Date of Award
3-9-2009
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Engineering Physics
First Advisor
Steven T. Fiorino, PhD
Abstract
An atmospheric optical turbulence strength model with a broad wavelength range of 355nm (ultraviolet) to 8.6m (radio frequencies) has been created at AFIT and implemented into the High Energy Laser End-to-End Operational Simulation tool (HELEEOS). This modeling and simulation tool is a first principles atmospheric propagation and characterization model. Within HELEEOS lies the High-Resolution Transmission Molecular Absorption (HITRAN) database, containing 1,734,469 spectral lines for 37 different molecules as of version 12.0 (2004). HITRAN affords HELEEOS incredible accuracy for electromagnetic (EM) propagation prediction. A full understanding of optical turbulence is needed to successfully predict EM radiation propagation, particularly within the application of high energy laser weapon systems. Existing models for optical turbulence do not encompass wide ranges of wavelengths, nor do they include anomalous dispersion effects. Both of these additions have been incorporated into AFIT/CDE's new optical turbulence strength model. This thesis's objective is to verify and demonstrate the optical turbulence prediction tool. This tool enables predictions to occur in notoriously difficult regions of the spectrum to measure (i.e. Terahertz). One can measure optical turbulence at one wavelength and accurately determined optical turbulence at a different wavelength based on the relation of the vertical gradient of the index of refraction to optical turbulence.
AFIT Designator
AFIT-GAP-ENP-09-M03
DTIC Accession Number
ADA495921
Recommended Citation
Cohen, J. Jean, "Demonstration and Verification of a Broad Spectrum Anomalous Dispersion Effects Tool for Index of Refraction and Optical Turbulence Calculations" (2009). Theses and Dissertations. 2436.
https://scholar.afit.edu/etd/2436
Included in
Atmospheric Sciences Commons, Atomic, Molecular and Optical Physics Commons, Statistical Models Commons