A comprehensive numerical study on the current-induced fluid–structure interaction of flexible submerged vegetation
Bibliographic record
Abstract
Submerged vegetation is becoming more and more relevant as a nature-based solution for coastal protection schemes, counteracting the effects of climate change and sea level rise. The numerical model REEF3D has been used to simulate the motion of and forces exerted on flexible vegetation under unidirectional currents. This study emphasizes the critical need for accurate solutions obtained by numerical models to investigate the complex ecosystem services, adopting a direct forcing approach using the immersed boundary method. The fluid–structure interaction capability within the finite difference model is comprehensively evaluated for the simulation of stem motions and forces exerted on flexible vegetation under varying unidirectional flows. Thresholds for numerical parameters, including a minimum number of 25 rigid elements composing the stem, are identified for accurate solutions. The necessity of using large eddy simulations and a Smagorinsky constant of 0.1 to simulate the turbulent flow is demonstrated. The study confirms the accuracy of the implemented fluid–structure interaction model to replicate stem bending (less than 10 % deviation relative to the stem length) and forces across varying hydrodynamic conditions. • First study to show that the FSI solver in REEF3D::CFD can accurately simulate stems. • Comparison of the results utilizing large eddy simulation to RANS turbulence models. • First investigation of the influence of the damping coefficients on the stem dynamics.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".