Optimizing Metal‐Surface Water Disinfection: CFD Study on Microorganism Collision Against a Triply Periodic Minimal Surface
Bibliographic record
Abstract
Abstract This research presents a point‐of‐use (POU) water treatment technology utilizing metal/metallic surfaces based on Triply Periodic Minimal Surface (TPMS) structured filtration infills. Computational Fluid Dynamics modeling using Ansys CFX software is employed to analyze the behavior of Escherichia coli bacteria within a continuous liquid phase moving through the filtration infill, assess particle collision dynamics, and evaluate the efficiency of filtration, considering pressure drop as a fundamental factor in process energy consumption. TPMS infill meshes are coded in a Schwarz P shape using Python, and the Darcy‐Forchheimer equation is employed to determine the permeability and resistance loss coefficient of the infill geometry. The results indicate that the TPMS infill efficiently captures particles while introducing a negligible pressure drop into the system. It is found that a single 40 mm infill configuration is the most efficient, exhibiting a higher collision rate compared to the smaller 20 mm infill configuration and a lower pressure drop compared to the two 20 mm infill configurations in the series. Additionally, this study provides insights into the behavior of continuous fluid flow through TPMS infill in view of scaled‐up implementation, including the presence of recirculation zones that can be exploited to further enhance the collision rate of particles.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".