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Record W4381619434 · doi:10.11159/ffhmt23.174

Thermal-Hydraulic Behaviour Comparison of Two Novel Lattice Structures with Simple Cubic BCC Lattice Structure

2023· article· en· W4381619434 on OpenAlexvenueno aff
Abhishek Dey, V. Raghavan, G. Venkatarathnam

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2023
Typearticle
Languageen
FieldMaterials Science
TopicSynthesis and properties of polymers
Canadian institutionsnot available
Fundersnot available
KeywordsCubic crystal systemLattice (music)Simple cubic latticeMaterials scienceCondensed matter physicsCrystal structureThermalStatistical physicsCrystallographyPhysicsThermodynamicsMathematicsChemistryStatisticsMonte Carlo method

Abstract

fetched live from OpenAlex

Additively manufactured lattice structures have the potential to replace traditional fins in heat exchangers. In this study, two new lattice structures with different strut diameters are modelled (namely TYPE-A, TYPE-B, TYPE-C, TYPE-D) and numerically compared with a simple cubic BCC (SC-BCC) lattice structure. The results show that all the lattice structures outperform SC-BCC lattice structure in terms of heat transfer. However, they also exhibit a higher pressure drop than SC-BCC lattice. Although the TYPE-A lattice has the highest lattice heat transfer coefficient, due to its lower fin efficiency, it exhibits less heat transfer than the TYPE-D lattice. TYPE-B lattice, which is a modified version of the TYPE-A lattice, shows less heat transfer and pressure drop. To identify the lattice structure with the best thermal-hydraulic behaviour, area goodness factor of each lattice structure is evaluated. The results reveal that TYPE-A lattice has an area goodness factor almost 50% higher than the SC-BCC lattice. This indicates that TYPE-A lattice structure is better suited for heat transfer applications where high heat transfer is required.

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 score0.019
Threshold uncertainty score0.617

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.279
Teacher spread0.240 · 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 topicSynthesis and properties of polymersFrench-language works237,207