Wind tunnel test study on the erosion wear of galvanized coatings on steel members under wind-blown-sand two-phase flow
Bibliographic record
Abstract
Pieces of galvanized steel angle, steel tube, and steel plate were made into test specimens, and then a uniform wind-sand flow field was formed in a reflux wind tunnel to simulate the environment of strong wind-sand areas. Through the wind tunnel testing, the erosion wear laws of wind-blown-sand two-phase flow on these specimens coatings were studied under different erosion mechanical parameters (erosion duration, erosion angle, and wind speed), different specimen aspect ratios (long edge length of the steel plate / short edge length of the steel plate) λ ar and spacing (distance from center to center of the front and the rear steel tube) S . The results have shown that the erosion rate ( E ) of the galvanized coatings initially increases and then decreases as the erosion angle increases. A 19° wind direction to the longitudinal axis of specimens causes maximum erosion. The wind speed exponent of galvanized steel angle and steel plate erosion are 2.41 and 2.33, respectively. With increasing λ ar , E of the galvanized steel plate decreases. Based on the wind tunnel tests, calculation models of E for galvanized coatings under wind-sand action were proposed. Due to the inability to simulate large-scale wind field characteristics, the test data of E obtained by the airflow-sand jetting method were proven to exhibit overestimation. With increasing spacing of the steel tubes, the ratio of E between the rear and front steel tubes λ r f decreases and then increases, before decreasing and then increasing again, possibly due to changes in the flow field, vortex shedding, and interactions between the wake and the downstream tube.
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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 | 0.001 | 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) | 0.001 | 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".