Computational fluid dynamics simulation of aerosol transport and deposition

Tang, Yingjie; Guo, Bing

doi:10.1007/s11783-011-0365-8

Cited by 17 publications

(6 citation statements)

References 102 publications

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“…Here, internal processes commonly refer to particle coagulation, condensation and gas-toparticle conversion, while external processes include transport, diffusion, deposition, and particle motion induced by different forces, such as electrostatic force and thermophoresis force. Due to the fact that internal processes, including coagulation and gas-to-particle conversion, are not easily included in the kinetic calculation for tracing particles, and there are also too many particles which need to be tracked, the Eulerian-Lagrangian approach is not recommended in workplaces [62]. Based on the mean-field theory, both internal and external processes can be described by a population balance equation, also known as the general dynamic equation.…”

Section: General Remarks On Cfd Modelling Of Nanoparticle Aerosolsmentioning

confidence: 99%

From Source to Dose

Seipenbusch

Asbach

et al. 2014

Handbook of Nanosafety

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Section: General Remarks On Cfd Modelling Of Nanoparticle Aerosolsmentioning

confidence: 99%

From Source to Dose

Seipenbusch

Asbach

et al. 2014

Handbook of Nanosafety

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“…DNS can numerically solve the Navier-Stokes equation for all significant spatial and temporal scales, while they do not require any additional turbulence modeling. However, the computational cost increases with Reynolds number as Re 3 [80].…”

Section: Introductionmentioning

confidence: 99%

“…In CFD models, particle transport and deposition could be described via the Lagrangian or Eulerian approaches. The Lagrangian and Eulerian approaches were deeply discussed in the review papers of Rostami et al (2009) and Tang and Guo (2011) [4,80]. The Lagrangian approach is suitable for larger particles (> 0.3 µm) [4].…”

Section: Introductionmentioning

confidence: 99%

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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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“…Zur Stabilisierung der Nanosuspension werden z. B. Tenside zugegeben [1,2], die in der Lebensmittel-und Arzneimittelindustrie weit verbreitet sind [3,4] [10] und die Agglomeration sowohl in der Gasphase als auch in der Flüssigphase über erhöhte Kollisionsraten verstärken [11,12]. Andererseits wird auch berichtet, dass turbulente Scherströmungen zu einer Fragmentierung vorhandener Agglome-rate führen können [13].…”

Section: Introductionunclassified

Stabilization of Nanoparticles in Bubble Columns – Influence of Foam Layer Height and pH Value

Weber

Pieper

Koch

2011

Chemie Ingenieur Technik

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RESSAS) durch Verwendung tensidhaltiger Lösungen schonend stabilisiert werden. Um den Einfluss relevanter Systemgrößen, wie Schaumschichthöhe und pH-Wert, auf die Abscheide-und Stabilisierungseffizienz zu ermitteln, wurde eine Modellblasensäule eingesetzt. Es zeigte sich, dass bereits bei geringen Bauhöhen die Kombination von Transportprozessen in den aufsteigenden Blasen und in der Schaumschicht zur effizienten Abscheidung von Nanopartikeln führt, die durch die zugegebenen Tenside gut stabilisiert werden. Der pH-Wert seinerseits beeinflusst die Eigenschaften des Schaums und darüber auch die Abscheideeffizienz.Organic drug nanoparticles may be stabilized in nanosupensions by Rapid Expansion of Supercritical Solutions into Aqueous Solutions (RESSAS) using surfactant solutions. A model bubble column was used in the present work in order to study the influence of relevant system parameters, such as foam height and pH value, on the deposition and stabilization efficiency. It turned out that, already at low heights, the combination of deposition processes in the rising bubbles and in the foam results in high separation efficiency for nanoparticles, which are well stabilized by the surfactants. The pH value influences the foam properties and consequently the separation efficiency. Abbildung 4. Zeitliche Entwicklung der C-Konzentration in derFlüssigkeitssäule für verschiedene pH-Werte. Eingezeichnet sind auch der Beginn und das Ende der Partikelzugabe, bei dem nur der Funkengenerator ausgeschaltet wurde, der N 2 -Gasstrom aber weiterlief.Abbildung 5. Entwicklung des Mediandurchmessers der stabilisierten Partikeln in Flüssigkeit (gefüllte Symbole) und Schaum (offene Symbole) für verschiedene pH-Werte für eine Aufsatzhöhe von 10 cm. Eingezeichnet sind auch das Ende der Partikelzugabe sowie die Größe der Ausgangspartikel (Aerosol) und der Primärpartikeln.

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Computational fluid dynamics simulation of aerosol transport and deposition

Cited by 17 publications

References 102 publications

From Source to Dose

From Source to Dose

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

Stabilization of Nanoparticles in Bubble Columns – Influence of Foam Layer Height and pH Value

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