CORNERSTONE RESEARCH GROUP INC — Department of Defense STTR Phase II: A20B-T018
CORNERSTONE RESEARCH GROUP INC — STTR Phase II award from Department of Defense.
- Amount
- $1,099,991
- Agency
- Department of Defense · Army
- Program / Phase
- STTR · Phase II
- Topic
- A20B-T018
- Solicitation
- 20.B
- NAICS
- —
- Place of performance
- OH
- Period
- 2022-04-11 → 2024-04-10
Description
Determining physical properties such as density, strength and stiffness of structures made from geomaterials typically requires destructive testing for direct measurements or inference from non-destructive measurements when the composition is unknown. These methods also require physical proximity to the structures being evaluated, a luxury that is not always available or safe. A non-destructive method that accurately determines chemistry, microstructure, and / or physical properties is needed, particularly for structures in which the fabrication history is unknown. Ideally this method would not require close proximity to the structure. Multi-spectral electromagnetic (EM) emissions from structures has the potential of providing a non-destructive method to determine the composition and physical properties of the geomaterial structures. The typical constituents in geomaterials have unique signatures when exposed to a range of EM frequencies. However, incident EM energy on a composite with unknown constituents will scatter the energy such that making direct inference from the emissions is nearly impossible without a-priori knowledge. Determining the composition of a geomaterial with an unknown set of constituents would require as multiple frequencies or wavelengths to aid in decoupling the convoluted signal. Cornerstone Research Group, Mississippi State University, and University of Maine propose to use advanced electromagnetic modeling and data analytics to determine the composition of a geomaterial from its multi-spectral electromagnetic scattering. This capability can allow the compositional analysis to be determined from stand-off remote sensing modalities.