Numerical assessment of ceramic micro heat exchangers working with nanofluids by Taguchi optimization approach
Bibliographic record
Abstract
The rapid advancements in microsystems technology have necessitated the exploration of innovative materials for efficient thermal management in micro heat exchangers. This research delves into the performance evaluation of three ultra-high temperature ceramics (UHTCs): ZrB2, BeO, and Si3N4 as alternative micro heat exchanger fabrication materials. The study systematically assessed the ceramics' interaction with Al2O3-nanofluids across diverse volume percentages and mass flow rates using the Taguchi optimization method. Beryllium oxide (BeO) emerged as the superior material, registering warm outlet temperatures as low as 64.86°C and cold outlet peaks at 31.68°C. Sensitivity analyses further underscored the critical role of inlet temperature on outlet dynamics, with warm and cold outlets showing significances of ~72% and ~99%, respectively. Additionally, the research pinpointed 0.75 vol% as the optimal Al2O3-nanofluid concentration, yielding the most favorable performance metrics across the ceramics.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".