Journal of Aerosol Science最新文献

Penetration of droplets in fully-developed turbulent pipe flows (vertical configuration) was studied numerically. Two Reynolds numbers ($R{e}_D$ = 37,700 and 11,700) based on the pipe diameter were used in the simulations. Statistics used in the single-phase flow characterization (mean velocities, root mean square fluctuation velocities, and turbulence dissipation rate) were obtained from the law of the wall relationships in addition to curve-fitting from direct numerical simulation (DNS) data found in the literature. The droplet phase was simulated using a one-way coupling Lagrangian random-walk eddy interaction model (EIM). Monodispersed droplets, ranging from 1.78 to $26.83\mu m$, were released separately in the pipe-flow computational domain. A modified eddy lifetime, based on local turbulent Reynolds numbers ($R{e}_{{\lambda }_T}$) and velocity fluctuations perpendicular to the walls, is proposed. Simulation results of droplet penetration show relatively good agreement against experimental data obtained from the literature.

Insights into the effect of temperature (T) and relative humidity (RH) as well as structure and polarisation on ion mobility help the comparison and interpretation of mobility and mass-based data. We measured alkylammonium ions in air under different T (14 °C, 24 °C, 34 °C and 41 °C) and RH (0 %, 20 %, 40 %) conditions using two individual setups (in both cases a planar differential mobility analyser coupled with a time-of-flight mass spectrometer) and the results are in excellent agreement. Mobility increases with rising T and decreases with water vapour loading. When separating the measurement mobility by structures, clear mass dependence was observed. The measured mobilities exhibited large deviations from theoretically calculated results in dry conditions, which are possibly caused by adduct formation on the monomer ions via clustering (or reactions). This phenomenon seems to be unavoidably associated with light ions under atmospheric pressures, which is worth further exploration and bearing in mind when comparing measurements to calculations. Both methanol and oxygen (occasionally nitrogen or alkyl chain elongation) are possible candidates of the adduct. Under spherical assumption, we used the modified Mason–Schamp's approximation to link the measured mobility to the mobility equivalent diameter. The drag enhancement factor $\xi$ and the effective gas-molecule collision diameter $d_g$ derived from our measurement data are comparable to literature values. Our data also exposed a non-linear dependence on the polarisation parameter ${\epsilon }^{*}$. Polarisation, $\xi$ and $d_g$ were parameterised using linear models against ion structures, T, and RH for primary, secondary and tertiary alkylammonium ions with identical alkyl groups. Our model parametrisations predict mobilities within ±10 % deviation from the measured data. The model also has satisfying predicting power for alkylammonium ions with unidentical alkyl structures.

The impact dynamics of micro-sized particles is still a fascinating topic of research, which can contribute to the understanding of the mechanisms of adhesion and rebound of these particles. A high-precision measurement system was developed that combines hardware, software, and numerical simulation methods to determine the impact dynamics parameters of micro-sized single particles under high temperature conditions by using Particle Shadow Velocimetry (PSV). The impact dynamic parameters of three standard particles (impact velocity, impact angle, coefficient of restitution, critical sticking velocity, etc.) were measured as examples to validate the reliability of this measurement system. The results demonstrated the reliability of the developed particle image processing algorithm. Corrections on the impact velocity and rebound velocity of small particles at low impact velocities were necessary. Particle size had a significant influence on the variation characteristics of the normal coefficient of restitution in particle impacts. A power function relationship between the critical sticking velocity and particle size was obtained. This paper provides a strong technical guidance for the future research on the impact dynamics of fly ash particles produced in the boiler field or other related fields.

This study presents a numerical approach for simulating the internal morphology of carbon black particles formed in a pyrolysis reactor. The simulation process involves a two-step approach using population balance models (PBM) and detailed population balance models (DPBM), solved via sectional and stochastic methods, to simulate the arrangement of primary particles within aggregates to allow determination of their fractal dimension (FD). The outcome is a novel introduction of simulating real flow reactors that for the first time provides the fractal dimension of particles as an output, rather than an assumed input. The results of this study have practical implications for optimizing the synthesis processes in carbon black production. The effects of various production parameters, including aggregation efficiency, temperature, pressure, and acetylene concentrations, on the fractal dimension values of carbon black particles are examined. It is observed that higher temperatures lead to the formation of larger fractal shapes with lower fractal dimensions and larger primary particle diameters. Moreover, increased reactor pressure and higher aggregation efficiency enhance the formation of carbon black aggregates, but also have a time-based effect with higher compactness at longer residence times. The time-based effect reveals the importance of sintering, where high loads of small particles enhance the overall sintering of the aggregates. These findings provide insights into the interplay between temperature, pressure, and particle morphology, highlighting the dynamic nature of carbon black nanoparticles and their response to synthesis process conditions.

Computational studies of micron particle deposition is used to predict inhaled particles through the nasal cavity for understanding drug delivery or toxicology risks. To ensure reliable results in future studies, this study evaluated particle tracking schemes and determined the most appropriate settings for predicting micron particle deposition in a pipe bend and nasal cavity geometry. Micron particles were injected into a fully developed 90° pipe bend under a turbulent flow regime with Reynolds number, $Re$ = 10,000, for comparison with existing data in the literature. Similarly, the micron particles were released into a more complex geometry, a human nasal cavity.

The study found that although the high-resolution tracking is the default and preferred option set out by Ansys-Fluent, the $5\mu m$ particle tested travelled further than when the high-resolution tracking was off. This was rectified by setting the wall nodal velocity to zero. All particle tracking schemes performed well and were suitable for predicting deposition for particle diameters $>5\mu m$, with high-resolution tracking and setting the wall nodal velocity to zero. However, the results become sensitive to the particle scheme when dealing with particle diameters ¡ $5\mu m$. The lower-order schemes overpredict deposition, while the higher-order schemes have zero deposition in the pipe bend unless the wall nodal velocity is set to zero. This study provides a list of recommended settings to best simulate particle deposition efficiency in Ansys Fluent (version 2023R2), although future releases of the CFD software may incorporate these settings as default options.

Counterflow diffusion flame is an attractive platform for fundamental research on kinetics of soot formation. Accurate determination of soot volume fraction in the flame is a prerequisite for in-depth analysis of the sooting characteristics and assessment of predictive soot models. Light extinction has been proven to be an efficient technique for measuring soot volume fraction thanks to its non-intrusiveness and its simple optical setup. Nevertheless, tomographic inversion needs to be performed if spatially resolved soot volume fraction is to be obtained from the measured light extinction data which is essentially a projection along the line-of-sight. In this regard, radial distribution of soot volume fraction would affect the accuracy of the measurement through its influences on the inversion processes. In this work, we show that the curtain flow, which is necessary to avoid the formation of the undesired secondary diffusion flame and to keep the core counterflow from ambient disturbances, has notable effects on spatially resolved soot volume fraction measurements with line-of-sight measurements. In particular, different flow rate settings of the curtain flow can result in different soot distributions at the edges of soot fields: upwards curved, outwards extended, and downwards curved, which may influence the measurement of centerline soot volume fraction distribution. The necessity of tomographic inversion, the minimal region of the projection image necessary for tomographic inversion (when necessary), the quasi-one-dimensional feature of soot distribution, and the sensitivity of measurement to slight flame asymmetry were investigated where possible to determine the most suitable curtain flow configuration for soot volume fraction measurements by light extinction. Recommendations on curtain flow setting are finally made.

To assess if an aggregated or an agglomerated material has to be considered as a nano-material, the size distribution of its constituent primary particles needs to be measured and the median diameter determined. To this end, the reference method uses either transmission or scanning electron microscopy to obtain images of the sample. The size of a significant number, usually a few hundreds, of primary particles are then measured manually. This task is highly time-consuming and subjected to operator bias. Some attempts have been made to automatize the size measurements. The algorithms and software available are generally successful at segmenting images of spherical objects with no or partial overlap but fail to properly segment irregular objects with strong overlap.

The advances made with deep learning algorithms are promising to solve the segmentation issues encountered so far on complicated samples. In this paper, we tested the open source deep learning Cellpose software on transmission and scanning electron microscope images of different samples to retrieve the median diameter of the primary particles and compare the results with both the manual and theoretical values. This software was chosen for its ease of use, its free availability and the fact that it is pre-trained, allowing the use of a limited set of training images.

For the samples used in this study, the quality of the segmentation was highly dependent on the number of objects on which the software model was trained, but a number of 500 to 1000 objects was enough to obtain good performances. The diameters measured using Cellpose segmentation are most of the time in agreement within 10% with the manual values. Interestingly, for scanning electron microscopy data, the results obtained with Cellpose are closer to the theoretical values when compared to the measurements obtained by hand, implying a smaller operator bias. If an uncertainty assessment still needs to be investigated for the diameters determined using Cellpose, this first attempt to use this software to segment electron microscope images of diverse samples is very promising and opens the possibility to fully automatize the identification of nano-structured materials.

Airborne particle transport in indoor environments plays an important role in occupant exposure to aerosols and public health problems. Several studies have examined indoor airflow and particle transport using computational fluid dynamics models. For the Lagrangian particle tracking model, the minimum particle concentration necessary for accurate prediction may vary with the airflow regime and sampling volume. Nonetheless, only a few studies have systematically quantified suitable particle numbers and sampling volumes, according to indoor airflow and ventilation conditions. This study addresses this gap by exploring quality control strategies for a Lagrangian particle tracking model to reliably predict indoor particle transport. Based on transient simulations, we analyzed the spatiotemporal distributions of indoor particle trajectories while varying the number of particles, sampling volume, and ventilation strategy. The results indicate that in general a sampling volume of 5 L can predict the normalized mean concentrations better than a 1 L sampling volume, particularly when dealing with a smaller number of particles. Furthermore, the required particle number concentrations vary significantly depending on the chosen ventilation strategy. For instance, under the conditions of a 5 L sampling volume and an air exchange rate of 2.7 h⁻¹, the minimum particle number concentrations for achieving reliable modeling predictions were observed to be 0.0075 cm⁻³ for displacement ventilation and 0.015 cm⁻³ for mixing ventilation. These results highlight the crucial role of the number of simulated particle trajectories in Lagrangian particle tracking models in determining prediction quality. The study findings suggest that quality control measures should acknowledge the significant variability in required particle numbers, which can often differ by an order of magnitude, contingent upon the specific combination of ventilation strategy and sampling volume.