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Record W3191607090 · doi:10.17762/de.vi.3595

The Performance of the hairpin type U Shape Double pipe heat exchanger type under effect of using Passive and Active Techniques.

2021· article· en· W3191607090 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDesign Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMechanicsHeat exchangerShell and tube heat exchangerMechanical engineeringMaterials scienceHeat transferHeat transfer enhancementVolumetric flow rateEngineeringHeat transfer coefficientPhysics

Abstract

fetched live from OpenAlex

The process of improving and developing heat exchanger performance has received a lot of attention, and efforts are still being made by specialized researchers and engineers with a huge investigations to increase rate of heat transfer to lessen the volume size and price cost of the factories apparatus accordingly. In this experimental study, a suitable heat exchanger equipped with flow meters and thermocouples for measuring flow rates and temperatures was used with the U shape hairpin type exchanger. The bending and angle of curvature of the tubes causes vortex flow, which greatly aids to attractive the rate of heat transfer process and increase the performance, The effect of active and passive techniques with different positions of the U shape Exchanger like position (U shape and Inverse U shape ) as parallel coupling with tube liquid in series is investigated during this study. passive technique represented using the O ring fin type. and an active technique represented by the injection of an air bubble by a small compressor through a special air diffuser. The results show that the best application was with inverse U shape (∩) and the performance enhanced about (19.1%) in the case of active techniques while and (11.1%) with passive techniques and by applying both techniques together, the overall enhancement was (30.272%), So this study provides new visions for further studies.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.307

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 head0.015
GPT teacher head0.216
Teacher spread0.201 · 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