The use of computational fluid dynamics in inhaler design
Bibliographic record
Abstract
INTRODUCTION: Computational fluid dynamics (CFD) has recently seen increased use in the design of pharmaceutical inhalers. The use of CFD in the design of inhalers is made difficult by the complex nature of aerosol generation. At present, CFD has provided valuable insight into certain aspects of inhaler performance, though limitations in computational power have prevented the full implementation of numerical methods in the design of inhalers. AREAS COVERED: This review examines the application of CFD in the design of aerosol drug delivery technologies with a focus on pressurized metered-dose inhalers (pMDI), nebulizers and dry powder inhalers (DPIs). Challenges associated with the application of CFD in inhaler design are discussed along with relevant investigations in the literature. Discussions of discrete element modeling (DEM) and the simulation of pharmaceutical aerosol dispersion are included. EXPERT OPINION: The extreme complexity of coupled fluid and aerosol dynamics associated with aerosol generation has somewhat limited the use of CFD in inhaler design. Combined CFD--DEM simulations provide a useful tool in the design of DPIs, though aerosol generation in pMDIs and nebulizers has eluded CFD modeling. The most beneficial use of CFD typically occurs when concurrent CFD and experimental analyses are performed, significantly enhancing the knowledge provided by experiment alone.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".