Evaluation of the Effect of Surface Irregularities on the Hydraulic Parameters within Unlined Dam Spillways
Bibliographic record
Abstract
Erosional incidents have heightened the necessity of studies regarding rock mass erosion in unlined dam spillways. Enhanced comprehension of hydraulic erodibility necessitates an investigation into the geomechanical and hydraulic aspects of erosional phenomena. Controlled blasting is commonly employed to establish unlined spillways in rock masses, and this process results in irregularities along the spillway surface profile. Recent research has identified key geometric parameters of rock masses that impact erosion in unlined spillways, such as joint opening, dip and dip direction, and joint spacing. However, the effect of spillway surface irregularities on hydraulic parameters remains uncertain. Numerous studies have examined the surface roughness of rock at the millimeter scale within the domain of hydraulic engineering. Despite these efforts, a noticeable gap persists in our understanding of how surface irregularities specifically exert influence over hydraulic parameters. Currently, there is a lack of a clear equation or methodology to incorporate irregularities into hydraulic erosive parameters. The main aim of this study is to show how such irregularities affect the hydraulic parameters. This study is dedicated to emphasizing the importance of considering these irregularities. Building upon the findings obtained, the core aim of this research is to facilitate the formulation of an equation in future investigations that effectively accounts for these irregularities when calculating hydraulic erosive parameters. To assess the significance of surface irregularities in unlined spillways, computational fluid dynamics (CFD) with ANSYS-Fluent software was employed to analyze 25 configurations of spillway surface irregularities and their effects on various factors, including pressure (total, dynamic, and static pressures), shear stress, flow velocity, and energy. The findings indicated that irregularities significantly influenced the hydraulic parameters. Specifically, an increased irregularity height led to a decrease in maximum velocity, total pressure, and shear stress. Conversely, total energy loss increased, amplifying the rock mass’s vulnerability to erosion due to these irregularities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".