Pressure drop and thrust predictions for transonic micronozzle flows
Bibliographic record
Abstract
In this paper, the expansion of xenon, argon, krypton, and neon gases through a Laval nozzle is studied experimentally and numerically. The pressurized gases are accelerated through the nozzle into a vacuum chamber in an attempt to simulate the operating conditions of a cold-gas thruster for attitude control of a micro-satellite. The gases are evaluated at several mass flow rates ranging between 0.178 mg/s and 3.568 mg/s. The Re numbers are low (8–256) and the estimated values of Kn number lie between 0.33 and 0.02 (transition and slip-flow regime). Direct Simulation Monte Carlo (DSMC) and continuum-based simulations with a no-slip boundary condition are performed. The DSMC and the experimental results show good agreement in the range Kn > 0.1, while the Navier-Stokes results describe the experimental data more accurately for Kn < 0.05. Comparison between the experimental and Navier-Stokes results shows high deviations at the lower mass flow rates and higher Kn numbers. A relation describing the deviation of the pressure drop through the nozzle as a function of Kn is obtained. For gases with small collision cross sections, the experimental pressure results deviate more strongly from the no-slip assumption. From the analysis of the developed function, it is possible to correct the pressure results for the studied gases, both in the slip-flow and transition regimes, with four gas-independent accommodation coefficients. The thrust delivered by the cold-gas thruster and the specific impulse is determined based on the numerical results. Furthermore, an increase of the thickness of the viscous boundary layer through the diffuser of the micronozzle is observed. This results in a shock-less decrease of the Mach number and the flow velocity, which penalizes thrust efficiency. The negative effect of the viscous boundary layer on thrust efficiency can be lowered through higher values of Re and a reduction of the diffuser length.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".