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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".