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Record W4387928193 · doi:10.1063/5.0189559

Shock formation in flowing plasmas by temporally and spatially smoothed laser beams

2024· article· en· W4387928193 on OpenAlexafffund
Jan Ludwig, S. Hüller, Harvey A. Rose, C. Bruulsema, W. A. Farmer, P. Michel, A. L. Milder, G. F. Swadling, W. Rozmus

Bibliographic record

VenuePhysics of Plasmas · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsUniversity of Alberta
FundersAlliance de recherche numérique du CanadaGrand Équipement National De Calcul IntensifLawrence Livermore National LaboratoryU.S. Department of Energy
KeywordsPhysicsShock (circulatory)PlasmaLaserShock wavePonderomotive forceInertial confinement fusionMechanicsSupersonic speedNonlinear systemClassical mechanicsOpticsNuclear physicsQuantum mechanics

Abstract

fetched live from OpenAlex

The cumulative impact of multiple laser speckles on a supersonic plasma flow across optically smoothed laser beams is investigated. The bending of laser beams caused by ponderomotive laser–plasma coupling, together with flow, leads to plasma a momentum-conserving response that results in a deceleration of the flow. Once the flow velocity decreases to a subsonic level, the action of the laser beams can generate a shock within the plasma. This scenario has been predicted theoretically and confirmed by hydrodynamic simulations. The conditions of shock generation are given in terms of the ponderomotive pressure, speckle size, and the flow velocity. The nonlinear properties of the shocks are analyzed using Rankine–Hugoniot relations. According to linear theory, temporally smoothed beams exhibit a higher threshold for shock generation. Numerical simulations with beams that are smoothed by spectral dispersion compare well with the linear theory results, diverging only in the nonlinear regime. The conditions necessary for shock generation and their effects on the laser–plasma coupling in the inertial confinement fusion experiments are also discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.743

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.239
Teacher spread0.230 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2024
Admission routes2
Has abstractyes

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