Collaborative Research: Seeing and hearing frictional interfaces: using total internal reflection imaging and acoustic methods to explore contact dynamics
- Institution
- Columbia University, NY
- Principal investigator
- Christine Mccarthy
- Funding
- $542K
- Duration
- 2025–2028
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Abstract
Both earthquakes and ice sheet collapse pose enormous hazards with severe societal consequences. Both systems are partly controlled by friction. Microscopic contacts at rock interfaces of the fault or the base of the ice sheet controls the friction in these systems. The details of how these surfaces evolve as rocks fail hold information to better understand future events and to assess hazards. It is impossible to observe these interfaces in nature. The most informative measurements come from sensors placed at the surface. The goal of this proposal is to instead observe an experimental system to determine how interfaces slip and evolve. The experiments listen to ice slip experiments with sensors to study how the faults evolve, and how changes may affect seismic hazards. The project will support a graduate student and a collaboration across three institutions. The goal of the study is to understand how the evolution of contacts during cycles of shear, slip, and stability control large scale behaviors in both faults and glacial systems. This will be accomplished by directly observing the coupled processes that control nucleation, slow and fast slip, healing, drag, and melting in ice to understand the fundamental mechanisms that drive the evolution of conditionally unstable frictional interfaces. A series of both static and sliding experiments will be performed using a custom cryogenic biaxial apparatus and a sample of ice atop a glass plate. Ice will be used for two key reasons: 1) it is transparent, allowing light and images to be transmitted through it; and 2) it has a low melting temperature, such that exploring a modest range of temperature covers a broad swath of homologous temperature, T/Tm, and thus both brittle and ductile behavior. This knowledge, analyzed by cutting-edge machine learning data analysis methods and extrapolated up to larger systems, will improve understanding of the mechanics of the entire stick-slip cycle and stability. The project will support a graduate student and a collaboration across three institutions. 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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