基于三维图像重构的水泥水化过程分布式智能模拟与优化

Characterization of cement particles is complicated due to their wide size ranges, complex shapes, and multi-phase nature from micro-structural viewpoint. Accurate characterization should allow for better prediction of cement performance and more realistic modeling of cement micro structural development. Neglecting the chemical details, This project was starting from measured particle-size distribution, combining the method of intelligent information processing, based on scanning electron microscopy and digital image processing, presents a technique of analyzing 2-D SEM/X-ray images, and characterization of 2-D cement images. By obtaining 2-D characteristic images and combining SEM backscattered electron and X-ray images, algorithm is executed to segment cement particles into silicates and aluminates, the appropriate volume fractions of these two phases exist in the generated 3-D image. To achieve this, a set of improved wavelet and ANN-based algorithm was adopted and a new Orthogonal Genetic Algorithm was introduced in this project. Based on the cellular-automata theory, a set of reaction rules is then applied to the starting microstructure to model the chemical reactions for all of the major phases during the evolving hydration process. The 3-D model of cement hydration and its network-based optimization method was also studied in the project.

以水泥材料的水化、硬化过程为研究对象，通过扫描电镜获取水化过程的二维图象序列，利用小波神经网络进行特征提取，结合材料理化规则研究微观结构的三维重构方法及水化过程的模型。在此基础上采取基于网络的协同进化算法同多智能体的方法相结合研究微观结构与性能的关系，构造分布式模拟优化环境并使之应用于材料性能评价和新型水泥材料的开发。