Thermohydraulic performance of ammonia, isopropanol, water and nanofluids as cooling fluid for lithium-ion 1C and 3C rating batteries
Bibliographic record
Abstract
Cooling Lithium-ion batteries of different C ratings receive excellent attention amongst researchers in thermal management. The present study proposes to investigate the thermohydraulic performance of a wavy channel and compare the finding with the conventional straight channel. The full Navier Stokes equations and the energy equation were solved numerically using the finite element technique. Water is the cooling liquid used in the simulation. The flow is laminar and steady state. It is found that the average Nusselt number is higher as the waviness of the channel wall increases. The average Nusselt number for the wavy channel case was increased by up to 31% compared to the straight channel configuration. Also, the performance evaluation criterion increases by 23% compared to straight channel configuration. Thus, allowing the fluid to absorb more heat. However, at the expense of the pressure drop, the performance evaluation criterion is higher for the wavy wall channel mode than the straight channel wall. Amongst all fluids studied in this paper, including water, ammonia, isopropanol, Ammonia binary mixture and TiO2 nanofluid at different concentrations, ammonia is the most suitable liquid for cooling.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".