Computer Aided Multi-scale Design of SiC-Si3N4 Nanoceramic Composites for High-Temperature Structural Applications

Vikas Tomer ; John Renaud

关键词: BUILDING MATERIALS;    CARBIDES;    CERAMICS;    COMPUTERS;    CREEP;    DEFORMATION;    DESIGN;    FINITE ELEMENT METHOD;    FRACTURE PROPERTIES;    FRACTURES;    GRAIN BOUNDARIES;    MICROSTRUCTURE;    MORPHOLOGY;    NITRIDES;    OPTIMIZATION;    POWER GENERATION;    POWER PLANT;   

DOI  :  10.2172/1007217
RP-ID  :  None
PID  :  OSTI ID: 1007217
Others  :  TRN: US201106%%881

学科分类：材料科学（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

It is estimated that by using better and improved high temperature structural materials, the power generation efficiency of the power plants can be increased by 15% resulting in significant cost savings. One such promising material system for future high-temperature structural applications in power plants is Silicon Carbide-Silicon Nitride (SiC-Si{sub 3}N{sub 4}) nanoceramic matrix composites. The described research work focuses on multiscale simulation-based design of these SiC-Si{sub 3}N{sub 4} nanoceramic matrix composites. There were two primary objectives of the research: (1) Development of a multiscale simulation tool and corresponding multiscale analyses of the high-temperature creep and fracture resistance properties of the SiC-Si{sub 3}N{sub 4} nanocomposites at nano-, meso- and continuum length- and timescales; and (2) Development of a simulation-based robust design optimization methodology for application to the multiscale simulations to predict the range of the most suitable phase morphologies for the desired high-temperature properties of the SiC-Si{sub 3}N{sub 4} nanocomposites. The multiscale simulation tool is based on a combination of molecular dynamics (MD), cohesive finite element method (CFEM), and continuum level modeling for characterizing time-dependent material deformation behavior. The material simulation tool is incorporated in a variable fidelity model management based design optimization framework. Material modeling includes development of an experimental verification framework. Using material models based on multiscaling, it was found using molecular simulations that clustering of the SiC particles near Si{sub 3}N{sub 4} grain boundaries leads to significant nanocomposite strengthening and significant rise in fracture resistance. It was found that a control of grain boundary thicknesses by dispersing non-stoichiometric carbide or nitride phases can lead to reduction in strength however significant rise in fracture strength. The temperature dependent strength and microstructural stability was also significantly depended upon the dispersion of new phases at grain boundaries. The material design framework incorporates high temperature creep and mechanical strength data in order to develop a collaborative multiscale framework of morphology optimization. The work also incorporates a computer aided material design dataset development procedure where a systematic dataset on material properties and morphology correlation could be obtained depending upon a material processing scientist's requirements. Two different aspects covered under this requirement are: (1) performing morphology related analyses at the nanoscale and at the microscale to develop a multiscale material design and analyses capability; (2) linking material behavior analyses with the developed design tool to form a set of material design problems that illustrate the range of material design dataset development that could be performed. Overall, a software based methodology to design microstructure of particle based ceramic nanocomposites has been developed. This methodology has been shown to predict changes in phase morphologies required for achieving optimal balance of conflicting properties such as minimal creep strain rate and high fracture strength at high temperatures. The methodology incorporates complex material models including atomistic approaches. The methodology will be useful to design materials for high temperature applications including those of interest to DoE while significantly reducing cost of expensive experiments.