Improving Oil Cooling Efficiency Using Polymeric Hollow Fibers
Bibliographic record
Abstract
Oil cooling plays a critical role across various industries, such as transformer cooling, automotive, computing, and aerospace.This study introduces an innovative approach to oil cooling, utilizing a thin-walled polymeric hollow fiber heat exchanger.Research mainly focuses on oil cooling in combustion engines, which represent one of the most widespread applications.The designed cooling system is highly adaptable for a wide range of applications.The presented solution offers an attractive alternative characterized by low energy consumption, reduced CO2 emissions, and high specific heat transfer performance.The innovative approach lies in the use of polymer hollow fibres instead of standard aluminium heat exchangers.This strategy also saves space in the engine compartment as the heat exchanger is located in the engine oil sump.This heat exchanger is manufactured from polyamide (PA612) with an outer fiber diameter of 1 mm.Despite the low thermal conductivity of PA612, the polymeric hollow fibre heat exchanger has low thermal resistance owing to its thin wall thickness of only 0.08 mm.The proposed solution underwent rigorous testing on a combustion engine test rig capable of simulating real-world engine operating conditions.The results show that the designed cooling system achieved thermal outputs up to almost 1250 W (with a water flow rate of 1.5 l•min-1 in the heat exchanger).The engine coolant temperatures did not exceed 83.5 °C, remaining within the standard limits.Thus, the proposed system fulfils its function as an oil cooling system.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".