No abstract

The U.S. maritime transportation system is a foundational element of the national supply chain, and its operators are under immense pressure to ensure that its ports can reliably and efficiently support supply chain demands despite their exposure to potential disruptions. Understanding the connectivity of these ports is critical to describing the robustness of port network regions in the face of disruption. In this work, Marine Cadastre Automatic Identification System data are utilized to describe a network of 62 interconnected ports (“nodes”) within the U.S. maritime transportation system. This network is analyzed with community detection via label propagation to quantitatively identify regions of the U.S. port network based on shared vessel traffic and examined with the PageRank algorithm to identify ports that are critical to regional traffic flow. The detected communities of the U.S. port network were driven by physical proximity in many instances. However, the Mississippi River and Gulf ports were split based on the predominant vessel type of each port. Additionally, ports along the Gulf often received lower PageRank scores than West coast ports of similar size. Quantitatively identifying port network regions and critical ports are valuable capabilities for discussion of regional robustness or investments to increase region-wide resilience.

A laboratory apparatus was constructed to physically realize the Burgers' vortex cartoon of turbulenceplankton interactions. Fluid motion is induced by corotating two disks while simultaneously withdrawing fluid axially through hollow drive shafts. The technique creates a flow pattern that mimics a Burgers' vortex with size and strength consistent with turbulence vortices in the coastal and near-surface zones. Specifically, the radius, circulation, and axial strain rate of the Burgers' vortex were specified to match typical dissipative vortices corresponding to two turbulence intensity levels (described by a mean turbulent dissipation rate of 0.009 cm 2 s 23 and 0.096 cm 2 s 23 , respectively). Tomographic particle image velocimetry was used to quantify the flow field, calibrate the apparatus, verify that it produces the desired vortex characteristics, and provide a three-dimensional velocity vector field to compare with zooplankton behavioral assays. The apparatus facilitates direct examination of the mechanistic aspects of plankton interaction with a turbulent-like vortex, which is demonstrated via quantifying the swimming behavior of Acartia tonsa in and around the vortex flow structure.