Numerical study on the stability of reservoir dams by water flow under complex boundary conditions
Bibliographic record
Abstract
Reservoir dams are highly susceptible to unstable conditions under the long-term action of water low.In this paper, we mainly investigate the effect of water low on the stability of reservoir dams under the conditions of complex boundaries.Through the advection orthogonal curve generation network and the use of the adjustment factor on the sparsity of the grid range of values, to achieve the mesh division of the reservoir dam body.The control volume method is used to discretize the control equations of the planar two-dimensional water low model under the complex boundary conditions, and the SIMPLEC method is used for numerical calculations at the same time.For the treatment of complex boundaries, this paper reduces the error between the simulated and real water margins by comparing and selecting the diagonal Cartesian method.The limit equilibrium method is introduced and combined with the strength reduction method to solve the problem together and comprehensively analyze the stability of the reservoir dam under the action of water low.Reservoir dam A is selected as the object of numerical calculation in this paper, and the input information of the model is set by setting parameters and selecting working conditions.The model is used to calculate the stability of the reservoir dam under the action of water seepage and water pressure.Compared to the static condition, the value of displacement and deformation of the dam body is increased by about 52.00% under the water low action condition.The stability of the reservoir dam body under the action of water low in iltration and pressure decreases signi icantly.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".