Optimizing Valved Holding Chambers for Enhanced Inhalation Therapy: Integrating Dakota and Ansys CFX in the Bezier-Curved Geometry Design Process
Bibliographic record
Abstract
Abstract In this paper, we introduce a globally efficient optimization method for enhancing the design of valved holding chambers (VHCs), integral to inhalation therapy. Our approach harnesses the capabilities of the Dakota optimization software, integrated seamlessly with Ansys CFX for advanced modeling and simulation. We specifically focus on crafting a VHC body shape with Bezier-curved geometry, aiming to significantly improve the capture and formation of aerosol particles. This innovative design achieves an optimal balance between capturing larger particles and promoting the generation of finer particles, crucial for effective drug delivery. The cornerstone of our method is the strategic use of Dakota’s robust optimization algorithms, which guide the design towards achieving globally optimal solutions. In parallel, Ansys CFX provides detailed insights into the multiphase flow dynamics and particle breakup processes, essential for a thorough understanding of aerosol behavior in VHCs. The synergistic combination of Dakota and Ansys CFX enables a thorough exploration of the design space, leading to a highly optimized VHC design. Our study focuses on the classification and mass flow of microparticles from pressurized metered dose inhalers (pMDI). We employ a combination of computational simulations and experimental validations to assess the performance of our optimized VHC design compared to existing commercial models. The results clearly indicate the superior efficiency of our design in delivering fine microparticles. This study not only demonstrates the effectiveness of integrating Dakota and Ansys CFX in the optimization of medical devices but also represents a significant advancement in aerosol drug delivery technology. Our findings offer a new perspective on VHC design, promising enhanced pulmonary drug delivery through a globally efficient optimization process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".