An Innovational, Collision Model and Data Set Generation at NovelTest-Rig for Validation of Numerical Model in the Frame of MachineLearning
Bibliographic record
Abstract
Granular flows are characterised by the high volume fraction of solid phase. To overcome the necessity of building real scale installations required to improve process where particulate transport is involved there is an urgent need of building a mathematical models capable to accurately simulate processes involving great number of particles. While simulating granular flows, the modelling mutual particle interactions still remains the greatest and most time consuming challenge. Due to complexity and great number of interactions, modeling such flows is not a trivial task. Nowadays two main models are available hybrid Euler-Lagrange (HEL) [1] and discrete element method (DEM) [2]. In HEL, interactions between solid phase are determined on the basis of Kinetic Theory of Granular Flow (KTGF), where probability of collision is determined from solid volume fraction in computational cell but time needed to obtain results is relatively short. In DEM, calculating every possible particles interactions is computationally expensive as well as time consuming. To overcome the problem of long lasting calculations and predicting particle interactions, while keeping required accuracy a fast and robust Reduced Model developed based on the algorithms of machine learning. The principle of the idea is to use the large set of particle data (before and after collision) and on their basis to build machine learning model capable to predict the new values of ex. velocities. In such a way created model will be used as a part of HEL approach to substitute forces between particle interactions basing on solid volume fraction with forces obtained using DEM. In such a way the precision of DEM model will be preserved but results will be obtained much faster. Parallel to creating the Reduced Model the experiment tests were carried out. The collected data will serve as the validation set for further analysis. Both experimental and simulation results will be compared using computer vision algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".