Reduced-Order Fluidic Model for Flow Instabilities in Two-Phase Microfluidic Heat Exchangers
Bibliographic record
Abstract
Two-phase microfluidic heat exchangers have the potential to provide high-heat flux cooling with lower thermal resistance and lower pumping power than single-phase heat exchangers. However, the process of phase change in two-phase heat exchangers can cause flow instabilities that lead to microchannel dryout and device failure [1–3]. Modeling these flow instabilities remains challenging because the key physics are highly coupled and occur over disparate time and length scales. This work introduces a new approach to capture transient thermal and fluidic transport with a reduced-order model consisting of fluidic, thermal, and phase-change submodels. The present study presents a reduced-order, transient, multichannel fluidic circuit submodel for integration into this proposed modeling approach. The fluidic submodel is applicable in flow regimes in which a thin liquid film exists around the bubble. Flow response to boiling is modeled considering bubble overpressure. An adaptive time step approach is used to treat the rapid flow response at short time scales after initial bubble vaporization. Using a seeded bubble technique for testing two-phase flow response, the model predicts a stability threshold at 0.015 W of localized superheating for two 100-micron square channels in parallel with a pump flow rate of 0.15 ml/min. Once integrated with the proposed reduced-order thermal and phase change models, this fluidic circuit model will yield criteria for stable two-phase heat exchanger operation considering factors such as pumping pressure, channel geometry, and applied heat flux that can be compared to experimental observations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".