ACORN Project Collaboration with Sandia National Labs

March 18 2021

 Predicted ML light intensity v. strain and strain rate

Dr. Donghyeon Ryu has been collaborating with Dr. Andy Huang at Sandia National Lab (SNL) for an SNL Lab Directed Research and Development (LDRD) Accelerated Collaborative Research Nucleus (ACORN) project, titled “Machine-Learning for Physical Processes with Consideration of Constitutive Equations.”

Two mechanical engineering students, M.S. student Mr. George Hoover and an undergraduate student Mr. David Kunkel have worked on this project for establishing a machine-learning (M-L)-based framework to derive multi-physics constitutive equations for novel functional materials. This includes the development of M-L damage diagnosis and prognosis approaches using a non-contact structural sensing skin. 

One of the functional materials that the team has been studying about is a mechanoluminescent (ML) material, which glows in response to external mechanical stimuli. Using M-L-based framework, the team has successfully built a constitutive equation for the ML material to predict ML light intensity (in mean pixel value) with input strain and strain rate.