Effect of the surface water content on dry saltation cloud dynamics: A wind tunnel simulation with particle tracking velocimetry
Bibliographic record
Abstract
Abstract The mass transport rate of wind‐borne particles, and indeed the fluid stress required for their entrainment, is strongly governed by inter‐particle cohesion arising from water retained through adsorption and capillary force. This paper reports on a series of wind tunnel experiments in which high‐speed photography was used to record the motion of dry sand particles as they impinged on test beds of systematically varied target gravimetric pore water content (0% ≤ W ≤ 10%). The wind friction velocity was preset to 0.33 m s −1 , sufficient to maintain a saltation cloud above an upwind strip of dry sand, which then was blown over the wetted surface. Discrete particle trajectories were identified in the camera images using expected particle area searching–particle tracking velocimetry (EPAS‐PTV). Adding progressively more water produced an exponential decrease in the normalized particle number density over the test surface. The largest response was achieved by wetting the bed surface to just 2%. To reduce the number of particles by a further 40%, it was necessary to add eight times more water, signifying a diminishing return regarding water use. Relative to particles either rebounding or splashed from a dry bed, the total particle velocity increased incrementally by a factor between 1.5 and 2 with increasing water content. Increasing amounts of pore water were associated with progressively higher saltation trajectories, reaching a plateau beyond W ~8%. The spatio‐temporal adjustment in the sand cloud was observed to be extremely rapid. There is no consensus in the literature on how to measure the water content that effectively governs aeolian transport. In this study, all approaches to sampling W produced strong correlation (R 2 ≥ 0.85). Sampling the topmost grains, however, provided the most accurate prediction of the normalized number density over the full range of water content.
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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 | 0.001 | 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) | 0.000 | 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".