Passive thermal management of a lithium-ion battery using carbon fiber loaded phase change material: comparison and optimization
Bibliographic record
Abstract
Phase Change Materials (PCMs) are currently used for many heat management applications. However, the heat transfer performance of PCMs is limited by their low thermal diffusivities. This is a critical issue for high heat flux applications, such as in the thermal management of lithium-ion (Li-ion) batteries. The present work aims the study heat transfer enhancement in a cylindrical Li-ion battery thermal management system consisting of a PCM (paraffin) loaded with randomly distributed and radially oriented carbon fibers. The system was simulated numerically under various cooling conditions, including naturally convecting air, in the presence of pure paraffin, and the presence of carbon fiber-loaded paraffin. The results for orderly arranged carbon fibers were compared with those of random distribution. Numerical results indicated that better battery thermal management can be achieved for the radially distributed carbon fiber arrangement in the PCM. The advantage of radial over random distributions can be due to the constant, uniform, and non-agglomerating distribution of carbon fibers under which thermo-physical properties of carbon fibers are better realized in the composite medium. The presence of carbon fibers with thermal conductivity of k=50W/m K in the PCM has caused more uniform temperature profiles in the radial direction because of the improved thermal conductivities. The results of this research can be used as a guideline for designing a battery thermal management 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 | 0.000 |
| 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".