Literature DB >> 30349146

Recommendations for Simulating Microparticle Deposition at Conditions Similar to the Upper Airways with Two-Equation Turbulence Models.

Karl Bass1, P Worth Longest1,2.   

Abstract

The development of a CFD model, from initial geometry to experimentally validated result with engineering insight, can be a time-consuming process that often requires several iterations of meshing and solver set-up. Applying a set of guidelines in the early stages can help to streamline the process and improve consistency between different models. The objective of this study was to determine both mesh and CFD solution parameters that enable the accurate simulation of microparticle deposition under flow conditions consistent with the upper respiratory airways including turbulent flow. A 90° bend geometry was used as a characteristic model that occurs throughout the airways and for which high-quality experimental aerosol deposition data is available in the transitional and turbulent flow regimes. Four meshes with varying degrees of near-wall resolution were compared, and key solver settings were applied to determine the parameters that minimize sensitivity to the near-wall (NW) mesh. The Low Reynolds number (LRN) k-ω model was used to resolve the turbulence field, which is a numerically efficient two-equation turbulence model, but has recently been considered overly simplistic. Some recent studies have used more complex turbulence models, such as Large Eddy Simulation (LES), to overcome the perceived weaknesses of two-equation models. Therefore, the secondary objective was to determine whether the more computationally efficient LRN k-ω model was capable of providing deposition results that were comparable to LES. Results show how NW mesh sensitivity is reduced through application of the Green-Gauss Node-based gradient discretization scheme and physically realistic near-wall corrections. Using the newly recommended meshing parameters and solution guidelines gives an excellent match to experimental data. Furthermore, deposition data from the LRN k-ω model compares favorably with LES results for the same characteristic geometry. In summary, this study provides a set of meshing and solution guidelines for simulating aerosol deposition in transitional and turbulent flows found in the upper respiratory airways using the numerically efficient LRN k-ω approach.

Entities:  

Keywords:  CFD modeling; Large eddy simulation (LES); Low Reynolds number (LRN) turbulence model; Reynolds-averaged Navier Stokes (RANS) equations; aerosol deposition; best practices; meshing guidelines; solution guidelines

Year:  2018        PMID: 30349146      PMCID: PMC6195318          DOI: 10.1016/j.jaerosci.2018.02.007

Source DB:  PubMed          Journal:  J Aerosol Sci        ISSN: 0021-8502            Impact factor:   3.433


  27 in total

Review 1.  Numerical modeling of pulsatile turbulent flow in stenotic vessels.

Authors:  Sonu S Varghese; Steven H Frankel
Journal:  J Biomech Eng       Date:  2003-08       Impact factor: 2.097

2.  Comparing MDI and DPI aerosol deposition using in vitro experiments and a new stochastic individual path (SIP) model of the conducting airways.

Authors:  P Worth Longest; Geng Tian; Ross L Walenga; Michael Hindle
Journal:  Pharm Res       Date:  2012-06       Impact factor: 4.200

3.  Effects of mesh style and grid convergence on particle deposition in bifurcating airway models with comparisons to experimental data.

Authors:  P Worth Longest; Samir Vinchurkar
Journal:  Med Eng Phys       Date:  2006-06-30       Impact factor: 2.242

4.  Characteristics of the turbulent laryngeal jet and its effect on airflow in the human intra-thoracic airways.

Authors:  Ching-Long Lin; Merryn H Tawhai; Geoffrey McLennan; Eric A Hoffman
Journal:  Respir Physiol Neurobiol       Date:  2007-02-14       Impact factor: 1.931

5.  Evaluation of the Respimat Soft Mist Inhaler using a concurrent CFD and in vitro approach.

Authors:  P Worth Longest; Michael Hindle
Journal:  J Aerosol Med Pulm Drug Deliv       Date:  2009-06       Impact factor: 2.849

6.  Validating Whole-Airway CFD Predictions of DPI Aerosol Deposition at Multiple Flow Rates.

Authors:  P Worth Longest; Geng Tian; Navvab Khajeh-Hosseini-Dalasm; Michael Hindle
Journal:  J Aerosol Med Pulm Drug Deliv       Date:  2016-04-15       Impact factor: 2.849

7.  Velocity measurements in steady flow through axisymmetric stenoses at moderate Reynolds numbers.

Authors:  S A Ahmed; D P Giddens
Journal:  J Biomech       Date:  1983       Impact factor: 2.712

8.  Pulsatile poststenotic flow studies with laser Doppler anemometry.

Authors:  S A Ahmed; D P Giddens
Journal:  J Biomech       Date:  1984       Impact factor: 2.712

9.  Validating CFD Predictions of Pharmaceutical Aerosol Deposition with In Vivo Data.

Authors:  Geng Tian; Michael Hindle; Sau Lee; P Worth Longest
Journal:  Pharm Res       Date:  2015-05-06       Impact factor: 4.200

10.  Current Inhalers Deliver Very Small Doses to the Lower Tracheobronchial Airways: Assessment of Healthy and Constricted Lungs.

Authors:  Ross L Walenga; P Worth Longest
Journal:  J Pharm Sci       Date:  2016-01-13       Impact factor: 3.534

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  11 in total

1.  Use of Computational Fluid Dynamics (CFD) Dispersion Parameters in the Development of a New DPI Actuated with Low Air Volumes.

Authors:  Worth Longest; Dale Farkas; Karl Bass; Michael Hindle
Journal:  Pharm Res       Date:  2019-05-28       Impact factor: 4.200

2.  Development of a New Inhaler for High-Efficiency Dispersion of Spray-Dried Powders Using Computational Fluid Dynamics (CFD) Modeling.

Authors:  Worth Longest; Dale Farkas
Journal:  AAPS J       Date:  2019-02-07       Impact factor: 4.009

3.  High-Efficiency Nose-to-Lung Aerosol Delivery in an Infant: Development of a Validated Computational Fluid Dynamics Method.

Authors:  Karl Bass; Susan Boc; Michael Hindle; Kelley Dodson; Worth Longest
Journal:  J Aerosol Med Pulm Drug Deliv       Date:  2018-12-15       Impact factor: 2.849

Review 4.  Use of computational fluid dynamics deposition modeling in respiratory drug delivery.

Authors:  P Worth Longest; Karl Bass; Rabijit Dutta; Vijaya Rani; Morgan L Thomas; Ahmad El-Achwah; Michael Hindle
Journal:  Expert Opin Drug Deliv       Date:  2018-12-10       Impact factor: 6.648

5.  Characterizing the Effects of Nasal Prong Interfaces on Aerosol Deposition in a Preterm Infant Nasal Model.

Authors:  Karl Bass; Mohammad A M Momin; Connor Howe; Ghali Aladwani; Sarah Strickler; Arun V Kolanjiyil; Michael Hindle; Robert M DiBlasi; Worth Longest
Journal:  AAPS PharmSciTech       Date:  2022-04-19       Impact factor: 3.246

6.  Computational Fluid Dynamics (CFD) Guided Spray Drying Recommendations for Improved Aerosol Performance of a Small-Particle Antibiotic Formulation.

Authors:  Worth Longest; Amr Hassan; Dale Farkas; Michael Hindle
Journal:  Pharm Res       Date:  2022-02-11       Impact factor: 4.200

7.  Optimizing Aerosolization Using Computational Fluid Dynamics in a Pediatric Air-Jet Dry Powder Inhaler.

Authors:  Karl Bass; Dale Farkas; Worth Longest
Journal:  AAPS PharmSciTech       Date:  2019-11-01       Impact factor: 3.246

8.  High-Efficiency Dry Powder Aerosol Delivery to Children: Review and Application of New Technologies.

Authors:  Karl Bass; Dale Farkas; Amr Hassan; Serena Bonasera; Michael Hindle; P Worth Longest
Journal:  J Aerosol Sci       Date:  2020-10-14       Impact factor: 3.433

9.  Computational Fluid Dynamics (CFD) Simulations of Spray Drying: Linking Drying Parameters with Experimental Aerosolization Performance.

Authors:  P Worth Longest; Dale Farkas; Amr Hassan; Michael Hindle
Journal:  Pharm Res       Date:  2020-05-21       Impact factor: 4.200

Review 10.  Computational fluid dynamics modelling of human upper airway: A review.

Authors:  W M Faizal; N N N Ghazali; C Y Khor; Irfan Anjum Badruddin; M Z Zainon; Aznijar Ahmad Yazid; Norliza Binti Ibrahim; Roziana Mohd Razi
Journal:  Comput Methods Programs Biomed       Date:  2020-06-26       Impact factor: 5.428

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