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Record W4289275284 · doi:10.1615/ichmt.2022.conv22.320

HEAT TRANSFER RECTIFICATION FOR ENERGY MANAGMEMENT IN BUILDINGS

2022· article· en· W4289275284 on OpenAlexaff
A. A. Mohamad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHeat transferThermal resistanceMaterials scienceRectificationMechanicsFlow (mathematics)Electrical resistance and conductanceMechanical engineeringThermal contactHeat sinkWork (physics)ThermalFluid dynamicsElectrical engineeringComposite materialPhysicsThermodynamicsEngineeringVoltage

Abstract

fetched live from OpenAlex

Due to the nature of the heat transfer through materials, conventional materials are not sensitive to the direction of the heat flow. In other words, the thermal resistance of a material is the same regardless of the heat flow direction. In the rectification process, the material should have high thermal resistance in one direction of heat flow and low thermal resistance if the heat flow direction is reversed. Scientists and engineers successfully developed materials and devices to make the electrical (electrons) or fluid flow resistance directionally sensitive. For example, electrical diodes have a low electrical resistance to the electrons flow in one direction and high resistance in the reverse direction. Similarly, one-directional valves in fluid flow have low hydraulic resistance in one direction and high resistance in the reverse direction of the flow. In this work, we review a few attempts to develop materials and devices sensitive to the heat flow directions. Also, we discuss our attempt to develop a dynamic wall based on the heat transfer rectifier concept. The proposed wall is sensitive to the heat flow direction for energy management in the buildings.

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: Empirical
Teacher disagreement score0.595
Threshold uncertainty score0.172

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 head0.010
GPT teacher head0.194
Teacher spread0.184 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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