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Record W1505214303 · doi:10.5772/19483

Numerical Modeling and Experimentation on Evaporator Coils for Refrigeration in Dry and Frosting Operational Conditions

2011· book-chapter· en· W1505214303 on OpenAlexaff
Zine Aidoun, Mohamed Ouzzane, Adlane Bendaou

Bibliographic record

VenueInTech eBooks · 2011
Typebook-chapter
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsRefrigerationHeat exchangerRefrigerantHeat transferMechanical engineeringEvaporatorCopper in heat exchangersPlate fin heat exchangerAir source heat pumpsProcess engineeringAir conditioningCondenser (optics)DefrostingHeat transfer enhancementHeat pump and refrigeration cycleEngineeringNuclear engineeringThermodynamicsPlate heat exchangerHeat transfer coefficient

Abstract

fetched live from OpenAlex

The drive to improve energy efficiency in refrigeration and heat pump systems necessarily leads to a continuous reassessment of the current heat transfer surface design and analysis techniques. The process of heat exchange between two fluids at different temperatures, separated by a solid wall occurs in many engineering applications and heat exchangers are the devices used to implement this operation. If improved heat exchanger designs are used as evaporators and condensers in refrigerators and heat pumps, these can considerably benefit from improved cycle efficiency. Air coolers or coils are heat exchangers applied extensively in cold stores, the food industry and air conditioning as evaporators. In these devices, heat transfer enhancement is used to achieve high heat transfer coefficients in small volumes, and extended surfaces or fins, classified as a passive method, are the most frequently encountered. Almost all forced convection air coolers use finned tubes. Coils have in this way become established as the heat transfer workhorse of the refrigeration industry, because of their high area density, their relatively low cost, and the excellent thermo physical properties of copper and aluminum, which are their principal construction materials. Compact coils are needed to facilitate the repackaging of a number of types of air conditioning and refrigeration equipment: a reduced volume effectively enables a new approach to be made to the modular design and a route towards improving performance and size is through appropriate selection of refrigerants, heat transfer enhancement of primary and secondary surfaces through advanced fin design and circuit configurations. Circuiting, although practically used on an empirical basis, has not yet received sufficient attention despite its potential for performance improvement, flow and heat transfer distribution, cost and operational efficiency. In the specific case of refrigeration and air conditioning, a confined phase changing refrigerant exchanges heat in evaporators with the cold room, giving up its heat. The design and operation of refrigeration coils is adapted to these particular conditions. Geometrically they generally consist of copper tubing to which aluminum fins are attached to increase their external surface area over which air is flowing, in order to compensate for this latter poor convection heat transfer. Coils generally achieve relatively high heat transfer area per unit volume by having dense arrays of finned tubes and the fins are generally corrugated or occasionally louvered plates with variable spacing and number of passes. Internal heat transfer of phase changing refrigerant is high and varies www.intechopen.com Two Phase Flow, Phase Change and Numerical Modeling 28 with flow regimes occurring along the tube passes. Flow on the secondary surfaces (outside of tubes and fins) in cooling, refrigeration or deep freezing, becomes rapidly complicated by the mass transfer during the commonly occurring processes of condensation and frost deposition, depending on the air prevailing conditions. Overall, geometric and operational considerations make these components very complex to design and analyse theoretically.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.811

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 head0.038
GPT teacher head0.267
Teacher spread0.229 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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