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Record W2545763264 · doi:10.1088/1009-0630/18/11/01

Magnetic Inertial Confinement Fusion (MICF)

2016· article· en· W2545763264 on OpenAlexaff
Miao Feng, Xianjun Zheng, Deng Baiquan, Wei Liu, Wei Ou, Yi Huang

Bibliographic record

VenuePlasma Science and Technology · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsHOPE Innovations (Canada)
FundersSouthwest University for Nationalities
KeywordsFusionInertial confinement fusionPhysicsPlasmaPinchNuclear fusionFusion powerComputational physicsOpticsClassical mechanicsMechanicsAtomic physicsNuclear physics

Abstract

fetched live from OpenAlex

Based on the similarity in models of the early Sun and the 3-D common focal region of the micro-pinch in X-pinch experiments, a novel hybrid fusion configuration by continuous focusing of multiple Z-pinched plasma beams on spatially symmetric plasma is proposed. By replacing gravity with Lorentz force with subsequent centripetal spherical pinch, the beam-target fusion reactivity is enhanced in a quasi-spherical converging region, thus achieving MICF. An assessment, presented here, suggests that a practical fusion power source could be achieved using deuterium alone. Plasma instabilities can be suppressed by fast rotation resulting from an asymmetric tangential torsion in the spherical focal region of this configuration. Mathematical equivalence with the Sun allows the development of appropriate equations for the focal region of MICF, which are solved numerically to provide density, temperature and pressure distributions that produce net fusion energy output. An analysis of MICF physics and a preliminary experimental demonstration of a single beam are also carried out.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.006
GPT teacher head0.224
Teacher spread0.218 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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