Airborne Puff and Plume Datasets for an Urban Landscape

Abstract

This report describes a database of Puffs and Plumes containing four high-resolution CFO datasets and associated files that capture he turbulent, time-dependent downwind evolution of ground-level tracer-gas clouds in a fictitious urban environment. Time histories of clouds from both continuous sources (plumes) and instantaneous sources (puffs) are presented. The database also contains files defining the urban geometry, a mask to overlay a 2D visualization of the geometry for display, and Fortran utilities to read and test the our compressed datasets. These datasets contain high-frequency time sequences of ground-level, neutrally buoyant, gas tracer density fields computed at 5-meter resolution on a uniformly spaced 1200x800 computational grid. These density fields, for both the puff and plume sources, originate from six separate locations in the 6-km by 4-km urban domain. These datasets are structured to allow rapid analysis of turbulent fluctuations in the concentration of evolving puff and plume clouds and to study the naturally occurring variability expected between distinct realizations of instantaneous releases in fluctuating wind fields.

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Document Details

Document Type
Technical Report
Publication Date
Apr 18, 2019
Accession Number
AD1075389

Entities

People

  • Jay Boris
  • Keith Obenschain
  • M Y Obenschain

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Biomedical
  • Counter WMD
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Boundary Layer
  • Buoyancy
  • Civil Defense
  • Computational Fluid Dynamics
  • Computer Programs
  • Computers
  • Data Sets
  • Databases
  • Fluid Dynamics
  • Fluid Flow
  • Fluid Mechanics
  • High Performance Computing
  • High Resolution
  • Large Eddy Simulation
  • Three Dimensional
  • Turbulence
  • Urban Areas

Fields of Study

  • Environmental science

Readers

  • Aerosol Science/Aerosol Physics
  • Fluid Mechanics and Fluid Dynamics.
  • Neural Network Machine Learning.

Technology Areas

  • Space