Multi objective optimization of multi-hole orifices using FSI analysis and NSGA II algorithm
Bibliographic record
Abstract
Multi-hole orifices have better performance than single-hole orifices. In this paper, multi-objective optimization of multi-hole orifices is performed using a Fluid-Solid Interaction (FSI) analysis and multi-objective genetic algorithm (NSGA II). In all numerical analysis, the governing equations of the solid and the governing equations of the fluid are carried out for orifice and fluid around orifice respectively. All calculations are made for a 16-hole orifice with circular holes. The design variable in the optimization process is the distance between the holes of the orifice and thus the amount of shrinkage or expansion of the orifice geometry. The objective functions are the pressure drop created on the sides of the orifice, the deformation and tension created in the orifice structure, which should be maximized, minimized and minimized respectively. In the results section, the Pareto front are presented which represent useful information for designing the multi-hole orifices geometry, and five orifices are also introduced as final design options that have better performance. The results of the sensitivity analysis of the various parameters are also presented and discussed in detail in the multi-hole orifices.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".