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This document details the resulting velocity of Mk279 Mod1 projectiles when fired from a RD24 barrel with between 15 and 110 grains of smokeless propellant to achieve velocities between 163 and 651m/s.
Reconstructing 3D granular microstructures within volumes of arbitrary geometries from limited 2D image data is crucial for predicting the material properties, as well as performances of structural components accounting for material microstructural effects. We present a novel generative learning framework that enables exascale reconstruction of granular microstructures within complex 3D geometric volumes. Building upon existing transfer learning techniques using pre-trained convolutional neural networks (CNN), we introduce several key innovations to overcome the difficulties inherent in arbitrary geometries. Our framework incorporates periodic boundary conditions using circular padding techniques, ensuring continuity and representativeness of the reconstructed microstructures. We also introduce a novel seamless transition reconstruction (STR) method that creates statistically equivalent transition zones to integrate multiple pre-existing 3D microstructure volumes. Based on STR, we propose a cost-effective strategy for reconstructing microstructures within complex geometric volumes, minimizing computational waste. Validation through numerical experiments using kinetic Monte Carlo simulations demonstrates accurate reproduction of grain statistics, including grain size distributions and morphology. A case study involving the reconstruction of a 4-blade propeller microstructure illustrates the method’s capability to efficiently handle complex geometries. In conclusion, the proposed framework significantly reduces computational demands while maintaining high reconstruction quality, paving the way for scalable microstructure reconstruction in materials design and analysis.
Nuclear thermal propulsion (NTP) using hydrogen as propellant in a solid-state fission reactor to reach temperatures of up to 3000K can achieve significantly higher specific impulse than chemical propulsion. A promising fuel that is again considered for NTP is U x Zr 1-x C y . To reduce the overall mass loss as well as the uranium fuel loss, a range of experiments using additional carbon in hot hydrogen (2600 K, 6 SLPM flow rate) were performed for up to 5-6 h. Both a CH 4 addition (0.1 and 0.2 vol%) in the hydrogen stream and the use of sacrificial graphite pieces upstream of the samples were tested, with sample compositions from 5 at% UC to 30 at% UC. The results showed a strong reduction of mass loss, up to a factor of 3, as well as a reduction of surface uranium losses in the presence of additional carbon in the atmosphere. Even with additional carbon in the atmosphere, material with 30 at% UC was not stable, but material with 20 at% UC was stable for up to 6h. The sample surfaces showed texturing after processing, possibly by grain growth on the surface. This led to a separation into grains with high uranium content close to the (100)-orientation, and with low uranium content in grains close to the (111)-orientation.