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Record W2949477607 · doi:10.22215/etd/2018-13177

Mathematical Modeling and Testing of a Loop Heat Pipe Using a Two – Way Pressure Regulating Valve

2018· dissertation· en· W2949477607 on OpenAlexaff
Juan Posada

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsLoop heat pipeBody orificeMechanicsMaterials scienceHeat transferEvaporatorVaporizationThermodynamicsHeat pipeEngineeringMechanical engineeringHeat exchangerPhysics

Abstract

fetched live from OpenAlex

A loop heat pipe (LHP) is a heat transfer device that uses the vaporization of a working fluid to transfer heat from an evaporation section to a condensing section.The operating characteristics of a LHP with a two-way pressure regulating valve (PRV) is experimentally investigated using a flight-qualified LHP.Experiments suggest that an orifice within the PRV causes a premature start-up.It is determined that the LHP is unable to control the evaporator temperature to 31 ℃ roughly after 130 W under test conditions.The LHP is found to be more sensitive to changes in sink temperatures when operating at higher powers.A steady-state LHP model is developed and is found to compare well with experimental results.It is determined that modeling the effects of the PRV on the LHP operation is not feasible using measurements due to the extreme sensitivity of the model to small changes in fluid pressure.iii ACKNOWLEDGEMENTS Firstly, I would like to express my sincere gratitude to my supervisor, Prof. Tarik Kaya, for giving me the opportunity to further my education and for providing me with an experience that I will always remember.His guidance, support, patience, flexibility and immense knowledge helped

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)0.001
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.285
Teacher spread0.247 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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