Performance Evaluation of a Liquid Desiccant Solar Air Conditioning System
Bibliographic record
Abstract
The current work focuses on the Queen's University Solar Liquid Desiccant Cooling Demonstration project. A solar Liquid Desiccant Air Conditioning system (LDAC) has been installed at a field site in Kingston, Ontario, Canada, and is currently being tested to evaluate the performance of the system when driven by solar energy. The installed system features a low-flow parallel plate liquid desiccant air conditioner, and a 95m2 evacuated tube solar collector array. While summer testing has only recently begun, five test days have shown an overall solar collector efficiency of 56%, solar fraction of 63% and a thermal COP of 0.47. The average total cooling was 12.3 kW and average latent cooling was 13.2 kW. The solar array was also operated between October 2011 and May 2012 and heat was rejected using a dry cooler. Over the heating season 18,800kWh were collected with an average collection efficiency of 61%. TRNSYS simulations over-predicted the amount of energy collected by 13% (21,264kWh) likely due to failed vacuum tubes, snow cover of collectors, and improper sensor location.
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 | 0.000 |
| 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 | 0.000 | 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".