National Laboratory for High Performance Computing

Polypropylene (PP) film capacitors have excellent dielectric properties at room temperature and are widely used in electric power systems. However, the deterioration of the mechanical embrittlement at low temperatures and the electrical insulating degradation at high elevated temperatures limit their high reliability under extreme temperature conditions. In this article, flexible ethylene chain segments were introduced by blending copolymerized polypropylene (PP-B) with homopolymerized propylene (HPP). Lowering its glass transition temperature causes the film to change from brittle to flexible at low temperatures, thus improving its mechanical toughness at low temperatures. Experimental results showed that the glass transition temperatures of HPP and 10 wt % HPP/PP-B were 18.5 and 15.61 °C, respectively, and the elongation at break of the 10 wt % HPP/PP-B film was 45% higher than that of the HPP film. Further, the β-phase nucleating agent was added to the HPP/PP-B composite film, which can induce the β-crystalline phase and refine the grain size. The deep traps at the interface between the amorphous and crystalline regions can effectively inhibit the long-range transport of the carriers. The leakage current density of (PP-WB2) at high temperature was significantly reduced by adding 0.03 wt % of the β-phase nucleating agent, and the discharge energy density of (PP-WB2) film at 85 °C was as high as 4.01 J/cm3, which is 69% higher than that of HPP. This study is instructive for improving the energy storage performance of PP capacitor films under severe operating conditions.

Geometric, magnetic and electronic properties of folded graphene nanoribbons (GNRs) are investigated by first-principles calculations.

The rapidly growing field of nature-inspired computing concerns the development and application of algorithms and methods based on biological or physical principles. This approach is particularly compelling for practitioners in high-performance computing, as natural algorithms are often inherently parallel in nature (for example, they may be based on a swarm-like model that uses a population of agents to optimize a function). Coupled with rising interest in nature-based algorithms is the growth in heterogenous computing; systems that use more than one kind of processor. We are therefore interested in the performance characteristics of nature-inspired algorithms on a number of different platforms. To this end, we present a new OpenCL-based implementation of the Ant Colony Optimization algorithm, and use it as the basis of extensive experimental tests. We benchmark the algorithm against existing implementations, on a wide variety of hardware platforms, and offer extensive analysis. This work provides rigorous foundations for future investigations of Ant Colony Optimization on high-performance platforms.