Numerical Modelling of Flow-Debris Interaction during Extreme Hydrodynamic Events with DualSPHysics-CHRONO
Bibliographic record
Abstract
Floods can transport debris of a very wide range of dimensions, from cohesive sediments to large floating debris, such as trees and cars. The latter increases the risk associated with floods by, for example, obstructing the flow or damaging structures due to impact. The transport of this type of debris and their interaction with structures are often studied experimentally in the context of tsunamis and flash floods. Numerical studies on this problem are rare, therefore the present study focuses on the numerical modelling of the flow-debris interaction. This is achieved by simulating multiple laboratory experiments, available in the literature, of a single buoyant container transported by a dam-break flow in order to validate the chosen numerical approach. The numerical simulations are carried using the open source DualSPHysics model based on the smoothed particle hydrodynamics method coupled with the multi-physics engine CHRONO, which handles the container–bottom interactions. The trajectory, as well as the velocity of the centroid of the container, were tracked throughout the simulation and compared with the same quantities measured in the laboratory. The agreement between the model and the experiment results is quantitatively assessed using the normalised root mean squared error and it is shown that the model is accurate in reproducing the floating container trajectory and velocity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".