GOALI: Novel 3D Experiments and Simulations Combined with Bayesian Optimization for Accelerated Design of Metallic Foams

Project Personnel

Ashley Spear

Principal Investigator

University of Utah

Email

Preston Thomas Fletcher

University of Utah

Email

Valerio Pascucci

University of Utah

Michael Czabaj

University of Utah

Email

Bryan Leyda

University of Utah

Email

Funding Divisions

Civil, Mechanical and Manufacturing Innovation (CMMI)

Open-cell metallic foams are an exciting class of structural materials that comprise a network of interconnected metallic ligaments, resulting in an interesting foam architecture. These low-density materials have garnered much attention over the past two decades based on their recognized potential for use in multi-functional applications. For example, in addition to serving as light-weight, load-bearing structures, open-cell metallic foams have the potential to serve concurrently as electrodes for energy-storage devices, as hosts for newly generated bone and blood vessels in biomedical implants, or as impact absorbers and noise insulators for advanced high-speed ground transportation. Despite their potential, the widespread deployment of open-cell metallic foams for a broader range of multi-functional applications remains hampered by inefficient, trial-and-error manufacturing approaches. This Designing Materials to Revolutionize and Engineer our Future (DMREF) Grant Opportunities for Academic Liaison with Industry (GOALI) award supports a joint academic-industry research effort to enable more efficient and intelligent design of open-cell metallic foams, and to achieve precise control over their performance for targeted applications. The results will provide dramatic improvements for the industry by increasing both the manufacturing efficiency and the tailorability of the foams, which will help to expand deployment of the foams throughout the energy, defense, biomedical, aerospace, and automotive industries. The research team will host outreach activities to expose students in K-12, undergraduate, and graduate school to this multi-disciplinary STEM research.

Publications

Vector Field Decompositions Using Multiscale Poisson Kernel
H. Bhatia, R. M. Kirby, V. Pascucci, P. T. Bremer
Towards Replacing Physical Testing of Granular Materials with a Topology-based Model
A. Venkat, A. Gyulassy, G. Kosiba, A. Maiti, H. Reinstein, R. Gee, P. T. Bremer, V. Pascucci
AMM: Adaptive Multilinear Meshes
H. Bhatia, D. Hoang, N. Morrical, V. Pascucci, P. T. Bremer, P. Lindstrom
Ray Tracing Structured AMR Data Using ExaBricks
I. Wald, S. Zellmann, W. Usher, N. Morrical, U. Lang, V. Pascucci
A Study of the Trade-off Between Reducing Precision and Reducing Resolution for Data Analysis and Visualization
D. Hoang, P. Klacansky, H. Bhatia, P. T. Bremer, P. Lindstrom, V. Pascucci
Improving the Usability of Virtual Reality Neuron Tracing with Topological Elements
T. McDonald, W. Usher, N. Morrical, A. Gyulassy, S. Petruzza, F. Federer, A. Angelucci, V. Pascucci
Efficient and Flexible Hierarchical Data Layouts for a Unified Encoding of Scalar Field Precision and Resolution
D. Hoang, B. Summa, H. Bhatia, P. Lindstrom, P. Klacansky, W. Usher, P. T. Bremer, V. Pascucci
High-throughput Feature Extraction for Measuring Attributes of Deforming Open-cell Foams
S. Petruzza, A. Gyulassy, S. Leventhal, J. J. Baglino, M. Czabaj, A. D. Spear, V. Pascucci
Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior
A. D. Spear, S. R. Kalidindi, B. Meredig, A. Kontsos, J. B. le Graverend