Accession Number:

AD1096913

Title:

3-D Multi-Scale Modeling Combined with Machine Learning for a Novel Structural-Prognosis Framework

Descriptive Note:

Technical Report,15 Apr 2015,14 Apr 2018

Corporate Author:

UNIVERSITY OF UTAH SALT LAKE CITY SALT LAKE CITY United States

Personal Author(s):

Report Date:

2018-08-16

Pagination or Media Count:

12.0

Abstract:

The goal of the research is to enhance structural-prognosis capabilities for the USAF by discovering a quantitative model capable of predicting the morphological evolution of three-dimensional 3-Dmicrostructurally small fatigue cracks MSFCs based on local, microstructure-sensitive fields. Three of the most significant hindrances to predicting the MSFC life for an arbitrary material microstructure under arbitrary far-field loading include 1 uncertainty in the rules i.e. quantitative, parametric representations of the crack-driving mechanisms that are used to evolve a 3-D crack at the scale of the microstructure 2missing or incomplete information in the cracks surroundings and applied boundary conditions and 3inadequate representation of cracks as evolving discontinuities and their corresponding fluctuations. During the three-year period of this AFOSR Young Investigator Program YIP award, the PI and her graduate students have made significant research advancements toward improving structural-prognosis tools for the USAF by addressing each of these challenges.

Subject Categories:

  • Mechanics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE