Simulation of ventilation system with unglazed solar collector and air heat pump
Bibliographic record
Abstract
The European Directive on Energy Performance of Buildings (EPBD) obliges to accept measures for improving the energy efficiency in buildings. As buildings become more airtight ventilation takes an important role in building. Ventilation efficiency as a rule is achieved using heat recovery. The efficiency of the air heat recovery is varies depending on the applied type of heat exchanger and changes during the year. A dozen of heat recovery units, renewable energy sources and technologies can be applied to ventilation system to increase an efficiency of it. It should be an opportunity to add ventilation system with renewable energy sources. Such energy recovery and renewable energy technologies as air heat recovery unit, air-to-water heat pump, and unglazed solar collector (UTSC) for ventilation system of the building are analyzed. TRNSYS simulation tool is applied. Simulation results of three technologies (heat recovery unit, air heat pump and unglazed solar collector) using ventilation system performance during the year is presented. Main indicators of UTSC are presented. Simulation results and obtained experimental data of individual cases of such system are compared.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".