Thermal And Hydraulic Performance Of A Novel Evaporator Coil For Refrigeration Systems
Bibliographic record
Abstract
With increasing energy cost and constraints on emission standards, there is a strong need to reduce energy consumption and/or increase equipment efficiency.Refrigeration systems are the third largest use of electricity globally, consuming 1.8 trillion kWh annually.There have been some recent developments in energy efficient refrigeration systems, driven by rising price of electricity and increasing environmental concerns.However, refrigeration equipment remains highly energy demanding.One of the key sources of high energy consumption is the need to periodically heat up the system to defrost the evaporator coils.To solve this problem and reduces the energy demand of the defrosting system, a new evaporator coil is designed which can be defrosted at a fraction of the required energy.The new coil is a finless spiral-helical coil that uses a patented defrost/de-icing technology.In this paper, experimental investigations are presented that compares the thermal and hydraulic performances of a conventional finned-tube coil with a finless coil for a small cooling capacity unit.An experimental setup is designed and built to measure the cooling capacity and air pressure drop of both finned-tube and finless evaporator coils.The results show a higher cooling capacity per surface area for the new finless coil than the finned-tube one and that is mainly due to its higher heat transfer coefficient; making it suitable for this application.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".