Use of Nanofluid in Multiple Channels Toward Cooling Lithium-Ion Battery
Bibliographic record
Abstract
Cooling lithium-ion batteries has been an interesting research subject in the energy sector. This paper addresses the means for a better cooling mechanism in this context. Two different fluids are used, mainly distilled water and nanofluid composed of 2%vol titanium oxide and 98% distilled water. The channel totaling six, has rectangular shapes with 2 mm in height. The channels are sandwiched between two sets of lithium-ion batteries used in a commercial application. The model governing equations for flow and heat transfer have been solved using the finite element technique. COMSOL software has been used in this analysis. Furthermore, pin-fins have been used to improve the cooling process. Results revealed that adding metallic nanoparticles (i.e., nanofluid) could enhance water as a cooling liquid. A 12% heat enhancement is noticeable using nanofluid but at the expense of the pressure drop. On the other hand, using pin-fins combined with nanofluid has been shown to have created a reverse flow in the channel and improved heat extraction by up to 29%. The location of the pin fin also demonstrates an additional parameter to be considered for heat enhancement.
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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".