Observations on Twinning in Zone-refined Tungsten

Observations on twinning in zone-refined tungste

Powder metallurgy approaches to high temperature components for gas turbine engines

Research is reported for the tensile strength, ductility, and heat performance characterisitics of powder metallurgy (p/m) superalloys. Oxide dispersion strengthened alloys were also evaluated for their strength during thermal processing. The mechanical attributes evident in both p/m supperalloys and dispersion strengthened alloys are discussed in terms of research into their possible combination

High gas velocity burner tests on silicon carbide and silicon nitride at 1200 C

Specimens of silicon carbide and silicon nitride were exposed to a Mach one gas velocity burner simulating a turbine engine environment. Cyclic tests up to 100 hour duration were conducted at specimen temperatures of 1200 C. A specimen geometry was used that develops thermal stresses during thermal cycling in a manner similar to blades and vanes of a gas turbine engine. Materials were compared on a basis of weight change, dimensional reductions, metallography, fluorescent penetrant inspection, X-ray diffraction analyses, failure mode, and general appearance. One hot pressed SiC, one reaction sintered SiC, and three hot pressed Si3N4 specimens survived the program goal of 100 one-hour cycle exposures. Of the materials that failed to meet the program goal, thermal fatigue was identified as the exclusive failure mode

High temperature mechanical properties of polycrystalline hafnium carbide and hafnium carbide containing 13-volume-percent hafnium diboride

High temperature mechanical properties of polycrystalline hafnium carbide containing 13- volume-percent hafnium diborid

Using Evolutionary Strategies for the Real-Time Learning of Controllers for Autonomous Agents in Xpilot-AI

Real-time learning is the process of an artificial intelligence agent learning behavior(s) at the same pace as it operates in the real world. Video games tend to be an excellent locale for testing real-time learning agents, as the action happens at real speeds with a good visual feedback mechanism, coupled with the possibility of comparing human performance to that of the agent\u27s. In addition, players want to be competing against a consistently challenging opponent. This paper is a discussion of a controller for an agent in the space combat game Xpilot and the evolution of said controller using two different methods. The controller is a multilayer neural network, which controls all facets of the agent\u27s behavior that are not created in the initial set-up. The neural network is evolved using 1-to-1 evolutionary strategies in one method and genetic algorithms in the other method. Using three independent trials per methodology, it was shown that evolutionary strategies learned faster, while genetic algorithms learned more consistently, leading to the idea that genetic algorithms may be superior when there is ample time before use, but evolutionary strategies are better when pressed for learning time as in real-time learning