Thrust Calculation for Low-Reynolds-Number Micronozzles
Bibliographic record
Abstract
Micronozzles represent a unique flow regime defined by low Reynolds numbers and supersonic Mach numbers. Currently, the classic method of calculating thrust is used by the micropropulsion community to determine nozzle performance from simulation data. This approach accounts for momentum flux and pressure imbalance at the nozzle exit, and it assumes that the viscous stress tensor’s contribution to thrust is negligible. This assumption, however, can break down at low Reynolds numbers, where viscous forces play a significant role in the flow dynamics. In this paper, an extended method of calculating thrust, which accounts for the force due to the viscous stress tensor, is derived from the Navier–Stokes equation. Computational fluid dynamic simulations are then used to assess and quantify the error produced by the classic method at low Reynolds numbers . Two micronozzle geometries are used as test cases: 1) an truncated planar plug nozzle, and 2) a 30 deg linear-walled planar de Laval nozzle. Results indicate that the accuracy of the classic method begins to break down at , below which there is a significant risk that the classic method will produce erroneous results. Moreover, for , the classic method has the potential to misrepresent the thrust of a simulated micronozzle by 50%.
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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) | 0.000 | 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) | 0.001 | 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".