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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF VERMONT & STATE AGRICULTURAL COLLEGE
Doing Business As Name:University of Vermont & State Agricultural College
PD/PI:
  • Joshua Bongard
  • (802) 656-4665
  • jbongard@uvm.edu
Award Date:07/28/2021
Estimated Total Award Amount: $ 401,103
Funds Obligated to Date: $ 401,103
  • FY 2021=$401,103
Start Date:09/01/2021
End Date:08/31/2025
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:DMREF/Collaborative Research: Design and Optimization of Granular Metamaterials using Artificial Evolution
Federal Award ID Number:2118810
DUNS ID:066811191
Parent DUNS ID:066811191
Program:DMREF
Program Officer:
  • Thomas F. Kuech
  • (703) 292-2218
  • tkuech@nsf.gov

Awardee Location

Street:85 South Prospect Street
City:Burlington
State:VT
ZIP:05405-0160
County:Burlington
Country:US
Awardee Cong. District:00

Primary Place of Performance

Organization Name:University of Vermont & State Agricultural College
Street:E428 Innovation Hall, University
City:Burlington
State:VT
ZIP:05405-1712
County:Burlington
Country:US
Cong. District:00

Abstract at Time of Award

Metamaterials capable of accessing multiple properties will lead to systems possessing a wide range of functions. Such multifunctional metamaterials will increase the autonomy, efficiency, and lifespan of systems and structures by dynamically adapting to task demands or changes in the environment. Granular metamaterials are an advantageous platform for such dynamic programmability, as the grain properties can be widely tuned to achieve different responses. Granular metamaterial response is dependent on many variables, including the grain arrangement, grain mass, modulus, and shape, friction or other interactions between grains, as well as whether the walls of the container are held fixed or can move in response to forces from the grains. With such a vast number of possible combinations of micro-structural variables, the task of designing the complex relationship between micro-structure and bulk properties is daunting. This Designing Materials to Revolutionize and Engineer our Future (DMREF) research applies evolutionary algorithms to efficiently search the immense parameter space of granular metamaterial designs for specific material properties, as well as identifying how a design can be perturbed to actively transition from one set of desired bulk properties to another. The project will establish a new artificial intelligence-driven approach to the design and optimization of granular metamaterials with adaptable properties. These materials will aid US productivity and prosperity by providing additional means to find and use materials impacting robotics and other technical areas. Future granular metamaterials will exhibit increased dynamic plasticity, enabling responses to different environmental inputs or task demands by reconfiguring their physical structure. This project addresses two issues: (1) How can granular assemblies with specific desired material properties be automatically designed? (i.e., What should the grains' arrangement, moduli, shapes, masses, friction coefficients, and other grain-scale properties be to yield a given bulk material property?); and (2) How can a series of granular assemblies that allow continuous, time-ordered changes in material properties be automatically designed? (i.e., Which set of grain-scale variables should change and by how much? If multiple solutions are found, which ones can be realized in experiments most easily?) To address these issues, a closed-loop procedure will be developed and implemented wherein physics-based discrete element method (DEM) simulations, evolution-based optimization, and physical realizations are combined to produce granular metamaterials with desired, optimal, and adaptable material properties. New knowledge and tools that will be generated by this project include: (i) alternative evolutionary algorithms for designing granular metamaterials capable of changing their own configuration and properties; (ii) physics-based DEM simulations that can predict the properties of granular materials with active grains; and (iii) New synthesis strategies for multifunctional grains and grain assemblies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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