Geometric optimization of coiled flow inverters to enhance biodiesel production using <scp>CFD</scp> and genetic algorithms
Bibliographic record
Abstract
Abstract This study explores a novel approach to the geometric optimization of coiled flow inverters (CFIs) aimed at enhancing biodiesel production efficiency. By simulating nine distinct CFI geometries using advanced computational fluid dynamics (CFD) and genetic algorithms (GA), this research introduces innovative methods for optimizing fluid flow characteristics. The CFD model yielded essential hydrodynamic data and friction factors, while oil conversion percentages for biodiesel were derived from existing literature. The integration of CFD results with experimental data significantly informed the GA optimization process, marking a key advancement in the field. Two new correlations were developed to predict friction factors and oil conversion percentages based on the coil length‐to‐diameter ratio, Reynolds number, and the number of 90° bends. This study uniquely identifies optimal geometries through a GA‐based multi‐objective approach, effectively balancing oil conversion and friction factor. Additionally, it delves into the trade‐offs between improving oil conversion and the resultant increase in pressure drop, highlighting the intricate complexities of fluid flow in CFIs and their implications for biodiesel production efficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".