The Effect of Temperature and Moisture Variation on the HeatTransfer Through Building Envelope
Bibliographic record
Abstract
This paper investigates the impact of the change of thermal conductivity of the insulation layer embedded in a typical residential building on the cooling effect.The simulation has been performed using the polystyrene (EPS), in extremely hot conditions of Al Ain (UAE) at different level of densities denoted as low density LD (12 kg/m3), high density HD (20 kg/m3), ultra-high density UHD (30 kg/m3) and super-high density SHD (35 kg/m3), and three moisture content levels (10%, 20%, and 30%) compared to dry insulation material for LD.The change of the thermal conductivity of the EPS material at different operating temperatures and moisture content has been investigated.The thermal wall resistance was evaluated by applying a conjugate heat transfer model based on enthalpy-based formulation.The thermal performance of the building incorporating polystyrene with variable thermal conductivity (λ -value) was compared to a constant thermal conductivity by quantifying the additional cooling demand and capacity due to the λ-relationship with time using the e-quest as a building energy analysis tool.The results show that, when the λ-value is modelled as a function of operating temperature, its effect on the temperature profile during daytime is significant compared with that obtained when a constant λ-value for the polystyrene (EPS) insulation is adopted, however, this trend is reversed at night time.A similar trend in the evolution of temperatures across the wall section was observed when EPS material was tested with different densities and moisture contents.The monthly energy consumption for cooling required by the building is found to be higher in case of variable thermal conductivity for LD sample.The yearly average change in space cooling demand and cooling capacity employing polystyrene with constant and variable thermal conductivity increases with the increase of the moisture content.Indeed, the highest change in cooling demand and capacity are 6.5% and 8.8% with 30% moisture content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".