Model of Laser-Induced Temperature Changes in Solid-State Optical Refrigerators

Abstract

We present an efficient and numerically stable method to calculate time-dependent, laser-induced temperature distributions in solids and provide a detailed description of the computational procedure and its implementation. This study combines the two-dimensional heat equation with laser-induced heat generation and temperature-dependent luminescence. The time-dependent optical response of a system is obtained numerically by the Crank Nicolson method. This general model is applied to the specific case of optical refrigeration in ytterbium (Yb3+) doped fluorozirconate glass (ZBLAN). The laser-induced temperature change upon optical pumping and the respective transient luminescence response are calculated and compared to experimental data. The model successfully predicts the zero-crossing temperature, the net quantum efficiency, and the functional shape of the transients. We find that the laser-cooling transients have a fast and a slow component that are determined by the excited-state lifetime of the luminescent ion and the thermal properties of the bulk, respectively. The tools presented here may find application in the design of a wide range of optical and optoelectronic devices.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2010
Accession Number
ADA560027

Entities

People

  • M. P. Hehlen
  • M. Sheik-bahae
  • R. I. Epstein
  • W. M. Patterson

Organizations

  • University of New Mexico

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Absorption Cross Sections
  • Climate Change
  • Coefficients
  • Differential Equations
  • Equations
  • Experimental Data
  • Fluoride Glass
  • Heat Capacity
  • Laser Cooling
  • Lasers
  • Luminescence
  • Materials
  • Optical Lattices
  • Quantum Efficiency
  • Specific Heat
  • Thermal Conductivity
  • Two Dimensional

Fields of Study

  • Engineering
  • Physics

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Materials Science and Engineering.
  • Optical Physics and Photonics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Directed Energy
  • Directed Energy - Lasers
  • Microelectronics
  • Quantum Computing