MétaCan
Menu
Back to cohort
Record W4312884652 · doi:10.1115/gt2022-83031

Performance Improvement of Heat Exchangers Used in a Hybrid Electric Aircraft

2022· article· en· W4312884652 on OpenAlexaffabout
Faezeh Rasimarzabadi, Alexander Crain, Pervez Canteenwalla, Patrick Zdunich, Evan Gibney

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsRadiator (engine cooling)CoolantHeat exchangerMechanical engineeringNuclear engineeringFinHeat transferThermal resistanceActive coolingAutomotive engineeringWater coolingMaterials scienceEngineeringMechanicsPhysics

Abstract

fetched live from OpenAlex

Abstract Thermal management system analysis of a kW scale hybrid electric aircraft developed at the National Research Council of Canada (NRC) has been provided with the aim of assessing the cooling capacity and improving the efficiency. For the electric propulsion system of the aircraft, a series cooling loop has been designed that comprises a heat exchanger (radiator) and two pumps. As coolant travels through the motor and controller, it accumulates heat, then the heat is dissipated from the coolant passing through the radiator. From the calculated parameters for different demonstration flight phases of the aircraft, it is concluded that the current radiator design is limited by the air side resistance. This air thermal resistance comprises over 76% of the total thermal resistance in all flight phases. Therefore, a large frontal area to meet cooling needs is required. The potential of different methods for cooling improvement of the radiator were investigated and a new concept was suggested for heat transfer enhancement which is a passive enhancing method by applying different micro-fin roughness on the fins or coolant tubes of a radiator.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.206
Teacher spread0.196 · 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 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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same topicAdvanced Aircraft Design and TechnologiesFrench-language works237,207