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Record W1990837543 · doi:10.5539/mas.v6n6p54

A Numerical Method for Rating Thermal Performance in Spiral Heat Exchangers

2012· article· en· W1990837543 on OpenAlexvenueno aff
Martin Martinez Garcia, Miguel Ángel Moreles

Bibliographic record

VenueModern Applied Science · 2012
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsSpiral (railway)Heat exchangerMechanicsNTU methodMaterials scienceHeat transferThermodynamicsMicro heat exchangerFlow (mathematics)InletHeat transfer coefficientThermalDynamic scraped surface heat exchangerPlate heat exchangerMechanical engineeringCritical heat fluxPhysicsEngineering

Abstract

fetched live from OpenAlex

The steady state heat transfer equations associated to two fluids in counter flow, hot and cold, in a spiral heat exchanger are considered in this study. A numerical method for solution was proposed to approximate the temperature distribution and overall heat transfer coefficients using the flow rates and the temperatures at inlets and outlets. In particular the effectiveness and correction factor were computed as performance parameters and develop a tool for design. The method is tested and validated on two actual spiral heat exchangers that were reported in the literature.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.781
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.259
Teacher spread0.238 · 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 designSimulation or modeling
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

Citations10
Published2012
Admission routes1
Has abstractyes

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