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Record W2807371010 · doi:10.22215/etd/2017-11804

Performance Analysis and Modeling of a Printed Circuit Heat Exchanger with Air and Carbon Dioxide as Working Fluids

2017· dissertation· en· W2807371010 on OpenAlexaff
Amr Daouk

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsHeat exchangerCarbon dioxideSupercritical carbon dioxideBrayton cycleMaterials scienceInletSupercritical fluidMechanical engineeringMechanicsComposite materialEnvironmental scienceThermodynamicsEngineeringChemistry

Abstract

fetched live from OpenAlex

A Printed Circuit Heat Exchanger (PCHE) was tested using air and carbon dioxide as working fluids to determine the temperature behavior of the fluid in the PCHE.These tests are conducted and analyzed to pave way for testing the heat exchanger with supercritical carbon dioxide (S -CO 2 ), to obtain data on its performance for use in S -CO 2 Brayton cycles.The tests have been conducted at thermal steady state where a total of 18 data sets have been tested.Air inlet temperature has been varied from 70 o C to 100 o C to 140 o C where both air and CO 2 were both kept at a pressure of either 5 bars or 10 bars while varying the flow from 5 LPM to 10 LPM.Results pertaining to the heat rate and pressure drop were analyzed and discussed.A 3D COMSOL model was created to simulate the PCHE's performance and the results obtained from the simulations have been analyzed and compared to the results obtained experimentally.The results show an average percentage error of 5.65% and 5.73% when comparing the outlet temperatures of air and CO 2 respectively.Further improvements to the test loop is required to remove limitations constricting the range of operation of the loop allowing us to obtain more data in wider ranges of temperatures, pressures and flow rates.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.221
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2017
Admission routes1
Has abstractyes

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