GOALI: Random Fiber Structures: Material Characterization & Adaptive Damage Response

Project Details

Description

This project will develop an adaptive, scale invariant material model based on microstructural information that will efficiently account for and predict the material and mechanical behaviors inherent in Random Fiber polymer Composites (RaFC). The proposed framework uniquely incorporates representative volume elements (RVE) and independent state variables (ISV) The successful development and implementation of the proposed model depends on: (i) the fundamental theory of ISV, which provides a carrier and invariant algorithm to bridge the micro phenomena building up to macroscopic behaviors; (ii) a quantitative micro description of the geometry and the structure by a set of generalized RVEs, which compose unified geometric primitives used to construct and characterize RaFCs. These primitives also provide an ideal approach for systematically investigating the relationship between microstructure and damage mechanisms. The major intellectual merit lies on establishing an interdisciplinary program to create and tailor a state-of-the-art computational model that integrates material science and applied mechanics to provide a comprehensive analysis tool for RaFCs. This program will provide the underpinnings for understanding more fiber reinforced material systems such as biomaterials and carbon nanotube composites.

The societal impact lies in that the innovative and comprehensive approach taken by this work creates enabling technologies for better material characterization. Improved material modeling results in creative product design, improved efficiency, increased applicability and decreased environmental burden. Educational impact will be also significant since the students involved will intern at Ford Research Labs. Participation of undergraduate female and minority students will be actively recruited to perform experimental and modeling tasks.

StatusFinished
Effective start/end date8/1/077/31/11

Funding

  • National Science Foundation: $253,399.00

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