Optical Fuel-Air Sensor

Abstract

LIBS, or laser-induced breakdown spectroscopy, is a widely used diagnostic enabling stand-off detection of atomic components of parent constituents; it involves the use of a pulsed laser that induces dielectric breakdown of the target media/sample and collection of the emitted radiation from the laser spark. Typically, LIBS is employed to analyze solid samples. The focus of this project is advancement of LIBS for analysis of gaseous media for the specific purpose of Fuel-Air (F/A) sensing, to derive the local F/A ratio within a high-speed combustor. For this program, our goals are to 1) improve the accuracy of the measurement technique (i.e., of the F/A ratio) and 2) improve the usability of the technique. To improve accuracy (and satisfy Goal 1), we are focusing on the use of Machine Learning to analyze the emission spectra. For Goal 2 we are exploring various improvements to the technique that include miniaturization of the needed equipment e.g., the laser and approaches to shutter the laser pulse to reduce the pulse energy for dielectric breakdown and thus the likelihood of window damage.

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Document Details

Document Type
Technical Report
Publication Date
Sep 20, 2020
Accession Number
AD1208435

Entities

People

  • Carter D. Campbell

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Air Force
  • Air Force Facilities
  • Air Force Research Laboratories
  • Bibliographies
  • Combustion Chambers
  • Combustors
  • Contracts
  • Detection
  • Emission Spectra
  • Fuel Air Ratio
  • Government Procurement
  • Governments
  • Information Exchange
  • Laser Pulses
  • Laser-Induced Breakdown Spectroscopy
  • Lasers
  • Learning
  • Machine Learning
  • Measurement
  • Pulsed Lasers
  • Spectra
  • Spectroscopy
  • Standards

Fields of Study

  • Physics

Readers

  • Combustion science or combustion engineering.
  • Optical Physics and Photonics.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • Directed Energy