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Rapidly Varying Flow01:24

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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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A streamline represents the trajectory that is always tangent to the fluid's velocity vector at any given point. The velocity of a fluid particle is always directed along the streamline, ensuring the particle continuously follows the streamline's path. Streamlines are particularly useful for visualizing the overall direction of flow in a fluid system, and they provide an instantaneous representation of the flow's velocity field. In steady flow, where conditions do not change over...
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Design Example: Flow of Oil Through Circular Pipes01:25

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Understanding fluid flow behavior through pipes is critical in fluid mechanics, especially in applications like oil transportation through pipelines. Hagen-Poiseuille's law provides an exact solution derived from the Navier-Stokes equations for steady, incompressible, and laminar flow within a circular pipe. Hagen-Poiseuille's law helps determine the necessary pressure drop across a pipeline section by determining parameters like pipe length, radius, oil viscosity, and the desired volumetric...
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Levels of Use of a GIS01:29

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to...
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Fluid flows are categorized by dimensionality and behavior, with one-dimensional flow being the simplest form, where properties like velocity and pressure change only along a single axis. Water moving through straight pipes exemplifies this flow type, as variations in other directions are minimal. One-dimensional analysis helps simplify understanding such flows, focusing solely on changes along the pipe's length.
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Updated: Nov 27, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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ODT Flow Explorer: Extract, Query, and Visualize Human Mobility.

Zhenlong Li1, Xiao Huang2, Xinyue Ye3

  • 1Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC, USA.

Arxiv
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

We created the ODT (Origin-Destination-Time) Flow Explorer, a web portal analyzing human mobility from geotagged tweets and SafeGraph data. This tool provides crucial insights into spatial interactions, especially vital during the COVID-19 pandemic.

Keywords:
SafeGraphTwitterbig datapopulation movementsocial media

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Area of Science:

  • Geographic Information Science
  • Computational Social Science
  • Data Science

Background:

  • Understanding human mobility is crucial for applications requiring knowledge of spatial interactions.
  • The COVID-19 pandemic underscored the need for real-time, fine-scale human mobility data.
  • Existing methods often lack the capacity for large-scale, dynamic mobility analysis.

Conclusions:

  • The ODT Flow Explorer offers a valuable resource for domains requiring timely, fine-grained human mobility data.
  • The platform's big data infrastructure supports analysis across diverse spatial and temporal scales.
  • This tool has significant potential to benefit urban planning, public health, and disaster response.