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Record W4381619417 · doi:10.11159/ffhmt23.173

Effects of Geometrical Parameters on Thermal-Hydraulic performance of air flowing in Additively Manufacturable Heat Exchanger

2023· article· en· W4381619417 on OpenAlexvenueno aff
Chandra Kishore, Vasudevan Raghavan, G. Venkatarathnam

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2023
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHeat exchangerThermalThermal hydraulicsMaterials scienceMechanicsMechanical engineeringNuclear engineeringEnvironmental sciencePetroleum engineeringThermodynamicsHeat transferEngineeringPhysics

Abstract

fetched live from OpenAlex

An additively manufactured airfoil finned plate-fin heat exchanger (PFHE) is proposed to be applied to a flue gas waste heat recovery system.This study numerically investigates the effect of various geometrical characteristics of the airfoil finned channel on the pressure drop and heat transfer using OpenFOAM with dry air as a working cold fluid.The studied parameters include the offset number ( f ), longitudinal number ( l ), transverse number ( t ), and fin height (h f ) of the airfoil.Additionally, the effect of the fin number on the thermal-hydraulic characteristics of the flow channel is also studied to determine the minimum number of fins where the friction factor and Colburn j-factor variation along the flow length is nearly stabilized.The results of the study show that the 20 number of fins is reasonable for assessing the heat exchanger performance.The offset number has an influence on pressure drop and heat transfer until the offset length approaches the fin length, after which it has no effect.The longitudinal number effect on heat transfer per unit area and pressure drop per unit length is a linear variation, while the transverse number and fin height effect is a non-linear variation.

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 score0.166
Threshold uncertainty score0.591

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 head0.016
GPT teacher head0.212
Teacher spread0.197 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicHeat Transfer and OptimizationFrench-language works237,207