Development of capillary-assisted low pressure evaporator for adsorption chillers
Bibliographic record
Abstract
The sales of air conditioners are poised to intensely increase over the next several years as incomes and global temperatures rise around the world. Conventional air conditioning systems use vapor-compression refrigeration (VCR) technology that has been the dominant technology for close to a century. However, the environmental impact of VCR systems, particularly their high energy consumption, around 36% of energy consumed in the US building sector, is contrary to sustainable development. In addition to the residential sector, VCR systems for vehicle air conditioning (A/C) applications can cause a 20% increase in fuel consumption. Moreover, while the commonly used refrigerants in VCR systems, hydrofluorocarbons (HFCs), are ozone-friendly, they still contribute to global warming. Alternative, natural refrigerants, such as water, have no toxicity and significantly lower global warming potential compared to HFCs. Furthermore, water is an ideal refrigerant for systems driven by low-grade thermal energy. Solar-thermal and waste-heat from industrial facilities and data centers are all abundant sources of low-grade thermal energy, with a temperature less than 100°C. Low-grade thermal energy can be used to run adsorption chillers for air conditioning of vehicle cabins and residential units. When using water as an air conditioning refrigerant, evaporation occurs at pressures below an atmosphere. In such a low pressure (LP) environment, the performance of a flooded evaporator is negatively affected by the hydrostatic pressure. This problem can be resolved by using a capillary-assisted low-pressure evaporator (CALPE) that exploits thin film evaporation. The focus of this doctoral research is to develop an effective CALPE for proof-of-concept demonstration of an adsorption chiller for vehicle A/C applications. In this research, a low pressure evaporator testbed is designed and built for the first time at Laboratory for Alternative Energy Conversion (LAEC) to test CALPE. In addition, a mathematical model is developed to understand detailed phenomena in capillary-assisted evaporation and to provide insight to design an effective and compact CALPE. Several commercial tubes with different fin geometries are tested. The results show that the capillary-assisted tubes provide two times greater heat transfer rate compared to a plain tube. To further enhance the performance, the outside surfaces of CALPE are coated with a thin film of porous copper to increase the capillary action and the surface area available for thin film evaporation. The coating increased the overall heat transfer coefficient by 30%. However, a significant amount of the thermal resistance is from the inside of the evaporator tubes. Therefore, a new µCALPE is designed with microchannels on the inside and rough capillary channels on the outside is 3D printed by using direct metal laser sintering process. The internal microchannels and external capillary channels led to enhanced heat transfer both internally and externally. The µCALPE increased the overall heat transfer coefficient by a factor of 2.5 when compared to the CALPE built with commercial Turbo Chil-40 FPI tubes, which had footprint of four times larger than that of µCALPE. The developed µCALPE is expandable to the entire low-grade thermal energy driven A/C systems in vehicles as well as residential units.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".