Characterization and Modeling of Synthetic Jet Flow Fields

Abstract

The purpose of this research was to develop a neural network-based, lumped deterministic source term (LDST) approximation module for modeling synthetic jets in large-scale CFD calculations. The LDST approximation technique developed by the author and his students was employed. A series of two-dimensional calculations were performed to characterize unsteady single and multiple synthetic jet flow fields. The LDSTs were parameterized for different geometries as well as various flow conditions. These results were then used to train a neural network to approximate the LDSTs over a wide variety of conditions. Results include the successful development of a neural network LDST module for single two-dimensional synthetic jets for different Mach number flows and different jet orifice angles and characterization studies of both single and dual two-dimensional synthetic jets.

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Document Details

Document Type
Technical Report
Publication Date
Apr 19, 2005
Accession Number
ADA433145

Entities

People

  • Claudio Filz
  • Daniel J. Lee
  • Gregory Workman
  • Katherine Grendell
  • Matteo Pes
  • Paul D. Orkwis
  • Terry Daviaux

Organizations

  • University of Cincinnati

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Boundary Layer
  • Computational Fluid Dynamics
  • Flow
  • Flow Fields
  • Fluid Dynamics
  • Geometry
  • Jet Flow
  • Mach Number
  • Neural Networks
  • Phase Shift
  • Physics
  • Pressure Gradients
  • Reynolds Number
  • Steady State
  • Training
  • Two Dimensional

Readers

  • Combustion and Flow Dynamics.
  • Computational Fluid Dynamics (CFD)
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks