Flood waves on infrastructure and on transport processes in mountain streams
Bibliographic record
Abstract
A series of numerical simulations are conducted to study the flood wave impact on infrastructure and its role on the transport processes in mountain streams. Unsteady forces by the flood waves are calculated for three typical problems. The first is to find the wave forces on a critical infrastructure. In the simulations, the infrastructure is a square block. The calculation shows the forces on the square block are in proportional to the height of the reflected surge wave. A wave-force coefficient is defined to quantify the magnitude of the force. It is found uniquely correlated with the surge-wave Froude number. Thesecond and the third series of simulations are conducted to find the mobility of the submerged obstacles to study transport processes in mountain streams. For the purpose, the simulations for the flows over a submerged square blockand a submerged hemispherical obstacle are conducted. The forces and the tipping moments are calculated from the simulations for a wide range of surgewave discharge coefficient. The goal is to find the critical discharge for the mobility of the gravels, rocks and boulders in the mountain streams. Thesimulation for the water depth and the velocity around the infrastructureand over the rocks and gravels are obtained by numerical solutions of the shallow water equations. The finite-volume approximation of the shallow water equations is implemented on a staggered grid. The shock-capture scheme MINMOD is used to suppress the overshot and undershot across the depth and velocity discontinuity such as the hydraulic jump as the flow changes from supercritical to subcritical state. A fourth-order Runge-Kutta method estimates the advancement of the computation step in time.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".