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Record W3001621831 · doi:10.1139/tcsme-2019-0060

Performance analysis of a dual-loop cooling system for engineering vehicles

2020· article· en· W3001621831 on OpenAlexvenueno aff
Chao Yu, Sicheng Qin, Bosen Chai

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsWater coolingLoop (graph theory)Dual loopAir coolingLoop heat pipeActive coolingInner loopInternal combustion engine coolingHeat transferMechanical engineeringControl theory (sociology)Materials scienceNuclear engineeringMechanicsEngineeringComputer scienceHeat pipePhysicsController (irrigation)ChemistryMathematics

Abstract

fetched live from OpenAlex

To improve the efficiency of heat transfer from the cooling system of non-road mobile machinery, a modification has been made to the classic cooling system. Specifically, we divide the classic cooling system into two independent cooling loops, namely, the high-temperature cooling loop and the low-temperature cooling loop. Through simulation and experimentation, the dual-loop cooling system was systematically studied, and it was found that the heat dissipation of the dual-loop cooling system was better than that of the single-loop cooling system. In comparison with the classic cooling system, the volume factor of our system increased by 49.3%, the power factor increased by 24.5%, the effective drag coefficient increased by 5.8%, and the compressed air loop length was shortened by 54.5%. In addition, the dual-loop cooling system can greatly reduce the temperature of pressurized air. The proposed new system can better meet the cooling needs of non-road mobile machinery.

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 score0.840
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.188
Teacher spread0.175 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicRefrigeration and Air Conditioning TechnologiesFrench-language works237,207