Design, Fabrication, and Testing of a Polymer Expanded Heat Exchanger for Absorption Chilling
Bibliographic record
Abstract
Abstract The increasing demand for energy-efficient and environmentally friendly cooling technologies has driven the exploration of advanced heat exchanger (HX) designs. Traditional metal HXs, while effective, are often heavy, expensive, and prone to corrosion. This study addresses these challenges, presenting the design, fabrication, and testing of a polymer expanded heat exchanger (PEHX) for a high-pressure, water–ammonia–helium absorption refrigerator. Utilizing open-source laser welding and 3D printing, the PEHX was constructed from linear low-density polyethylene and acrylonitrile butadiene styrene. The PEHX achieved an effectiveness of 0.62, a 13% improvement over the existing heat exchanger's 0.55, potentially reducing the refrigerator's power consumption by 5 W. Over a 10-year lifespan, this could save approximately 453 kWh of energy, equivalent to electricity costs of $68 and greenhouse gas emissions of 321 kg(CO2,e). However, the PEHX exhibited a higher pressure drop than the existing heat exchanger, necessitating further design improvements, including optimized welding techniques, alternative flow patterns, and redesigned headers to reduce pressure drop. This work demonstrates the potential of additive manufacturing of polymer heat exchangers for applications requiring lightweight, cost-effective, and corrosion-resistant heat transfer solutions, and highlights areas for future research.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".