A Stochastic Fractional Modeling Framework for Design of New Functional Materials
Abstract
Multi-functional natural materials are the product of millions of years of evolution, locally optimized for their specific missions. They have unprecedented stiffness, strength and toughness at low density due to their hierarchical, multi-scale structure, leading to a wide spectrum of anomalous behaviors. Better understanding and exploiting Òanomalous materialsÓ opens up a new rich field, which will transform our perspective towards the design of new functional/smart materials with ``extraordinary capabilities." We will develop a Òdesign-drivenÓ computational-mathematical framework, which i) iteratively forms the new admissible (fractional-order) constitutive laws for materials with the desired performance, ii) quantifies/mitigates the associated model uncertainties, iii) carries out the corresponding Òfailure analysisÓ under prescribed risk by developing a new mathematical language for the failure process, and iv) sheds light on the Ònon-GaussianÓ features in the new material micro-structure prior and after failure. This ARO YIP proposal is a cohesive integration of the following four research components, which forms the pillars of the proposed research, RC1: Bi-Level Constitutive Model Construction, RC2: Uncertainty Mitigation, RC3: Failure Analysis, RC4: Inverse Stochastic Homogenization (Down-Scaling). The proposed stochastic framework will lead to the development of new functional materials for specific missions with significant applications of interest to US DoD such as: rheology of aging gels and polymers, new media with spatially variable heterogeneity for controlled wave propagation, nonlocal vibration analysis in the context of heterogeneous media and soft matter subject to extreme events, e.g., blast waves, and life-cycle prediction and failure analysis of materials due to different mechanisms, e.g., dislocation avalanches characterized by stress intermittency, creep, and fatigue. The proposed research will lead to more reliable life prediction of the US army equipment, in addition to improve the conceptual design and capability of flexible robots and multi-functional structures.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 06, 2019
- Source ID
- W911NF1910444
Entities
People
- Mohsen Zayernouri
Organizations
- Army Contracting Command
- Michigan State University
- United States Army