Multiphysics Numerical Simulation and Geometric Optimization of a Micronozzle for the MEMS Thruster
Bibliographic record
Abstract
This paper reports a numerical research on MEMS (microelectromechanical system) micronozzles through multiphysics coupling simulation along with design optimization based on simulation results. The micronozzle, which is a core component of the electrothermal microthruster, features a micron-scale geometry, a 2-dimensional (2D) Laval configuration, a rectangular cross section, and a highly thermal conductive silicon wall due to MEMS fabrication. As a result, viscous loss in the flow field and heat transfer to the nozzle wall can strongly influence nozzle performance, namely, thrust force and specific impulse. To accurately understand the flow field inside the micronozzle and how the highly thermal conductive silicon wall interacts with gas flow, a numerical simulation that couples fluid dynamics field and solid heat transfer field is employed in the research. The influence of different structural parameters on micronozzle performance is then investigated to set a basis for design optimization. The optimum design of the linear expander micronozzle is obtained through constrained optimization by linear approximation. To further improve micronozzle performance, the bell-shaped expander is adapted. The optimization result shows that the bell-shaped expander is not suitable for micronozzle featuring 2D Laval configuration, and the reason behind the phenomenon is thoroughly discussed.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".