Enhanced ignition behavior of reactive material projectiles impacting fuel-filled tank

Reactive material projectiles can be an extremely efficient lethality enhancement technology that incorporates the defeat mechanisms of chemical energy and kinetic energy.This paper presents such a research on the enhanced ignition behavior of reactive material projectiles impacting a fuel-filled tank.Firstly,the ignition process description of the fuel-filled tank impacted by inert metal and reactive material projectiles is presented.Secondly,ballistic impact experiments are performed to investigate the ignition effects of the fuel-filled tank impacted by reactive material versus tungsten alloy projectiles with mass matched.The fuel tank used for the experiments is a cylindrical steel casing structure filled with aviation kerosene and sealed with aluminum cover plates on both ends using screw bolts.The experimental results indicate that,compared with impacts from tungsten alloy projectiles,there is dramatically enhanced structural damage to the fuel-filled tank and an enhanced ignition effect caused by reactive material projectile impacts.Finally,an analytical model is developed,by which the effects of the aluminum cover plate thickness on critical structural failure energy of the fuel-filled tank and the total energy of the reactive material projectile deposited into the fuel-filled tank are discussed.The analysis shows a good agreement with the experiments.

A Collaborative Filtering Recommendation Algorithm Based on the Difference and the Correlation of Users’Ratings

The traditional similarity algorithm in collaborative filtering mainly pay attention to the similarity or correlation of users’ratings,lacking the consideration of difference of users’ratings.In this paper,we divide the relationship of users’ratings into differential part and correlated part,proposing a similarity measurement based on the difference and the correlation of users’ratings which performs well with non-sparse dataset.In order to solve the problem that the algorithm is not accurate in spare dataset,we improve it by prefilling the vacancy of rating matrix.Experiment results show that this algorithm improves significantly the accuracy of the recommendation after prefilling the rating matrix.