Prediction of high temperature metal matrix structural material failure using a massively parallel computer

Large scale high speed design and analysis of next generation aerospace systems is achieved by focusing on the computational integration and synchronization of probabilistic mathematics, structural/material mechanics, and parallel computing. Design costs have been driven upward by mathematical models that require multiple levels of interactive analysis and utilize time consuming convergence criteria that further drive computing and design costs upward. To reduce CPU time and memory limitations, an effective real time parallelization (RTP) of the solution is introduced. Recursive Internal Partitioning (RIP) is used to partition the entire domain into subdomains with one or more subdomains being assigned to each independent processor. The Alpha STAR multifrontal algorithm (AMF) is implemented to assemble, condense, and solve for the unknowns at all finite element nodes. A multi level optimization technique is utilized to speed up the simulation processing time of the diversified field of specialized analysis techniques and mathematical models. These models require hierarchical multiple levels of interactive analysis utilizing time consuming convergence. The generic high speed civil transport (hsct) model is used to demonstrate the large scale computing capability. Results of multicriteria optimization indicate an order of magnitude reduction in computing time. Numerical solutions as well as physical phenomena are discussed and recommendations are provided for future solutions.