Efficient Parallel Geometry Distribution for the Simulation of Complex Flows

Lintermann, Andreas

doi:10.7712/100016.1885.5067

Cited by 6 publications

(1 citation statement)

References 30 publications

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“…However, the results from the low-Reynolds number (LRN) k-ω and the shear-stress transport (SST) are similar to those of the LES method [81]. Though LES and DNS are more computationally expensive than RANS, the time for the simulation of aerosol deposition by LES and DNS could be reduced due to the availability of high-performance computing [82,83].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Aerosol Deposition in the Human Respiratory Tract via Computational Models: A Review with Recent Updates

et al. 2020

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The measurement of deposited aerosol particles in the respiratory tract via in vivo and in vitro approaches is difficult due to those approaches' many limitations. In order to overcome these obstacles, different computational models have been developed to predict the deposition of aerosol particles inside the lung. Recently, some remarkable models have been developed based on conventional semi-empirical models, one-dimensional whole-lung models, three-dimensional computational fluid dynamics models, and artificial neural networks for the prediction of aerosol-particle deposition with a high accuracy relative to experimental data. However, these models still have some disadvantages that should be overcome shortly. In this paper, we take a closer look at the current research trends as well as the future directions of this research area.Atmosphere 2020, 11, 137 2 of 27 PM Cd , and PM Pb ), the annual limit values are 6, 5, and 500 ng/m 3 , respectively (Directives 1999/30/EC and 2008/50/EC) [9]. Inhaled drug therapy can be a useful option against many lung diseases such as asthma or COPD. The efficiency of pharmaceutical aerosols depends on their deposition in the airways, especially the upper generations [10]. The estimation of aerosol transportation and deposition in the human lung can therefore indicate strategies for the prevention or alleviation of health effects from inhaled toxic particles or new drug-aerosol delivery approaches that can overcome the limitations of inhalers [11]. Atmospheric-aerosol deposition in the human respiratory tract (RT) can be directly measured by monitoring and comparing inhaled and exhaled particle concentrations. However, due to experimental limitations, the regional dose in the respiratory system is hard to measure experimentally but can be predicted by the application of different mathematical models. Suitable mathematical models are those that show a reasonable correlation between results obtained from modeling and empirical measurements [12]. Computational models generally consist of three elements: (1) a mathematical formulation that describes the relevant physical and chemical processes, (2) the setting of specific initial and boundary conditions, and (3) the solving of equations for the specified geometry. The accuracy of a computational model depends on the extent to which these elements are realistic. Additionally, regardless of the computational model that is applied, four types of input data are required: (1) lung morphometry, (2) breathing pattern, (3) particle properties, and (4) gas/vapor properties (Figure 1) [4]. The effects of these input data to the computational models were introduced in the review of Rostami (2009) and Hussain et al. (2011) [4,13]. In this review, we only present the advances of lung geometry models due to their strong relations to the accuracies of the computational models [4,13].

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Section: Introductionmentioning

confidence: 99%