Experimental Validation of the Near‐Bed Particle‐Borne Stress Profile in Aeolian Transport Systems
Bibliographic record
Abstract
Abstract Self‐regulation of sediment transport by wind is widely assumed to derive from the partitioning of momentum from the fluid flow to the particle cloud. Consequently, the fluid stress on the bed surface is suggested by some to drop below that required to entrain particles, while the cloud is sustained by particle ricochet and splash that derive from the impacts of particles moving ballistically along the bed surface. While these theoretical constructs underpin present‐day numerical models of aeolian saltation, the particle‐borne stress has never been measured directly in either a laboratory or field setting. Such measurements are required for model validation and calibration of numerical simulations and were undertaken in vertical profile in the Trent Environmental Wind Tunnel using particle tracking velocimetry. Test sand was normally distributed with a median diameter of 590 μm. Particle momentum was calculated from the diameter and velocity of each image pair captured using high‐speed photography and then summed within each elevation band. The median diameter of the air‐borne particles was found to increase with friction velocity (u*), suggestive of sorting of the bed surface, and to decrease with elevation. Within a two‐dimensional framework, the vertical and windward components of the particle velocity were found to increase with elevation, with u* having little to no influence. Confirming the results of previous numerical simulations, these experiments show that the normalized particle‐borne stress increases exponentially toward the bed surface; however, within the lowest few millimeters where particle splash dominates, no consistent change is observed with elevation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".