas flow and was 0.7×10^(21)m^(-3),2.4×10^(21)m^(-3),and 4.2×10^(21)m^(-3)at the flow rates of 50,300,and 600 sccm,and pressures of 10,35,and 60 Pa,respectively.The gas temperature remained at room temperature throughout the experiments.The dissociation fraction decreased exponentially along the length of the tubes in all cases.The penetration depths for well-oxidized nickel were 1.2,1.7,and 2.4 cm,respectively.For cobalt,they were slightly lower at 1.0,1.3,and 1.6 cm,respectively,while for copper,they were 1.1,1.3,and 1.7 cm,respectively.The results were explained by gas dynamics and heterogeneous surface association.These data are useful in any attempt to estimate the loss of molecular fragments along tubes,which serve as catalysts for the association of various radicals to stable molecules.

Error field penetration in J-TEXT tokamak based on two-fluid drift-MHD model

An externally generated resonant magnetic perturbation can induce complex non-ideal MHD responses in their resonant surfaces.We have studied the plasma responses using Fitzpatrick's improved two-fluid model and program LAYER.We calculated the error field penetration threshold for J-TEXT.In addition,we find that the island width increases slightly as the error field amplitude increases when the error field amplitude is below the critical penetration value.However,the island width suddenly jumps to a large value because the shielding effect of the plasma against the error field disappears after the penetration.By scanning the natural mode frequency,we find that the shielding effect of the plasma decreases as the natural mode frequency decreases.Finally,we obtain the m/n=2/1 penetration threshold scaling on density and temperature.

Machine learning optimization strategy of shaped charge liner structure based on jet penetration efficiency

Shaped charge liner(SCL)has been extensively applied in oil recovery and defense industries.Achieving superior penetration capability through optimizing SCL structures presents a substantial challenge due to intricate rate-dependent processes involving detonation-driven liner collapse,high-speed jet stretching,and penetration.This study introduces an innovative optimization strategy for SCL structures that employs jet penetration efficiency as the primary objective function.The strategy combines experimentally validated finite element method with machine learning(FEM-ML).We propose a novel jet penetration efficiency index derived from enhanced cutoff velocity and shape characteristics of the jet via machine learning.This index effectively evaluates the jet penetration performance.Furthermore,a multi-model fusion based on a machine learning optimization method,called XGBOOST-MFO,is put forward to optimize SCL structure over a large input space.The strategy's feasibility is demonstrated through the optimization of copper SCL implemented via the FEM-ML strategy.Finally,this strategy is extended to optimize the structure of the recently emerging CrMnFeCoNi high-entropy alloy conical liners and hemispherical copper liners.Therefore,the strategy can provide helpful guidance for the engineering design of SCL.

Data-driven prediction of dimensionless quantities for semi-infinite target penetration by integrating machine-learning and feature selection methods

This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.

Physics-data coupling-driven method to predict the penetration depth into concrete targets

The projectile penetration process into concrete target is a nonlinear complex problem.With the increase ofexperiment data,the data-driven paradigm has exhibited a new feasible method to solve such complex prob-lem.However,due to poor quality of experimental data,the traditional machine learning(ML)methods,whichare driven only by experimental data,have poor generalization capabilities and limited prediction accuracy.Therefore,this study intends to exhibit a ML method fusing the prior knowledge with experiment data.The newML method can constrain the fitting to experimental data,improve the generalization ability and the predic-tion accuracy.Experimental results show that integrating domain prior knowledge can effectively improve theperformance of the prediction model for penetration depth into concrete targets.

Mechanism of acupoint penetration acupuncture therapy regulating chondrocyte autophagy via the PI3K/Akt-mTOR pathway in KOA rats

Objective To investigate whether acupoint penetration acupuncture(APA)could regulate chondrocyte autophagy and apoptosis via the PI3K/Akt-mTOR signaling pathway to reduce cartilage degeneration in knee osteoarthritis(KOA)rats.Methods KOA was induced in rats via intra-articular injection of sodium iodoacetate resolution.Forty male Sprague-Dawley rats were randomly assigned to blank control,model,APA,electro-acupuncture(EA),and sham model groups(n=8)and those in the APA and EA groups received their respective therapies.Following completion of the treatment course,histological examinations of cartilage and muscle were conducted.Levels of apoptosis-and autophagy-related factors,including Bax,Bcl-2,mTOR,ULK-1,and Beclin-1 protein,and mRNAs were assessed.Additionally,β-endorphin(β-EP)concentrations in the brain and serum were measured.Results Histological analysis revealed that APA alleviated cartilage and muscle damage compared with the model group.APA inhibited cartilage degeneration by modulating the expression of apoptosis-and autophagy-related proteins and mRNA,thus preventing chondrocyte apoptosis.In the APA group,Bax and mTOR protein levels were significantly lower than those in the model group(both P=.024).Conversely,the Bcl-2 expression level was significantly higher than that in the EA group(P=.035).Additionally,ULK-1 expression was significantly lower than that in the EA group(P=.045).The mRNA level of Bax was significantly higher than that in the blank control group(P<.001).However,Beclin-1 levels were significantly higher than those in both the model and EA groups(both P<.001).ELISA results showed a significant decrease in the concentration ofβ-EP in the brains of the rats in the APA group compared with those in the model group(P=.032).Conclusions APA reduced osteoarthritis-related pain and alleviated cartilage damage by upregulating chondrocyte autophagy and down-regulating apoptosis via signaling pathways involving PI3K/Akt-mTOR in KOA rats.

Contemporary strategies and approaches for characterizing composition and enhancing biofilm penetration targeting bacterial extracellular polymeric substances

Extracellular polymeric substances(EPS)constitutes crucial elements within bacterial biofilms,facili-tating accelerated antimicrobial resistance and conferring defense against the host's immune cells.Developing precise and effective antibiofilm approaches and strategies,tailored to the specific charac-teristics of EPS composition,can offer valuable insights for the creation of novel antimicrobial drugs.This,in turn,holds the potential to mitigate the alarming issue of bacterial drug resistance.Current analysis of EPS compositions relies heavily on colorimetric approaches with a significant bias,which is likely due to the selection of a standard compound and the cross-interference of various EPS compounds.Considering the pivotal role of EPS in biofilm functionality,it is imperative for EPS research to delve deeper into the analysis of intricate compositions,moving beyond the current focus on polymeric materials.This ne-cessitates a shift from heavy reliance on colorimetric analytic methods to more comprehensive and nuanced analytical approaches.In this study,we have provided a comprehensive summary of existing analytical methods utilized in the characterization of EPS compositions.Additionally,novel strategies aimed at targeting EPS to enhance biofilm penetration were explored,with a specific focus on high-lighting the limitations associated with colorimetric methods.Furthermore,we have outlined the challenges faced in identifying additional components of EPS and propose a prospective research plan to address these challenges.This review has the potential to guide future researchers in the search for novel compounds capable of suppressing EPS,thereby inhibiting biofilm formation.This insight opens up a new avenue for exploration within this research domain.

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Interpretation and characterization of rate of penetration intelligent prediction model

Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its credibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.

Enhancing Mobile Security through Comprehensive Penetration Testing

In today’s era, where mobile devices have become an integral part of our daily lives, ensuring the security of mobile applications has become increasingly crucial. Mobile penetration testing, a specialized subfield within the realm of cybersecurity, plays a vital role in safeguarding mobile ecosystems against the ever-evolving landscape of threats. The ubiquity of mobile devices has made them a prime target for cybercriminals, and the data and functionality accessed through mobile applications make them valuable assets to protect. Mobile penetration testing is designed to identify vulnerabilities, weaknesses, and potential exploits within mobile applications and the devices themselves. Unlike traditional penetration testing, which often focuses on network and server security, mobile penetration testing zeroes in on the unique challenges posed by mobile platforms. Mobile penetration testing, a specialized field within cybersecurity, is an essential tool in the Cybersecurity specialists’ toolkit to protect mobile ecosystems from emerging threats. This article introduces mobile penetration testing, emphasizing its significance, including comprehensive learning labs for Android and iOS platforms, and highlighting how it distinctly differs from traditional penetration testing methodologies.

Impact point prediction guidance of ballistic missile in high maneuver penetration condition

An impact point prediction(IPP) guidance based on supervised learning is proposed to address the problem of precise guidance for the ballistic missile in high maneuver penetration condition.An accurate ballistic trajectory model is applied to generate training samples,and ablation experiments are conducted to determine the mapping relationship between the flight state and the impact point.At the same time,the impact point coordinates are decoupled to improve the prediction accuracy,and the sigmoid activation function is improved to ameliorate the prediction efficiency.Therefore,an IPP neural network model,which solves the contradiction between the accuracy and the speed of the IPP,is established.In view of the performance deviation of the divert control system,the mapping relationship between the guidance parameters and the impact deviation is analysed based on the variational principle.In addition,a fast iterative model of guidance parameters is designed for reference to the Newton iteration method,which solves the nonlinear strong coupling problem of the guidance parameter solution.Monte Carlo simulation results show that the prediction accuracy of the impact point is high,with a 3 σ prediction error of 4.5 m,and the guidance method is robust,with a 3 σ error of 7.5 m.On the STM32F407 singlechip microcomputer,a single IPP takes about 2.374 ms,and a single guidance solution takes about9.936 ms,which has a good real-time performance and a certain engineering application value.

Experimental and Theoretical Study on the Self-Weight Penetration Velocity of Suction Anchor Foundations in Sand

The application of the wellhead suction anchor in the second production test of natural gas hydrates(NGHs)in the South China Sea(SCS)was met with success.This design incorporates a central conductor guide pipe,which distinguishes it from traditional suction foundations.However,this addition resulted in a relatively high penetration resistance and a shallower penetration depth at the self-weight penetration stage.To mitigate this issue,the current study proposes an optimized design where the end of the suction foundation is sharpened.The installation characteristics of the traditional suction foundation and new suction foundation during self-weight penetration into sand are studied through laboratory tests and theoretical analysis.The flat and sharpened bottom shapes are considered in the traditional and new suction models.The effects of the initial penetration velocity on the initial penetration depth and soil plug and impact cavity characteristics are systematically studied.The results show that the self-weight penetration depth of the foundation with a sharpened bottom is 44.5%deeper than that of the foundation with a flat bottom.There are cavities around the foundation at the self-weight penetration stage,and the penetration depth is overestimated by 15%-30%.Finally,a model for predicting the penetration depth of the new suction foundation is proposed.

Penetration Tests of Multi-Bucket Foundation in Sand for Offshore Wind Turbines

Suction foundations are generally installed with negative pressures to overcome the resistance of soils and complete the penetration,but excessive negative pressures are also avoided to cause seepage damages.In this paper,the model test method was used to analyze the movement characteristics of multi-bucket foundations in the process of sinking in sand,and the common calculation methods of sinking resistances are verified.The critical negative pressure corresponding to the seepage failure of foundation was determined under the action of increasing negative pressure step by step and the characteristics of soil failure were studied.The calculation formula of critical suction in sand was verified in application,and according to the test results,the value of seepage coefficient was modified,which provides an example for the study of suction foundation in sand soils.

Investigating a Vitamin D Delivery Toothpaste Using a Penetration Enhancer Compound

Expanding in the oral care business, being passionately driven by innovative and scientific products, functional toothpaste has recently become more popular for functionality, variety, and efficacy. Many new types of toothpaste are commercially manufactured with diverse fragrances, colors, probiotics, and pharmaceutical ingredients to enhance the functionalities of toothpaste. Our study attempted to create a toothpaste formulation that might facilitate the intraoral delivery of vitamin D3 into the bloodstream. Simply brushing our teeth with toothpaste should be easy to take the essential vitamin regularly. In this study, an emulsion-based toothpaste mixed with an azone compound and sodium dodecyl sulfate as penetration enhancers blended thoroughly with other ingredients and then with vitamin D. Multiple toothpaste characteristic tests were performed, such as abrasiveness, scratchiness, spreadability, pH, foaming, cleaning, and antibacterial strength with our vitamin D toothpaste, and compared with those of other commercial brand toothpaste. To confirm the intraoral delivery of vitamin D through the oral cavity, an earthworm transport study and TEER value test were conducted using L. terrestris skin. Our data demonstrated the high feasibility of intraoral delivery of vitamin D based on those two skin studies with various experimental support;our vitamin D toothpaste had comparable characteristics with other commercial toothpaste for cleaning functionality.

Reactive jet density distribution effect on its penetration behavior

In this paper,the penetration mechanism of reactive jet with non-uniform density distribution is studied.The simulations show that the density deficit occurs in the whole reactive jet,and the density increases from the jet tip to tail.The density of jet tip is approximately 1.5 g/cm3,which is lower than that of the reactive liner materials.The X-ray experiments show similar results with the simulations.The density decreasing effect of jet tip has a significant influence on the penetration behavior when the reactive jet impacts steel plate.According to the simulation results,this paper assumes that the density gradient in the jet section has linear distribution.Then,the deflagration pressure generated by each jet element at the bottom of crater is introduced into the Bernoulli equation.Based on the virtual origin model and Szendrei-Held equation,the analytical models for penetration depth and radial cratering of reactive jet with the density reduction are obtained.Moreover,to further prove the validity of analytical models,the penetration experiments of the reactive liner shaped charge against steel plate under different standoffs are carried out.There is a convergence between the analytical crater profiles and experimental results when reactive jets penetrate steel plates under different standoffs,especially at standoff of 1.5 and 2.0charge diameters.

Study of the axial density/impedance gradient composite long rod hypervelocity penetration into a four-layer Q345 target

Based on the dynamic shock response of the material and structure,the hypervelocity impact processes and mechanisms of long composite rods with axial density/impedance gradients penetration into fourlayer targets were studied through experiments and numerical simulation methods.The propagation law of the shock waves,together with the structural responses of the projectiles and targets,the formation and evolution of the fragment groups formed during the processes and their distributions were described.The damage of each target plate was quantitatively analysed by comparing the results of the experiment and numerical simulation.The results showed that the axial density/impedance gradient projectiles could decrease the impact pressure to a certain extent,and the degree of damage to the target plate decreased layer by layer when the head density/impedance of the projectile was high.When the head density/impedance of the projectile was low,the degree of target damage first increased layer by layer until the projectile was completely eroded and then it decreased.The results can provide a reference for the design and application of long rods with axial composite structure for velocities ranging from 6 to 10 Ma or greater.

The Magnetic Penetration Depth Calculated with the Mechanism of “Close-Shell Inversion”

Meissner effect is one of the two fundamental properties of superconductors, it allows them to actively exclude external magnetic fields from their interior, leaving the field to decay quickly from the surface to the interior within a very thin layer whose thickness is characterized by the penetration depth . Based on the mechanism of “close-shell inversion” for superconductivity proposed earlier, we proceed in this paper to calculate the magnetic penetration depth. It is found that repelling the external magnetic field is just a spontaneous and dynamic response of conduction electrons in superconductors. Calculation results show that the net magnetic field decays exponentially, in consistent with the existing theories and experimental data. .