COLLABORATIVE RESEARCH: Analysis and design of material microstructure using stochastic simulation techniques

Johns Hopkins University, Baltimore MD

Investigators

Abstract

0084533 Graham In recent years, tremendous progress has been made in the fabrication of advanced materials, such as composites. As these fabrication techniques have progressed, it has become apparent that models describing behavior of these materials need improvement in order to be properly introduced into engineering design. A major obstacle in improving these models for many materials is the high degree of randomness at the microstructural level. This research plan describes simulation-based methods that address both the improved analysis and the design of random composite microstructures, including advanced fibrous/particulate composites, cellular/porous materials, concrete, or other multi-phase materials. Specifically, the proposed research work will focus on the following three areas: 1) probabilistic characterization of material microstructures, 2) stochastic simulation of material microstructures, 3) sensitivity studies of material behavior - design issues. The sensitivity studies will identify critical sizes and clustering patterns of inclusions, volume fractions, etc. The above analyses serve two extremely important roles in engineering of composite materials: to characterize existing composite materials and to aid in improving and optimizing the design of new applications of composite materials. Because the proposed techniques are all based on finite element methods, they could potentially be applied to any phenomena that have been successfully modeled using standard (deterministic) finite element analyses, including critical stress, strain, or displacement response of mechanical/structural systems, general thermoelastic behavior, material or geometric nonlinearities, or nonlinear loadings. The study is a collaborative effort between Princeton University and The Johns Hopkins University supported under NSF initiative 00-26, Exploratory Research on Model-Based Simulation. ***