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Record W2018420867 · doi:10.1109/jqe.2009.2028031

Experiment and Numerical Modeling of High-Power Passively Q-Switched Ytterbium-Doped Double-Clad Fiber Lasers

2009· article· en· W2018420867 on OpenAlexaff
Lei Pan, Ilya Utkin, R. Fedosejevs

Bibliographic record

VenueIEEE Journal of Quantum Electronics · 2009
Typearticle
Languageen
FieldEngineering
TopicSolid State Laser Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsYtterbiumFiber laserMaterials scienceLaserDouble-clad fiberOpticsDopingOptoelectronicsPower (physics)Optical fiberPolarization-maintaining optical fiberPhysicsFiber optic sensor

Abstract

fetched live from OpenAlex

Two high-power and high-repetition-rate passively Q-switched Yb-doped double-clad fiber lasers have been demonstrated with small (5.4 ¿m) and medium (10 ¿m) core diameters using Cr4+:YAG as an external saturable absorber. At a pump power of 13.8 W, 7.8 W single-mode outputs have been obtained with a pulse repetition rate of 120 kHz, pulse energy of 65 ¿J , and pulse duration of 116 ns. A theoretical model is developed to predict the laser spectrum and numerically simulate the output characteristics versus pump power. The focused beam in Cr4+:YAG is assumed to have a Gaussian profile and is analyzed using traveling wave rate equations. The effect of amplified spontaneous emission is also investigated in the simulation, which shows reasonable agreement with experimental observations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.248
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2009
Admission routes1
Has abstractyes

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