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Record W4283325505 · doi:10.1364/ao.464425

Effective all-fiber pump recycler for kilowatt fiber lasers

2022· article· en· W4283325505 on OpenAlexafffund
Weixuan Lin, Maxime Desjardins-Carrière, Victor Lambin Iezzi, André Vincelette, Marie-Hélène Bussières-Hersir, Martin Rochette

Bibliographic record

VenueApplied Optics · 2022
Typearticle
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceFiber laserOpticsLaserDouble-clad fiberOptoelectronicsDispersion-shifted fiberFiberFiber optic sensorComposite materialPhysics

Abstract

fetched live from OpenAlex

We report an effective pump recycler for industrial kilowatt fiber lasers. The pump recycler is a (6+1)×1 tapered fiber bundle, with signal ports of Ge-doped fiber (GDF) with core/cladding diameters of 20/400 µm and pump fiber ports (PFPs) with core/cladding diameters of 135/155 µm. By splicing PFPs in pairs, 77.9% of the residual pump light reaching the pump recycler is sent back to the cladding of the GDF. The insertion of a pump recycler increases the power conversion efficiency (PCE) of a fiber laser using an Yb-doped fiber (YDF) from 61.0% to 70.5%, with a maximum output power of 2.78 kW. The laser with a 20 m long YDF and pump recycler compares well to another laser using a 40 m long YDF without pump recycler. In both cases, the PCE is comparable but the laser with a 20 m long YDF and pump recycler benefits from reduced stimulated Raman scattering (SRS), thus enabling an 80% increase in Raman threshold. By giving access to short YDF length, the tapered fiber bundle represents an effective pump recycler since it enables reducing SRS while keeping a large PCE.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.874
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations4
Published2022
Admission routes2
Has abstractyes

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