Unsteady Analysis of Particle-Laden Gas Flows When Hit by a Moving Shock Wave
Bibliographic record
Abstract
Abstract A successful prediction of the thermo-fluid mechanical characteristics of gas and particles is very crucial and imperative for the successful design and operation of rocket nozzles and energy conversion systems. This paper describes an interaction phenomenon when a moving shock wave hits a two-phase medium of gas and particles. A particle-laden gas is considered to be located along a ramp so that numerical integration is accomplished from the tip of ramp for a finite period. For the numerical solution, a fully conservative unsteady implicit 2nd order time-accurate sub-iteration method and a 2nd order Total Variation Diminishing (TVD) scheme are used with the finite volume method (FVM) for gas phase. For particle phase, the Monotonic Upstream Schemes for Conservation Laws (MUSCL) as well as the solution of the Riemann problem for the particle motion equations is used. Transient development of thermo-fluid mechanical characteristics is calculated and discussed by changing the particle mass density and particle specific heat. Major results reveal that when the particle mass density is smaller, there is a stronger interaction between two phases so that the velocity and temperature differences between two phases more rapidly decrease. When the particle specific heat is varied, only a thermal effect (temperature difference between gas and particles) is observed while the other effects (variations of particle concentration and velocity fields) are minor.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".