Search

The analysis was performed on six commonly used 5.56 x 45 mm projectile types (M193, M855, M855A1, L31A1, M995, and AP45). The projectile's impact velocity (chosen as 900 m/s) was the same in the examination process. This made it possible to assess how the projectile's design has affected its performance against a hard steel target. These evaluations included numerical simulations (Ansys Autodyn) of projectile impacts. Using the available experimental data, the materials in the computational model were first validated. A short description and CAD model of projectiles is also given. Relevant conclusions were reported. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is well suited for the prediction of high-complexity, high-dimensional problems such as those encountered in terminal ballistics. We evaluate the performance of four popular ML-based regression models, extreme gradient boosting (XGBoost), artificial neural network (ANN), support vector regression (SVR), and Gaussian process regression (GP), on two common terminal ballistics' problems: (a) predicting the V50 ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments, and (b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness. To achieve this we utilise two datasets, each consisting of approximately 1000 samples, collated from public release sources. We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range. Although extrapolation is not advisable for ML-based regression models, for applications such as lethality/survivability analysis, such capability is required. To circumvent this, we implement expert knowledge and physics-based models via enforced monotonicity, as a Gaussian prior mean, and through a modified loss function. The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models, providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not. The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types, target materials and thicknesses, and impact conditions significantly more diverse than that achievable from any existing analytical approach. Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster. We provide some general guidelines throughout for the development, application, and reporting of ML models in terminal ballistics problems. [ABSTRACT FROM AUTHOR]

The ongoing pursuit of space and hypersonic flight continues to expose critical gaps in the understanding of material behavior under hypervelocity impact (HVI) and hypersonic flow conditions. Such limitations pose serious risks for aerospace vehicles, spacecraft, hardened structures, defensive systems, etc. Consequently, the development of materials and systems that can endure HVIs and hypersonic flight is a major obstacle in the quest for sustainable space exploration, reusable air-breathing hypersonic vehicles, and enduring protective structures. HVIs (≥ 3.0 km/s) can induce severe material deformation, erosion, fracturing, fragmentation, melting, vaporization, and sublimation. At the same time, hypersonic (≥ Mach 5) vehicles may be subjected to intense thermal and mechanical loads. Addressing these grand challenges requires a multifaceted and interdisciplinary approach, combining well-designed experiments with physics-based analytical and numerical modeling. Studying material behavior under HVIs and hypersonic conditions has been facilitated by two-stage light gas gun (2SLGG) aeroballistic ranges for almost seven decades. This current study surveys over 90 2SLGG aeroballistic ranges operational since 1990 to assess global launch and experimental capabilities. The 2SLGG's origins and research applications are explored, highlighting its significance in various fields, including shock physics, planetary science/defense, military defense, nuclear physics, hypersonic vehicle survivability and performance, and spacecraft micro-meteoroid/orbital debris protection. A summary of relevant HVI phenomena is presented to underscore the importance of 2SLGGs and to elucidate similarities and differences among various 2SLGG aeroballistic ranges and their supporting methods/tools. The 2SLGG's working principles are explained, and configurations and operations are compared. Modifications resulting in "three-stage light gas guns" are briefly mentioned for completeness. The full range of current 2SLGG performance capabilities is assessed with impact kinetic energies ranging from ∼ 10 joules to nearly 100 megajoules, and the facility survey results are used to explain the variations in aeroballistic range tankage, experiment types, research applications, and diagnostic systems. Finally, an overview of 2SLGG performance prediction methods is provided, featuring notable empirical, analytical, and numerical approaches. • Survey of over 90 2SLGG aeroballistic ranges active since 1990 worldwide. • 2SLGG's impact in multiple fields like military and space highlighted. • Detailed 2SLGG working principles and three-stage gun modifications explained. • 2SLGG performance spectrum from 10 J to 100 MJ detailed. • Variability in 2SLGG ranges and their scientific uses described. [ABSTRACT FROM AUTHOR]