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Record W2928619856 · doi:10.11159/enfht19.140

Transient Behaviour of Heat Exchangers Under Inlet Perturbations

2019· article· en· W2928619856 on OpenAlexafffund
Shahram Fotowat, Serena Askar, Amir Fartaj

Bibliographic record

VenueProceedings of the World Congress on Momentum, Heat and Mass Transfer · 2019
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Windsor
KeywordsHeat exchangerTransient (computer programming)InletMechanicsTransient analysisMaterials scienceEnvironmental scienceThermodynamicsPhysicsComputer scienceMechanical engineeringTransient responseEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The aim of this investigation is to characterize the transient performance of different heat exchangers for their design and efficient control. This paper explored the effect of inlet mass flow rate and temperature step variations on the dynamic response of a conventional and a minichannel heat exchanger. The work was also extended to include a comparison between these two heat exchangers when both subjected to sudden changes in the liquid inlet conditions. A comprehensive, well-equipped, and large-scale experimental setup was designed and assembled to examine the thermal performance of different heat exchangers under steady and transient changes. At the early stage of a step change, the influence of changing mass flow and inlet temperature of the hot fluid on the response time is found significant while, this effect diminishes as the system approaches steady state. The hot liquid inlet conditions change of mass flow and temperature have more significant effect on the minichannel liquid outlet temperature when compared to the conventional heat exchanger. The reported results can find an application in the design and selection of a cross flow heat exchanger used in thermal management systems such as in automotive industry where compact heat exchanger is required for size and weight reduction.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.683

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 head0.009
GPT teacher head0.205
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.

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

Citations1
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Momentum, Heat and Mass TransferSame topicHeat Transfer and OptimizationFrench-language works237,207