An analytical model for estimation of internal erosion rate
Bibliographic record
Abstract
Estimating erosion rate of solid particles in a porous medium is of interest to geotechnical engineers; which use analytical or numerical models for this purpose. Constitutive law of erosion is a key component in the development of such models. These models estimate the solid erosion rate as a function of various modelling parameters such as fluid velocity and time.Using the principles of dimensional analysis, a constitutive law is proposed for assessment of the rate of erosion in relation to the fluid velocity and a dimensionless proportionality constant called the erosion coefficient, λ. Based on physics of the erosion process, experimental observations and approximation theory, λ is expressed as a function of grain density, particle Reynolds number and porosity variation. Then, the proposed constitutive model is combined with the principle of conservation of mass to arrive at an analytical model for estimation of internal erosion rate.The analytical model shows that the erosion rate has a non-linear direct relationship with fluid velocity and a non-linear inverse relationship with time. The proposed analytical model is calibrated and validated using experimental data available in the literature. The validation results show that the model estimations of erosion rate can closely reproduce experimental data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".