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Record W4402454955 · doi:10.11159/htff24.127

Performance Characterization of a Small-Scale Pillow Plate Heat Exchanger Designed with the Effectiveness-NTU Method

2024· article· en· W4402454955 on OpenAlexvenueno aff
Alessandro Dai Pré, Luca Marchetto, M. Grigiante

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHeat exchangerScale (ratio)Characterization (materials science)Materials sciencePlate heat exchangerMechanical engineeringComputer scienceEngineeringPhysicsNanotechnology

Abstract

fetched live from OpenAlex

In recent years the design of pillow plate heat exchangers (PPHE) is attracting more and more interest both in the scientific community and in relevant industrial sectors.In this manuscript an experimental investigation has been carried out to study the thermo-hydraulic behaviour of a very compact PPHE designed in collaboration with a manufacturing company.This has allowed to design a PPHE with the smallest geometric parameters currently achievable with the present manufacturing technologies.This device mounts two pillow plates 450 mm long and 80 mm wide with an internal inflation of 3mm.In this preliminary analysis water is used as working fluid both for hot and cold channel of the PPHE which has been installed on a purpose-built laboratory-scale setup.The design of the PPHE has been carried out by implementing the efficiency-NTU number (-NTU) method specific to PPHE geometries.The inside h1 and outside h2 heat transfer coefficients have been determined by correlations available in literature for normal types of PPHEs.The strength of the adopted approach has been verified by evaluating the errors percentage for the outlet temperatures, the efficiency and the Darcy factor referred to a wide experimental campaign.In terms of errors, the performance for predicted thermal power set from -15% to 15%, for thermal efficiency from -13% to 10%, and between 15% and 30% underestimation for the Darcy factor.The proposed procedure looks a promising engineering tool to be implemented for those applications involving small scale PPHE.

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 score0.124
Threshold uncertainty score0.549

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 head0.006
GPT teacher head0.192
Teacher spread0.186 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

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