Search for: All records

We briefly review the Γ-convergence of phase-field fracture to Griffith fracture, and describe how softening and nucleation occur when implementing phase-field models. An example is given of how this softening and nucleation can be completely stopped, while preserving crack growth and Γ-convergence. We then show how nucleation can locally be turned back on, based on any criterion, such as a stress threshold. Again, these modifications preserve Γ-convergence, and they can be applied to static, quasi-static, and dynamic models. Additionally, we describe why these modifications can be expected to improve the convergence of phase-field models. 

The 2021 airborne Light Detection and Ranging (LiDAR) data was acquired by the University of Alaska Geophysical Institute for an National Science Foundation (NSF) funded project focused on catastrophic arctic lake drainage in northern Alaska. The data was acquired at six key field study sites that included drained lake basins north of Teshekpuk Lake, perched lakes on the Ikpikpuk River Delta, tapped lakes at Drew Point, the large drained lake basin complex at the Pik Dunes, a lake and drained lake basin complex at the Anaktuvuk River tundra fire site, and the cascading lake drainage events along the Chipp and Alaktak Rivers. This dataset encompasses 300 square kilometers of terrestrial and aquatic tundra settings in northern Alaska. The data were acquired between 22 July and 27 July 2021 at an estimated density of 16-20 points per square meter (ppm) using an Reigl VQ-580ii LiDAR system flying at an altitude of 750 meters above ground level. The vertical accuracy of this dataset is 10 centimeters. The data have been post-processed to WGS84 UTM Zone 5 North in Ellipsoid Heights (meters). 

This data set covers the Anaktuvuk River fire site and maps drained lake basins in this area as described by Jones et al (2015). The data set is derived from airborne Light Detection and Ranging (LiDAR) data acquired in 2009 and 2014. The classification of drained lake basins is based on digital terrain models (DTMs) created from the classified LiDAR data and using the a topographic position index (TPI). The TPI output was manually categorized relative to existing surficial geology maps and refined into the following terrain units: (1) drained lake basins, (2) yedoma uplands, (3) rocky uplands, (4) glaciated upland, (5) river floodplain and (6) tundra stream gulches. The drained lake basin class is the subject of this data set publication. Jones, B., Grosse, G., Arp, C. et al. Recent Arctic tundra fire initiates widespread thermokarst development. Sci Rep 5, 15865 (2015). https://doi.org/10.1038/srep15865 

This data set covers the Anaktuvuk River fire site and maps drained lake basins in this area as described by Jones et al (2015). The data set is derived from airborne Light Detection and Ranging (LiDAR) data acquired in 2009 and 2014. The classification of drained lake basins is based on digital terrain models (DTMs) created from the classified LiDAR data and using the a topographic position index (TPI). The TPI output was manually categorized relative to existing surficial geology maps and refined into the following terrain units: (1) drained lake basins, (2) yedoma uplands, (3) rocky uplands, (4) glaciated upland, (5) river floodplain and (6) tundra stream gulches. The drained lake basin class is the subject of this data set publication. Jones, B., Grosse, G., Arp, C. et al. Recent Arctic tundra fire initiates widespread thermokarst development. Sci Rep 5, 15865 (2015). https://doi.org/10.1038/srep15865