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Record W2149023114 · doi:10.1109/modsym.1998.741200

Kick sensitivity analysis for the LHC inflectors

2002· article· en· W2149023114 on OpenAlexaff
Michael Barnes, M. Jheeta, G.D. Wait, L. Ducimetière, G.H. Schroder, E.B. Vossenberg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGyrotron and Vacuum Electronics Research
Canadian institutionsTRIUMF
Fundersnot available
KeywordsLarge Hadron ColliderCapacitorRippleElectromagnetic coilSensitivity (control systems)PhysicsInductanceElectrical engineeringParasitic elementVoltageElectronic circuitMagnetSuperconducting magnetElectronic engineeringOptoelectronicsEngineeringNuclear physics

Abstract

fetched live from OpenAlex

The injection kicker system of CERN's Large Hadron Collider (LHC) will consist of two sets of four kicker magnet systems each producing a magnetic field pulse of 1.3 T.m. with a duration of 6.5 /spl mu/s, a rise time of 900 ns, and flat top ripple of less than /spl plusmn/0.5%. The electrical circuit of the complete system, including all known parasitic quantities, has been simulated with PSpice. Many parasitic elements were determined from Opera2D simulations which included eddy-currents. Equivalent circuits which simulate the frequency dependence of inductance and resistance of the Pulse Forming Network (PFN) have been derived. PSpice has been utilised to carry out a sensitivity analysis of the field to the value of both individual and groups of circuit components. Capacitors for a prototype 5 /spl Omega/ PFN have been purchased and, based on the measured values of these capacitors, the diameter of the PFN coil has been re-optimised. The results of the sensitivity analysis have been used to define component tolerances for a prototype PFN. Low and high voltage measurements have commenced on the prototype PFN, and the results of the sensitivity analysis will be used to determine the source of any excessive ripple. This paper presents the results of both the analyses and measurements.

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 categoriesInsufficient payload (model declined to judge)
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 score0.814
Threshold uncertainty score0.997

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)0.0040.000

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 head0.042
GPT teacher head0.308
Teacher spread0.266 · 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 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

Citations5
Published2002
Admission routes1
Has abstractyes

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