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Record W2989693044 · doi:10.18280/ejee.210501

Study of Kinematic System for Solar Tracking of a Linear Fresnel Plant to Reduce End Losses

2019· article· en· W2989693044 on OpenAlexvenueno aff
Mario Antonio Cucumo, Vittorio Ferraro, Dimitrios Kaliakatsos, Francesco Nicoletti

Bibliographic record

VenueEuropean Journal of Electrical Engineering · 2019
Typearticle
Languageen
FieldComputer Science
TopicSolar Radiation and Photovoltaics
Canadian institutionsnot available
Fundersnot available
KeywordsKinematicsTracking (education)Solar trackerEnvironmental scienceGeodesyOpticsPhysicsPhotovoltaic systemGeologyEngineeringClassical mechanicsElectrical engineering

Abstract

fetched live from OpenAlex

Linear Fresnel Plants are composed of flat mirrors that reflect direct radiation towards a fixed receiver tube parallel to the reflectors. In traditional plants, mirrors are able to rotate around a single axis. This type of solar tracking system generates high losses at its ends. In the paper a new configuration is studied in which the mirrors placed at the ends are moved around two axes. The proposed system is thus able to recover the direct radiation which, otherwise, would not reach the receiver tube. The rotation of the mirrors is managed by stepper servomotors. They allow a robust solar tracking, not influenced by external forces. The work presents the kinematic system for moving the field of mirrors actuated with open loop control. Stepper motors generate a motion by steps; in order to execute each step at the exact moment, the logic of movement is properly defined. It is necessary to know in detail the temporal position and the law of motion for each rotation to identify the driving logic of the motors. The behaviour of the "electric motor -mechanical system" is numerically modelled, evaluating the torque and the errors in the motion.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.235
Teacher spread0.214 · 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 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

Citations12
Published2019
Admission routes1
Has abstractyes

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