FLOW AND THERMAL PERFORMANCE IN TWO-PASS CHANNELS WITH INTERCONNECTING STRAIGHT AND CONVERGING SLOTS FOR ELECTRONICS COOLING APPLICATIONS
Bibliographic record
Abstract
Effective cooling of electronic systems in electric vehicles is critical for maintaining thermal performance and reliability while minimizing system weight and complexity, particularly in compact configurations like two-pass channels, where flow distribution and thermal efficiency are challenging. In this study, the passive modification of a two-pass cooling channel was investigated using various types and sizes of interconnecting slot configurations. The interconnecting slots were installed in the divider wall to direct coolant to the downstream channel leg, improving the cooling efficiency for onboard circuits on the channel top. The simulations were conducted at a Reynolds number of 1.2 &times; 10<sup>4</sup>, based on the channel hydraulic diameter of <i>D<sub>h</sub></i> &#61; 26.3 mm. The simulations employed Reynolds-Averaged Navier-Stokes (RANS) modeling with the shear-stress transport (SST) <i>k-&omega;</i> turbulence model. Two slot designs, including straight and converging interconnecting slots with three slot sizes of 0.08<i>D<sub>h</sub></i>, 0.16<i>D<sub>h</sub></i>, and 0.24<i>D<sub>h</sub></i>, were analyzed. The introduction of interconnecting slots yielded significant performance improvements. Hydrodynamically, the slots substantially reduced the channel pressure drop compared to the no-slot baseline by up to 65&#37; for the largest slot and 36&#37; for the smallest. Thermally, the maximum module temperature slightly decreased with slot integration, while temperature uniformity improved, as shown by a 13&#37; reduction in the inter-module temperature difference relative to the baseline. Notably, the straight and converging slot configurations achieved comparable levels of heat transfer enhancement and pumping loss reduction. However, the converging slot design requires more material removal from the channel wall, thereby reducing the overall weight of the cooling system while maintaining similar performance.
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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 | 0.000 | 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) | 0.000 | 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".