Self-adaptive microvalve array for energy efficient fluidic cooling in microelectronic systems
Bibliographic record
Abstract
In the present work, the performance of temperature-regulated microvalves is investigated analytically for energy efficient fluidic cooling of microelectronic systems. The objectives are to decrease the overall mass flow rate of coolant (hence decreasing the pumping power) as well as to improve the temperature uniformity across the chip surface with hot spots. For this purpose, temperature-regulated microvalves are used to manage the coolant mass flow rate distribution throughout the chip based on the local chip temperature. The aim of this study is to find the optimum temperature response function of the microvalves to have more energy efficient cooling. Linear, quadratic and exponential temperature response behaviors are considered for the microvalves. Results show that for the linear microvalves, the mass flow rate and the temperature non-uniformity across the chip decrease by 50% and 29% respectively by using active self-adaptive microvalves, compared to the reference condition without any microvalve. These enhancement values are respectively 45% and 55% when using exponential instead of linear microvalves. This study shows that the concept of selfadaptive microvalve arrays for distributed chip cooling can have a significant impact on power and performance, opening a new approach for microfluidic cooling compared to traditional fixed microchannels.
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) | 0.001 | 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".