Vegetation stem dynamics under wave loading: Insights from a coupled fluid–structure model
Bibliographic record
Abstract
Accurate replication of the stem motion of submerged aquatic vegetation is crucial to gain insights into its capacity for coastal protection and adaptability, such as the wave attenuation capacity and local sediment mobilization. The fluid–structure interaction solver within the numerical model REEF3D::CFD is validated, and then further employed to analyze vegetation stem motion and resulting drag forces, under wave loads using reference experiments, and covering a wide range of material properties and hydrodynamic conditions found in natural aquatic vegetation. Good agreement between simulated and experimental results is achieved for most test cases. This is expressed by less than 10 % deviation of the simulated to the experimental forces and the stem positions relative to the stem length. To investigate possible sources of discrepancies between the numerical and experimental results, the flow field in front of the stem is compared with that measured during the reference experiments. Bending modes of stem movements provide further insights into the complex dynamic behavior of stems. Additionally, the study demonstrates the influence of the model’s built-in damping coefficients on the time-dependent stem movement under wave load for different material types. For all tested material types, the findings suggest that the use of damping coefficients ranging between 1 × 10 -8 and 1 × 10 -6 led to the successful replication of the stem dynamics and the resulting drag forces. Accurate predictions of vegetation response to wave loading require careful selection of the governing parameters in the structural model replicating the stem. Considering this, the proposed fluid–structure interaction solver proves highly effective for simulating a wide range of flexible, submerged vegetation stems.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".