Sensitivity analysis of fluid flow parameters on the performance of fully dense ZrB2-made micro heat exchangers
Bibliographic record
Abstract
Heat exchangers are important in modern technology and are used in various industries such as power plants, automobiles and airplanes. Their main role is to ensure efficient heat transfer tailored to specific system needs. With miniaturized electronics, challenges such as circuit overheating have emerged, increasing the demand for compact yet high-performance heat exchangers. The advent of micro-electromechanical systems has increased the application of micro heat exchangers with their high surface-to-volume ratio promising enhanced efficiency. Although metals such as aluminum are commonly used for fabricating heat exchangers, their susceptibility to corrosion and high temperatures limits their usefulness. This study turns attention to ultrahigh temperature ceramics, specifically fully sintered ZrB2, known for their high temperature durability and oxidation resistance. Utilizing the Taguchi approach, a robust optimization method, this study explores the sensitivity analysis of fluid flow parameters on the performance of fully dense ZrB2-made micro heat exchangers and highlights the potential of ceramics in heat exchanger construction. Based on the results, the mass flow rate with an estimated contribution of 4.4% in the effectiveness is the most influential parameter on the performance, and in the best case, the effectiveness reaches 24.3%.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".