Deposition of polydisperse particles representing nasal spray application in a human nasal cavity was performed under transient breathing profiles of sniffing, constant flow, and breath hold. The LES turbulence model was used to describe the fluid phase. Particles were introduced into the flow field with initial spray conditions, including spray cone angle, insertion angle, and initial velocity. Since nasal spray atomizer design determines the particle conditions, fifteen particle size distributions were used, each defined by a log-normal distribution with a different volume mean diameter ( Dv 50). Particle deposition in the anterior region was approximately 80% when Dv 50 > 50μm, and this decreased to 45% as Dv 50 decreased to 10μ m for constant and sniff breathing conditions. The decrease in anterior deposition was countered with increased deposition in the middle and posterior regions. The significance of increased deposition in the middle region for drug delivery shows there is potential for nasal delivered drugs to reach the highly vascularised mucosal walls in the main nasal passages. For multiple targeted deposition sites, an optimisation equation was introduced where deposition results of any two targeted sites could be combined and a weighting between 0 to 1 was applied to each targeted site, representing the relative importance of each deposition site.
While solid mechanics codes are now conventional tools both in industry and research, the increasingly more exigent requirements of both sectors are fuelling the need for more computational power and more advanced algorithms. For obvious reasons, commercial codes are lagging behind academic codes often dedicated either to the implementation of one new technique, or the upscaling of current conventional codes to tackle massively large scale computational problems. Only in a few cases, both approaches have been followed simultaneously. In this article, a solid mechanics simulation strategy for parallel supercomputers based on a hybrid approach is presented. Hybrid parallelization exploits the thread-level parallelism of multicore architectures, com- bining MPI tasks with OpenMP threads. This paper describes the proposed strategy, programmed in Alya, a parallel multiphysics code. Hybrid parallelization is specially well suited for the current trend of supercomputers, namely large clusters of multicores. The strategy is assessed through transient non-linear solid mechanics problems, both for explicit and implicit schemes, running on thousands of cores. In order to demonstrate the flexibility of the proposed strategy under advance algorithmic evolution of computational mechanics, a non-local parallel overset meshes method (Chimera-like) is implemented and the conservation of the scalability is demonstrated.
We present a Computational Fluid Dynamics (CFD) modeling strategy for onshore wind farms aimed at predicting and opti- mizing the production of farms using a CFD model that includes meteorological data assimilation, complex terrain and wind turbine effects. The model involves the solution of the Reynolds-Averaged Navier-Stokes (RANS) equations together with a k-ɛ turbulence model specially designed for the Atmospheric Boundary Layer (ABL). The model involves automatic meshing and generation of boundary conditions with atmospheric boundary layer shape for the entering wind flow. As the integration of the model up to the ground surface is still not viable for complex terrains, a specific law of the wall including roughness effects is implemented. The wake effects and the aerodynamic behavior of the wind turbines are described using the actuator disk model, upon which a volumetric force is included in the momentum equations. The placement of the wind turbines and a mesh refinement for the near wakes is done by means of a Chimera method. The model is implemented in Alya, a High Performance Computing (HPC) multi physics parallel solver based on finite elements and developed at Barcelona Supercomputing Center.The research of G. Houzeaux is being partly done under a I3 contract with the Spanish Ministerio de Ciencia e Inovación. The work of B. Eguzkitza is financed by a scholarship from the Fundación IBERDROLA supporting\ud the project ”Optimization of wind farms using computational fluid dynamics”.Peer ReviewedPostprint (published version
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.