Yu Zheng

The ballistic insert plate–gelatin configuration is a hard–soft coupled terminal-ballistic system for evaluating perforation resistance and post-perforation residual threat, but reliable prediction remains difficult because real ballistic tests are costly, sparse, and limited in test conditions. Objective: This study aims to develop a physics-guided small-sample modelling framework for jointly characterizing perforation probability, mass-dependent 50% perforation velocity, and residual velocity in tungsten-alloy fragment impact tests. Methods: Based on 21 impact tests, including 11 perforation cases, a continuous-mass logistic model was established to describe perforation probability as a function of impact velocity and projectile mass. The estimated mass-dependent 50% perforation velocity was used as the threshold anchor of a physics-based residual-velocity backbone, and four residual-learning models were introduced to correct the remaining prediction error. Their outputs were integrated using a reliability-gated ensemble determined by leave-one-out cross-validation errors. Results: The continuous-mass formulation enabled information sharing between two neighboring projectile-mass groups of approximately 3.5 g and 4.1 g. With feasible-domain constraints, the fitted backbone parameters were 3.912 and 0.663. For the present dataset, the integrated model achieved a root mean square error of approximately 31 m/s and a coefficient of determination of approximately 0.60, while physics-constrained data augmentation was used only for training regularization. Conclusions: The proposed framework provides an interpretable and experimentally constrained approach for small-sample insert–gelatin ballistic data, although broader validation with additional projectile masses, insert materials, gelatin conditions, and impact velocities is still required.

Equivalent-thickness conversion among metallic targets is important for vulnerability assessment and protection modelling, but conventional coefficients are often treated as material constants and seldom include fragment morphology. Penetration tests were conducted on 616 armor steel, Q235 steel, and LY12 aluminum alloy using steel spheres, tungsten spheres, and tungsten block fragments. A V50-based database was established, and failure mechanisms were examined from perforation morphology, exit-side SEM, and metallography. A semi-empirical ballistic-limit model was developed from key variables, with a residual correction term to describe the coupled deviation between fragment geometry and target response. The corrected model achieved a mean absolute percentage error of 7.91% and a coefficient of determination of 0.852. The conversion matrices show strong dependence on fragment type. For 616 steel converted to LY12 aluminum alloy, the coefficient increases from 2.50 for spherical fragments to 3.48 for tungsten blocks, giving a morphology-induced deviation of 39.03%. Thus, conversion should be defined by fragment type and target material rather than by a single material constant.

To support dynamic penetration decision-making, the millisecond-level real-time response requirement of missile attitude control systems requires efficient ballistic limit velocity (BLV) prediction models. This study proposes a deep learning model based on a YOLO–Mamba hybrid architecture, which achieves the adaptive modeling of multiphysical field coupling effects through feature cross-modules and polynomial expansion. The global feature extraction capability of YOLO and the local temporal modeling of Mamba synergistically enhance multiscale feature capture. In experiments, the model’s inference speed is 1.3 times greater than that of traditional methods, and its prediction error on ballistic datasets is reduced by 32.5–47.8% compared to those of SVM/random forests while maintaining a generalization accuracy of over 92% in data-scarce scenarios. The proposed model serves as a high-precision tool for the optimization of protective materials, and the YOLO–Mamba hybrid architecture offers a novel approach to data-driven modeling of complex impact dynamics problems.

Aiming at the difference of different networks in the penetration problem, the paper starts from the precision, time and so on. Comparison of deep neural networks (DNN), Decision Trees, Ran Dom Forests, XGBoost, and Support vector machine (SVM), shows that different networks are different in predicting the ballistic limit of ball penetration. The test results show that the predictive value of the decision tree is 0.01 higher than that of the depth neural network (DNN), but the use time is much longer than that of DNN. The predictions were 0.05 higher when compared with a deep neural network (DNN) using Random Forests, and the predictions were not evenly distributed but the usage time was much lower. When comparing SVM with DNN, the predicted value is 0.0.9 higher, but the Support vector machine time is much less, and the predicted value distribution is even and curvilinear. The predicted values were found to be 0.09 higher when compared with deep neural network (DNN) using XGBoost, but were much lower with the use of specimens (DNN) and the predicted values were unevenly distributed.

Nanoporous material liquid systems (NMLSs) demonstrate promising potential for blast protection due to their high energy absorption density. This investigation numerically evaluated the use of NMLSs in mitigating blast effects on fiber–composite circular structures. The coupled Eulerian–Lagrangian method was employed to establish the numerical models of fiber alone, water–fiber, and NMLS–fiber, subjected to the internal close-field blast loading. The simulations focused on a widely studied NMLS, nanoporous silica particles immersed in distilled water. Four NMLSs, featuring varying particle-to-water ratios yet identical densities to that of water, were designed to modulate the energy absorption capacity while maintaining identical mass. These NMLSs were modeled by Equation of State (EOS) compaction. The dynamic responses of the fiber circles in the simulations were compared to evaluate the blast mitigation of different liquids. When the explosive mass was relatively small or medium, both the water and NMLSs exhibited blast mitigation. The NMLSs outperformed water because the energy absorption capacity caused a greater attenuation of blast pressure in the NMLSs. In the small-mass explosive cases, all four NMLSs could rapidly reduce the blast pressure to the infiltration pressure but their wave impedances decreased as the particle-to-water ratio increased, resulting in that a NMLS with greater energy absorption capacity, however, had inferior blast mitigation performance. When the explosive mass was relatively large, all the fiber circles experienced significant fiber failure and only the NMLS with the greatest energy absorption capacity exhibited blast mitigation.

In order to solve the problems related to the prediction of penetration depth of composite materials, a new method combining physical mechanism and data driving is proposed in this paper. This method integrates 53 groups of ballistic experimental data and 278 groups of LS-DYNA simulation data to construct a data set with 23 characteristic parameters. Previous studies have shown that the ratio of the radius of the circular arc of the warhead to the diameter of the projectile (CRH) has a significant influence on the penetration depth of the projectile. This paper uses the hyperbolic tangent function (tanh(2CRH)) to calculate its saturation effect and conduct in-depth analysis. An adaptive noise injection method of concrete type is used in data processing, which can reduce the data distribution difference between C80 and C150 to 42%. At the same time, because of the modified sequential forward selection algorithm, the input dimension of the model is reduced by 56% and its important physical characteristics are retained. Finally, a Bayesian-optimized Bagging integrated model is constructed, which realizes the high accuracy prediction of RMSE (Root mean square error)=0.23 m and R²=0.90 on the test set. The test data show that when the fiber content reaches about 1%, the penetration resistance of the material is obviously improved. This discovery provides a new direction for optimizing protective materials. Compared with the traditional Forrestal theoretical model, this model not only reduces the prediction error by 92%, but also verifies the effectiveness and universality of the "physical mechanism-guided + data-driven correction" hybrid modeling method in complex penetration problems, providing a generalizable research paradigm for similar engineering mechanics problems that are difficult to fully parameterize.

The objective of this study was to study the penetration characteristics of tungsten alloy fragments on PCrNi3MoV target plates. Specifically, the penetration of square tungsten alloy fragments of gun steel target plates of different thicknesses were studied using a ballistic gun launching device. Based on the ballistic test results, the relationship between the bulge height of the target plate and the kinetic energy of the fragments was obtained, and the failure mode and process of the tungsten alloy fragments penetrating the steel target were analyzed. Then, the variation on the bulge height of the target plate with the kinetic energy of the fragments was obtained using dimensional analysis. The prediction results were consistent with the test results, and thus, the prediction model can be used in practical applications.

The mechanical properties of polyethylene (PE) materials are greatly influenced by their molecular structures, environmental temperature, and strain rate. In this study, static and dynamic compression tests were performed on two semicrystalline PE materials—ultrahigh molecular weight polyethylene (UHMWPE) and high-density polyethylene (HDPE). The stress–strain curves of HDPE and UHMWPE under uniaxial compression at temperatures of −40–120 °C and strain rates of 0.001–5500 s⁻¹ were obtained. The research findings suggest that both the UHMWPE and HDPE showed significant strain rate-strengthening effect and temperature-softening effect. In particular, HDPE exhibited better compression resistance and high-temperature resistance. The relationships between the yield stress and temperature and between the yield stress and strain rate for both materials were fitted, and the Cowper–Symonds constitutive model was built while considering the temperature effect. The parameters of the constitutive model were obtained and input into LS-DYNA software to simulate the dynamic compression process of HDPE. The simulation result was consistent with the test result, validating the accuracy of the constitutive parameters.

In this paper, the plastic flow and fracture behavior of 3D-printed Ti6Al4V (TC-4) alloy under different temperatures (289–1073 K) and strain rates (0.1–4100 s⁻¹) were studied by using the MTS comprehensive experimental machine (MTS) and split Hopkinson pressure bar (SHPB) equipment. The patterns of the influence of temperature and strain rate on the plastic flow behavior of 3D-printed materials in different printing directions were analyzed and compared with those of the traditional TC-4. Based on the experimental data, the modified Johnson–Cook (J-C) constitutive model of 3D-printed TC-4 alloy was established, and the plastic deformation behavior of the material driven by detonation was studied by X-ray photography. The research results showed that under static loading conditions, the strength of the material (AM-P-TC-4) along the printing direction was much higher than the strength of the material perpendicular to the printing direction (AM-T-TC-4). However, there was no difference in material strength for different directions under dynamic loading. Second, under the same deformation conditions, the strength of the 3D-printed TC-4 alloy was considerably higher than that of the traditional TC-4 alloy, but adiabatic shear fracture could be more easily induced under dynamic compressive deformation conditions for the 3D-printed TC-4 alloy, and its fracture strain was substantially less than that of TC-4 alloys. The modified J-C constitutive model established in this paper could better describe the plastic flow behavior of the AM-P-TC-4 alloy under high temperature and high-strain rate deformation conditions.