Heat transfer mechanisms in vertical green systems and energy balance equations
Bibliographic record
Abstract
the use of vegetated vertical systems is a sustainable technology for improving the energy efficiency of buildings in cities in order to reduce the energy consumption for air conditioning in summer and to increase the thermal insulation in winter.increasing urban green infrastructure (ugi) in a city can contribute to improve urban climate in summer reducing buildings surface temperature and urban air especially in southern europe.the application of vertical green systems requires the knowledge of the energy performance of the applied greenery system.the choice of the green facades depends on the local climate, water availability, building shape.the presence of green facades affects the building microclimate all day, by reducing heat waves during the warm periods and heat losses from the building in the cold period.the heat and mass transfer between the external environment, the green facades and the building surface determine the building microclimate.solar radiation, long wave infrared radiation, convective heat transfer and evapotranspiration are the main mechanisms of heat transfer in a green façade.the paper describes the main parameters concerning heat flow in green facades that can be used in simulation models for predicting temperatures in buildings using the external weather conditions as model inputs.the input parameters are: external air temperature and relative humidity, solar radiation, wind velocity and direction, plants and building characteristics.the green facade was described by a schematic representation, four layers were defined: the green layer, the external surface of the building wall, the internal surface of the building wall, the air inside the building.the energy balance was defined for each layer and all the terms involved in the energy exchange between the layers were defined as a function of the plant, the weather conditions and the constructive characteristics of the wall.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".