A Silicon Microturbopump for a Rankine-Cycle Power Generation Microsystem—Part I: Component and System Design
Bibliographic record
Abstract
This paper presents the design approach for a microturbopump, which is the core component of a micro steam turbine power plant-on-a-chip that implements the Rankine thermodynamic cycle for micro power generation. The turbopump integrates components that are demonstrated for the first time at microscale, such as a four-stage radial planar type microturbine and a one-sided hydrostatic thrust bearing (TB) system, along with a spiral groove viscous pump, a partially grooved seal, and a hydrostatic journal bearing. This paper presents the analytical models developed for each component, including a flow resistance model for the TB and models based on lubrication theory for the pump and seal. They are integrated to enable the microsystem design by satisfying force and power balance conditions on the rotor. Considering our previous thermodynamic cycle analysis on the Rankine micro power generation system, which is aimed at generating a few watts of electric power for applications in portable electronics or waste energy harvesting, we have designed a centimeter-scale demo turbopump device delivering 4.7 W of turbine mechanical power and 71% of turbopump efficiency in order to demonstrate the effectiveness of the component design models and system design principles. Fabrication and testing of the microturbopump are presented in the second part of this two-part paper.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".