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Record W2142582324 · doi:10.1115/rtd2004-66026

Optimization of Diesel Engine: Synchronous Alternator Group

2004· article· en· W2142582324 on OpenAlexaff
G.F. Girda, Abdemusa Moosajee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsAlternatorAutomotive engineeringDiesel engineLine (geometry)Electrical engineeringRelayPower (physics)Diesel fuelEngineeringPhysics

Abstract

fetched live from OpenAlex

The paper describes the findings of an experiment that is a result of close collaboration among four companies. The paper discusses the experiment on one locomotive by using a microprocessor-based relay, Multiple Function Relay (MFR) SEL-701, for on-line measurement, control and optimization of a HEP (Head End Power) group. The HEP has a Diesel engine, 810 HP/908 HP, 1800 rpm, and a double wound three-phase self excited synchronous alternator, 625 kVA, 575 V. The HEP group is installed on the locomotive and supplies the electrical hotel power to the train’s coaches. The relay SEL-701 (Schweitzer Engineering Laboratories) measures the Diesel engine temperatures on 7 different points and the winding temperatures at 3 internal points, one per each phase. The SEL-701 monitors alternator output currents and voltages and controls one (or both) train lines when the Diesel engine’s hottest temperature equals the maximum admissible temperature. Amongst others the paper highlights the benefits derived by use of on-line measurements of the Diesel engine before and after relocation of a pre-existent engine shutdown temperature probe. In addition, the paper discusses the decrease in the numbers of Diesel engine shut downs due to the modified mode of protection and the increase in available electrical power supplied to the train lines together with the comparison of the HEP group efficiency before and after modification.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.788
Threshold uncertainty score0.270

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)0.0000.000

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 head0.003
GPT teacher head0.170
Teacher spread0.167 · 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 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

Citations0
Published2004
Admission routes1
Has abstractyes

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