Scramjet Propulsive Flowpath Prediction Improvements Using Recent Modeling Upgrades

Abstract

Reynolds Averaged Navier Stokes simulations have been performed to examine modeling upgrades for scramjet flowpath predictions. A flush, non-reacting hydrogen fuel injector flowfield was used as the model problem, and an LES simulation of the problem was used for comparison purposes. Calculations were first performed examining the effect of Schmidt number with a constant Prandtl number. Next the effect of the compressibility correction used was examined. These findings indicated that for this injector configuration, the effect of the compressibility had a major impact on the solution, and that the average Schmidt number of about 0.45 compared closely to the LES simulation results. Next, a new scalar fluctuation model was used to obtain local values of Prandtl and Schmidt number whose values were found to vary significantly across the fuel jet mixing layer. The turbulent Prandtl number was found to vary between 0.4 to 0.9, and the turbulent Schmidt number varied from 0.6 to 1.2. Finally, a comparison was performed using an unstructured flow solver with grid adaptation. This technique is now being used to obtain grid resolved solutions in a systematic and straightforward manner in our design studies.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA465776

Entities

People

  • C. Kannepalli
  • James D. Ott
  • K. Brinckman
  • S. M. Dash

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Boltzmann Equation
  • Boundary Layer
  • Chemistry
  • Combustion
  • Computational Fluid Dynamics
  • Diffusion
  • Diffusivity
  • Equations
  • Fuel Injection
  • Geometry
  • Kinetic Energy
  • Payload
  • Prandtl Number
  • Supersonic Combustion Ramjet Engines
  • Three Dimensional
  • Turbulence
  • Turbulent Mixing

Fields of Study

  • Physics

Readers

  • Combustion and Flow Dynamics.
  • Computational Fluid Dynamics (CFD)
  • Image Processing and Computer Vision.