Temporal evolution of flow field structure for vehicles accelerating in evacuated tube transportation system
Bibliographic record
Abstract
As a supersonic transportation system, the flow around a vehicle in an Evacuated Tube Transportation (ETT) system will evolve through series of flow structures during acceleration. The occurrence of choked flow and shock wave will especially lead to the drastic change in flow field structures. In this study, based on the one-dimensional inviscid flow assumption, a theoretical model is established to quantitatively describe the formation time, formation location of choked flow, and Frontal Normal Shock Wave (FNSW), as well as the distance of the disturbed flow field region ahead of the vehicle in the ETT system. It is found out that the formation time of FNSW as well as the initial distance between the vehicle head and FNSW is linearly proportional to the blockage ratio while linearly inverse proportional to the acceleration rate of the vehicle. An experimentally verified numerical model is also established with an overset mesh technique to investigate the flow field evolution for vehicles accelerating in the ETT system. The results from numerical analysis agree well with the theoretical model. Meanwhile, five typical flow field structures are summarized for a vehicle accelerating from a stationary state to supersonic state in the ETT system. The applicability of the wind tunnel method and overset mesh technique in numerical simulation of the ETT system is systematically discussed. The influence of occurrence and dissipation of choked flow and shock wave on the vehicle's aerodynamic drag profile are then analyzed quantitatively.
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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 | 0.001 | 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) | 0.001 | 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".