Investigation of the Entropy Generation and Exergy Destruction Rates for a Novel Micro-Jet Heat Sink Working With a Nanofluid for Efficient Cooling of Motor Inverters in Electric Vehicles
Bibliographic record
Abstract
Abstract Unlike the first law analysis which quantifies energy in systems, the second law of thermodynamics specifies the quality of energy and the direction of the processes. In this research, a novel heat sink based on micro-jet impingement for cooling inverters in electric vehicles is thermodynamically analyzed to establish the entropy generation and exergy destruction rates. The numerical simulations are performed using the finite volume method implemented in ANSYS Fluent. Numerical analyses are performed for typical motor inverter heat fluxes ranging from 100 to 300 W/cm2, Reynolds number between 5,000 and 20,000. Alumina-water nanofluid was considered with nanoparticles at concentrations of 0, 0.008, and 0.017 by volume. Results indicate that the maximum reduction in the thermal entropy generation rate is 6.65% when the nanoparticle concentration rises to 0.017. Whereas the frictional entropy generation rate increases by 73% for the same increase in nanoparticle volume fraction. Despite this, the total irreversibility drops as the concentration rises such that the highest reduction in the total irreversibility is 6.1% since the thermal entropy generation rate is the dominant source of irreversibility. With this, utilizing nanofluids decreases the exergy destruction rate leading to a lower amount of wasted energy. When the concentration is increased from 0 to 0.008 and 0 to 0.017 at a heat flux of 100 W/cm2, the optimal Reynolds numbers for maximum reduction in exergy destruction are 10,000 and 5,000, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".