Material model for modeling clay at high strain rates

This paper was accepted for publication in the journal International Journal of Impact Engineering and the definitive published version is available at http://dx.doi.org/10.1016/j.ijimpeng.2015.11.005Modeling clay is a soft malleable material made from oils and waxes. This material is fundamental for ballistic evaluation of body armors because it is used as backing material in ballistic tests. After a ballistic impact, a back-face indentation is measured to assess performance of the armor. Due to the important role of modeling clay in this particular application, its mechanical characterization and comprehension of penetration mechanics are essential for development of new personal protection systems. This paper presents a two-step computational methodology to calibrate parameters of a Cowper-Symonds material model for modeling clay at characteristic strain rates up to 1.8×104 s-1. In the first stage, a high-speed camera is used to record the penetration of a gas-gun launched cylindrical mass with a hemispherical cap into a block of clay. Image-processing software is used to capture the tail of the projectile as it penetrates into the clay. These data are then used to sample the penetration depth as function of time. In the second stage, an in-house developed model of penetration, based on both the spherical cavity expansion theory and the Tate penetration equation, is used to determine, by inverse analysis, the parameters of the Cowper-Symonds clay model. The proposed constitutive relationship for clay and the determined material parameters can be applied accurately to problems involving high strain rates

A fly-locust based neuronal control system applied to an unmanned aerial vehicle: the invertebrate neuronal principles for course stabilization, altitude control and collision avoidance

The most versatile and robust flying machines are still those produced by nature through evolution. The solutions to the 6 DOF control problem faced by these machines are implemented in extremely small neuronal structures comprising thousands of neurons. Hence, the biological principles of flight control are not only very effective but also efficient in terms of their implementation. An important question is to what extent these principles can be generalized to man-made flying platforms. Here, this question is investigated in relation to the computational and behavioral principles of the opto-motor system of the fly and locust. The aim is to provide a control infrastructure based only on biologically plausible and realistic neuronal models of the insect opto-motor system. It is shown that relying solely on vision, biologically constrained neuronal models of the fly visual system suffice for course stabilization and altitude control of a blimp-based UAV. Moreover, the system is augmented with a collision avoidance model based on the Lobula Giant Movement Detector neuron of the Locust. It is shown that the biologically constrained course stabilization model is highly robust and that the combined model is able to perform autonomous indoor flight

MQXFS1 quadrupole design report

This report presents the reference design of MQXFS1, the first 1.5 m prototype of the low-beta quadrupoles (MQXF) for the LHC High Luminosity Upgrade. The MQXF quadrupoles have 150 mm aperture, coil peak field of about 12 T, and use Nb3Sn conductor. The design is based on the LARP HQ quadrupoles, which had 120 mm aperture. MQXFS1 has 1st generation cable cross-section and magnetic desig