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18 August 2022 Inhalation selected mesh and the chosen solver and sub-model set-up. It also can allow variations on the model (e.g., altered geometry) to be run with confidence. Validation data for aerosols and sprays can include high-speed images of a plume emerging from a device nozzle. Comparing these (with correct scal- ing) side-by-side with CFD simulation results is a good start for monitoring the shape and evolution of the aerosol. Quantitative data, such as local particle size distribution, particle velocity and flow velocity measurements, are invaluable in validating an aero- sol simulation. Particle data can be measured using laser diffraction or phase Doppler methods and flow velocity from particle image velocimetry (PIV) meth- ods. Comparison with analytical particle size distri- bution measurements taken with an impactor can be useful, provided the actual flow geometry before the impactor is modeled in the simulation. Design optimization e final potential step in the CFD workflow is the incorporation of the simulation within an optimi- zation process, which is typically available in pro- prietary CFD packages. ese optimizations seek to either: • optimize the design of a product/device within some constraints to a set of goals; • select the best set of sub-model options to repre- sent some experimental validation data as closely as possible. Recommendations for critical evaluation of CFD studies In analyzing CFD studies, look for quantitative claims of either cell size or number of cells, with evi- dence of refinement in areas of high velocity gradi- ent. Ideally, there should be evidence or claim of a mesh-dependence study. With Lagrangian particle tracking, look for quantitative data on the total num- ber of parcels tracked (and a parcel number depen- dence study would be optimal). Both dependency studies are conducted in reference 19. In regions of dense aerosol, the cells should be small enough so that the volume fraction is not unrealistically large, i.e., that the droplets inside the parcels in those cells do not have a packing volume larger than the cell itself. is could result in excessive (i.e., non-physi- cal) transfer of force or energy between the Lagrang- ian and Eulerian phases in these cells in a coupled simulation and would likely skew the predictions. In addition, consider whether the code used has been validated for a similar problem, in the study under scrutiny or in works referenced. Consider too the val- ues of fluid system properties and non-dimensional numbers, i.e., the ranges of validity of the model used and whether the current study falls within those. Conclusions and next steps for deploying CFD is article has shown some of the capabilities of industrial CFD for pharmaceutical aerosol applica- tions, from the perspective of implementing CFD modeling within a product development process. e fundamental workings of the standard finite vol- ume method with Lagrangian particle tracking have been explained in context, with the requirements and suggested processes for successful modeling. Looking to the future, those with relevant "CFD skills" may be attracted to careers in the pharma- ceutical sector and development of these skills are typically core to most mechanical, aerospace and biomedical engineering graduates. Knowledge of and experience with CFD sub-model selection, such as turbulence, atomization and particle/surface interac- tion, as well as programming ability and understand- ing of relevant validation data, is often acquired at the postgraduate/PhD level or via equivalent inten- sive R&D. Industry access to more advanced CFD packages, bespoke capabilities and personnel with training and experience can come through partner- ships with research organizations and universities, which often retain their own high-performance com- puting facilities. References 1. Gavtash B, Jacques C, Versteeg H, Myatt B: Com- putational fluid dynamics simulation of cavitating propellant flow inside a pressurised metered dose inhaler expansion chamber, using volume of fluid method. In Drug Delivery to the Lungs 2020. e Aerosol Society. Bristol, UK: 2020:31. 2. De Backer J, Vos W, Vinchurkar S, Claes R, et al.: Validation of computational fluid dynamics in CT-based airway models with SPECT/CT. Radiol- ogy 2010, 257:854-862. 3. United Nations Environment Program: e Kigali Amendment to the Montreal Protocol: HFC Phase- down. OzonAction Fact sheet OZFS/16/11_1, UNEP, Paris, France: 2016. 4. Smith AV, Johns RJR, McNamara PM: Improving the process of engine design through the integrated application of CAE methods. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 1994, 208:259-267. 5. Uzoka C, Mishra R: Integration of TRIZ and CFD to new product development process. Inter- national Journal of Computational Fluid Dynamics 2020, 34(6):418-437. 6. Versteeg HK, Hargrave G, Harrington L, et al.: e use of computational fluid dynamics (CFD) to predict pMDI air flows and aerosol plume formation. In Respiratory Drug Delivery VII 2000, 1:257-264.