Methodology for Making a Self-Absorption Correction to the Simple Copper-Line-Ratio Technique Used for Arc-Tunnel Enthalpy Measurements.

Abstract

Detailed theory and analytical procedures are presented which provide a method for self-absorption correction of the simple 5106/5153 angstrom copper-line-ratio technique used to measure arc-tunnel stagnation enthalpy. This correction procedure involves simultaneous measurement of the 5106 angstrom line full half-width in order to infer copper number density and gas temperatue from the self-absorption broadened line profile shape, and the simple one intensity ratio. Theoretical formulations of other dominant line-broadening contributions (i.e., Doppler, Van der Waals, and Stark effects) are also included in order to accurately determine the degree of line broadening that results from self-absorption alone. Typical data reduction curves are presented to demonstrate the correction process. These curves indicate that large errors in measured enthalpy could result if self-absorption was not properly accounted for in the data analysis procedure. An error estimate is also included to indicate the typical accuracy presently obtainable from this self-absorption correction method. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1979
Accession Number
ADA078686

Entities

People

  • Anthony A. Boiarski

Organizations

  • University of Dayton

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Absorption Coefficients
  • Atoms
  • Collision Broadening
  • Computational Science
  • Computer Programming
  • Computer Programs
  • Computers
  • Doppler Effect
  • Energy Levels
  • Line Spectra
  • Magnetic Fields
  • Measurement
  • Measuring Instruments
  • Scattering
  • Spectral Lines
  • Stark Effect
  • Uncertainty Principle

Fields of Study

  • Physics

Readers

  • Spectroscopy.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference