An Automatic Method for Optimizing Venturi Shape in Cavitation Flows
Bibliographic record
Abstract
In order to lower the energy consumption of the fibrillation stage for the pulp and paper industry, a new technology need to be innovated and developed. The current research work deals with a new innovative concept based on creating cavitation in the pulp flow. A venturi nozzle is designed and optimized, where hydrodynamic cavitation is achieved by the so called Venturi effect. This paper focuses on the development of an automatic method for venturi shape optimization. The process of cavitation is hard to control and can cause high mechanical wear, therefore an optimization study of the venturi shape is performed with two main objectives. Firstly, to achieve cavitation that is sustained for as long as possible downstream and secondly to avoid cavitation at the walls. The developed method is a type of two-level optimization based on neural networks and evolutionary optimization. A number of simulations are executed and the optimization is then performed on a solver approximation instead of the real solver, which considerably reduces computation time. The obtained results show the optimal venturi configuration and the relative importance of each shape parameter. The optimal configuration is a clear improvement of the baseline configuration and an improvement also compared to all of the tested samples, thereby validating the optimization method.
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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 | 0.001 | 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".