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The transport of material through the atmosphere is an issue with wide ranging implications for fields as diverse as agriculture, aviation, and human health. Due to the unsteady nature of the atmosphere, predicting how material will be transported via the Earth’s wind field is challenging. Lagrangian diagnostics, such as Lagrangian coherent structures (LCSs), have been used to discover the most significant regions of material collection or dispersion. However, Lagrangian diagnostics can be time-consuming to calculate and often rely on weather forecasts that may not be completely accurate. Recently, Eulerian diagnostics have been developed which can provide indications of LCS and have computational advantages over their Lagrangian counterparts. In this paper, a methodology is developed for estimating local Eulerian diagnostics from wind velocity data measured by a single fixed-wing unmanned aircraft system (UAS) flying in a circular arc. Using a simulation environment, driven by realistic atmospheric velocity data from the North American Mesoscale (NAM) model, it is shown that the Eulerian diagnostic estimates from UAS measurements approximate the true local Eulerian diagnostics and also predict the passage of LCSs. This methodology requires only a single flying UAS, making it easier and more affordable to implement in the field than existing alternatives, such as multiple UASs and Dopler LiDAR measurements. Our method is general enough to be applied to calculate the gradient of any scalar field.
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This content will become publicly available on May 27, 2026
Coherent Structures in Active Flows on Dynamic Surfaces
Abstract Coherent structures—flow features that organize material transport and deformation—are central to analyzing complex flows in fluids, plasmas, and active matter. Yet, identifying such structures on dynamic surfaces remains an open challenge, limiting their application to many living and synthetic systems. Here, we introduce a geometric framework to extract Lagrangian and Eulerian coherent structures from velocity data on arbitrarily shaped, time-evolving surfaces. Our method operates directly on triangulated meshes, avoiding global parametrizations while preserving objectivity and robustness to noise. Applying this framework to active nematic vesicles, collectively migrating epithelial spheroids, and beating zebrafish hearts, we uncover hidden transport barriers and Lagrangian deformation patterns—such as dynamic attractors, repellers, isotropic and anisotropic strain—missed by conventional Eulerian analyses. This approach offers a new perspective on soft and living matter, revealing how geometry and activity can be harnessed to program synthetic materials, and how Lagrangian strain and principal deformation directions can help uncover mechanosensitive processes and directional cues in morphogenesis.
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- PAR ID:
- 10650548
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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