3D Numerical Analysis of Heat Transfer in a Low Reynolds Number Microturbine Cascade
Bibliographic record
Abstract
This paper presents the conjugate heat transfer in a submillimeter scale microturbine characterized by laminar yet highly three-dimensional flows. Such a miniature turbine is part of a MEMS (microelectromechanical system) power plant-on-a-chip currently under development for distributed power generation from waste heat. Adiabatic subsonic flows in the turbine have previously been studied numerically and are characterized by low Reynolds number laminar flow (Re < 2500) but with complex vortical structures. The present work addresses the influence of these flow structures on heat transfer, including the effect of the horseshoe and tip vortices. Calculations were done for tip clearance gaps equal to 0%, 5% and 10% blade height. Three different scenarios were considered: adiabatic walls, the hub and casing temperature of 573K or the hub at 573K and the casing at 450K, for incoming flow at 600K. The heat transfer is more variable in the suction side since dominant vortices are adjacent to this blade side. The heat flux even changes its sign where the vortices begin to separate from the suction side, indicating that gas cooled in the hub and casing boundary layers is transported on the blades by the horseshoe vortices. The tip vortex prevents the top passage vortex from interacting with the suction side, which eliminates the negative heat transfer in this region. Due to the dominant vortices, the Nusselt number is found to be a function of the thermal boundary conditions and cannot be predicted with traditional boundary layer correlations.
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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) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".