Jianzhan Long

Developing Cemented Carbides Through ICME

The ICME (Integrated Computational Materials Engineering) for cemented carbides aims to combine key experiments with multi-scale simulations from nano (10−10~10−8 m) to micro (10−8~10−4 m) to meso (10−4~10−2 m) and to macro (10−2~10 m) during the whole R&D process of cemented carbides. Based on ICME, the framework for R&D of cemented carbides, involving end-user demand, product design and industrial application, is established. In this work, a description to our established thermodynamic and thermophysical (diffusion coefficient, interfacial energy, and thermal conductivity and so on) databases is presented, followed by simulation of microstructure evolution during sintering of cemented carbides by means of phase field method. Work is also done to investigate the correlation between microstructure and mechanical properties (crack, stress distribution, and coupled temp-displacement) by using phase field and finite element methods. The proposed ICME for cemented carbides is used to develop a few new cemented carbides (including double layer gradient cemented carbides and γ′-strengthened Co–Ni–Al binder cemented carbides), which have found industry applications.

On the Optimization of a Duplicate Document Detection Algorithm Based on SIMD and Document Statistics

although considerable effort has been devoted to duplicate document detection (DDD) and its applications, there is very limited study on the optimization of its time-consuming functions. An experimental analysis which is conducted on a million Grant Proposal documents from the nsfc.gov.cn shows that even by using the clustering and the sampling methods, the speed of DDD is still quite slow. By analyzing the performance of our system with Intel VTune Performance Analyzer, we find out that the shingle comparison is the most time-consuming part in our system, occupying 58% CPU usage. Based on the analysis of the whole algorithm and the data statistics, we propose and implement an optimized shingle comparison algorithm using Intel SIMD technology. Experiments demonstrate that the proposed optimization technique brings 11.6%-38.5% performance gain with various instruction sets and parameters settings. Further performance gain could be achieved base on the accuracy and speed tradeoff.