Analysis of a Trombe Wall Prototype with Thermal Energy Storage andAir Vent Management
Bibliographic record
Abstract
Thermal energy storage (TES) mediums can be integrated into Trombe walls to improve the utilization of solar energy to offset building energy loads.The vents at the top and bottom of a Trombe wall can be used to manage the flow of heated air through a Trombe wall to store and deliver thermal energy to match the demand profile in buildings.Herein we investigate the thermal energy stored in fire-clay bricks as a sensible storage medium integrated into a scaled Trombe wall prototype.After 3.8 hours under a solarsimulated light source at an intensity of ~325 W/m 2 , ~33 MJ/kg is stored in the fireclay bricks when the vents are open.In comparison, ~37 MJ/kg is stored in the fireclay bricks under similar experimental conditions when the vents are closed.During the discharging phase, which begins when the light source is turned off, it takes ~3.5 hours for the amount of thermal energy stored in the fireclay bricks to decrease by 50% when the vents in the Trombe wall prototype are open.On the other hand, it takes ~4.4 hours for the amount of thermal energy stored in the fireclay bricks to decrease by 50% when the vents are closed.Improved management of solar thermal energy in Trombe walls can reduce the carbon footprint of residential and commercial 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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".