Turbulence in the Stable Planetary Boundary Layer and Aloft: Modeling and Characterization Using DNS and LES

Abstract

During the last 12 months we have conducted focused analysis of our DNS solutions, explored new gravity-wave breaking simulations (including quantifying the turbulent Prandtl number), evaluated the numerics used in our LES code, conducted new work on optimal perturbation of stratified shear flows, extended verification comparisons between our DNS solutions and recent aircraft measurements (an found excellent agreement), and developed a Bayesian hierarchical methodology for deducing unresolved turbulent motions in mesoscale model simulations of stratified flows. In addition we also helped write a successful DoD HPCMO proposal for challenge status for the computer work associated with this project and a successful DOD Golden Opportunity Capability Applications Projects (CAP) proposal that will provide an additional 1 to 2 million hours of computer time on newly delivered MPP systems before they are made generally available to DoD users. The CAP program benefits the DoD HPCMO by having new systems rigorously tested by experienced users.

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Document Details

Document Type
Technical Report
Publication Date
Nov 15, 2004
Accession Number
ADA432709

Entities

People

  • Joseph Werne

Organizations

  • Northwest Research Associates

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Atmospheric Motion
  • Boundary Layer
  • Computers
  • Equations
  • Flow
  • Gravity Waves
  • High Performance Computing
  • Layers
  • Measurement
  • Prandtl Number
  • Probability
  • Probability Distributions
  • Shear Flow
  • Simulations
  • Temperature Gradients
  • Turbulence

Fields of Study

  • Physics

Readers

  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Computational Fluid Dynamics (CFD)
  • Public Financial Management and Budgeting

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference