Demistify: a large-eddy simulation (LES) and single-column model (SCM) intercomparison of radiation fog

An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the Local and Non-local Fog Experiment (LANFEX) field campaign. Seven of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst three are research-grade SCMs designed for fog simulation, and the LESs are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number concentration (CDNC) conditions. The main SCM bias appears to be toward the overdevelopment of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parameterisation, as it is to the underlying aerosol or CDNC.

Quantitative Analysis of Droplet Size Distribution in Plant Protection Spray Based on Machine Learning Method

Spray droplet size is the main factor affecting the deposition uniformity on a target crop. Studying the influence of multiple factors on the droplet size distribution as well as the evaluation method is of great significance for improving the utilization of pesticides. In this paper, volume median diameter (VMD) and relative span (RS) were selected to evaluate the droplet size distribution under different hollow cone nozzles, flow rates and spatial positions, and the quantitative models of VMD and RS were established based on machine learning methods. The results showed that support vector regression (SVR) had excellent results for VMD (Rc = 0.9974, Rp = 0.9929), while multi-layer perceptron (MLP) had the best effect for RS (Rc = 0.9504, Rp = 0.9537). The correlation coefficient of the prediction set is higher than 0.95, showing the excellent ability of machine learning on predicting the droplet size distribution. In addition, the visualization images of the droplet size distribution were obtained based on the optimal models, which provided intuitive guidance for realizing the uniform distribution of pesticide deposition. In conclusion, this study provides a novel and feasible method for quantitative evaluation of droplet size distribution and offers a theoretical basis for further determining appropriate operation parameters according to the optimal droplet size.

Impact of cloud base turbulence on CCN activation: Single size CCN

This paper examines the impact of cloud-base turbulence on activation of cloud condensation nuclei (CCN). Following our previous studies, we contrast activation within a non-turbulent adiabatic parcel and an adiabatic parcel filled with turbulence. The latter is simulated by applying a forced implicit large eddy simulation within a triply periodic computational domain of 643 m3. We consider two monodisperse CCN. Small CCN have a dry radius of 0.01 micron and a corresponding activation (critical) radius and critical supersaturation of 0.6 micron and 1.3%, respectively. Large CCN have a dry radius of 0.2 micron and feature activation radius of 5.4 micron and critical supersaturation 0.15 %. CCN are assumed in 200 cm−3 concentration in all cases. Mean cloud base updraft velocities of 0.33, 1, and 3 m s−1 are considered. In the non-turbulent parcel, all CCN are activated and lead to a monodisperse droplet size distribution above the cloud base, with practically the same droplet size in all simulations. In contrast, turbulence can lead to activation of only a fraction of all CCN with a non-zero spectral width above the cloud base, of the order of 1 micron, especially in the case of small CCN and weak mean cloud base ascent. We compare our results to studies of the turbulent single-size CCN activation in the Pi chamber. Sensitivity simulations that apply a smaller turbulence intensity, smaller computational domain, and modified initial conditions document the impact of specific modeling assumptions. The simulations call for a more realistic high-resolution modeling of turbulent cloud base activation.

Lubrication and Sensory Properties of Emulsion Systems and Effects of Droplet Size Distribution

The functional and sensory properties of food emulsion are thought to be complicated and influenced by many factors, such as the emulsifier, oil/fat mass fraction, and size of oil/fat droplets. In addition, the perceived texture of food emulsion during oral processing is mainly dominated by its rheological and tribological responses. This study investigated the effect of droplet size distribution as well as the content of oil droplets on the lubrication and sensory properties of o/w emulsion systems. Friction curves for reconstituted milk samples (composition: skimmed milk and milk cream) and Casein sodium salt (hereinafter referred to as CSS) stabilized model emulsions (olive oil as oil phase) were obtained using a soft texture analyzer tribometer with a three ball-on-disc setup combined with a soft surfaces (PDMS) tribology system. Sensory discrimination was conducted by 22 participants using an intensity scoring method. Stribeck curve analyses showed that, for reconstituted milk samples with similar rheological properties, increasing the volume fraction of oil/fat droplets in the size range of 1–10 µm will significantly enhance lubrication, while for CSS-stabilized emulsions, the size effect of oil/fat droplets reduced to around 1 µm. Surprisingly, once the size of oil/fat droplets of both systems reached nano size (d90 = 0.3 µm), increasing the oil/fat content gave no further enhancement, and the friction coefficient showed no significant difference (p > 0.05). Results from sensory analysis show that consumers are capable of discriminating emulsions, which vary in oil/fat droplet size and in oil/fat content (p < 0.01). However, it appeared that the discrimination capability of the panelist was significantly reduced for emulsions containing nano-sized droplets.

A Promising Cutaneous Leishmaniasis Treatment with a Nanoemulsion-Based Cream with a Generic Pentavalent Antimony (Ulamina) as the Active Ingredient

Leishmania parasites are the etiological agents of Leishmaniasis, a tropical disease that affects around 15 million people in about 90 countries. The chosen therapy for this disease is based on antimony V compounds, such as meglumine antimoniate. It can be administered as a parenteral, subcutaneous or perilesional form as successive infiltrations with pre-established doses localized in the border of the granuloma that characterizes the wound of Cutaneous Leishmaniasis (CL). Herein, a topical pharmaceutical recipe, such as an emulsion, is proposed to eliminate the trauma caused by administering the medicine in parenteral form to the face or other difficult access zones. The evaluation of this vehicle was performed by analyzing parameters such as pH, viscosity, homogeneity and droplet size distribution. Furthermore, the effectiveness of the emulsion was proved by in vitro experiments using Strat-M synthetic membranes, showing that the transdermal passage of the antimonial complex is guaranteed. Moreover, complete healing of the wound has been attained in patients with CL, as shown with two clinical cases in this article.

Investigation of Droplet Size Distribution for Vibrating Mesh Atomizer

Vibrating mesh atomizers (VMA) are increasing in demand for various aerosol applications due to their ability to generate uniformly sized droplets. Currently there are two types of VMA (commercial metallic membranes and silicon based). High Uniformity and control of small droplet size are the basic requirements for many aerosol applications, for which ultrasonic or VMA are employed. However, there is limited research on understanding the droplet size distribution of different types of atomizers. In this study three aerosol generators were investigated: Ultrasonic, metallic VMA, and MEMS-based silicon VMA. The primary objective was to compare these devices on droplet size distribution and mechanism of action. A systematic study to compare the performance of the two VMA was investigated based on droplet distribution, volumetric median diameter (VMD) using liquids with different physiochemical properties. Size distribution of the droplet produced by the metallic VMA was twice the span compared to silicon VMA for fluids with viscosity <2cP. The metallic VMA also resulted in an increase in VMD as the viscosity increased, whereas the Si VMA did not see a significant increase in VMD. The silicon-based VMA demonstrated a 4-15x increase in fine particle fraction control compared to metallic VMA. The results demonstrate that silicon based VMA has narrower droplet distribution with more uniform droplet size and lower span compared to metallic VMA.

Insights into spray impingement on mask surface: effect of mask properties on penetration and aerosolization of cough droplets

The emergence of the COVID 19 pandemic has demonstrated the importance of face masks, making them a part of the routine during the pandemic which is still continuing. The face masks act as source control, reducing the transmission of infectious respiratory droplets by acting as a physical barrier blocking the droplets during speaking, breathing, coughing, sneezing, etc. The novelty of current study is to replicate the droplet size distribution and velocity scale similar to an actual cough or a mild sneeze and conduct a fundamental study to investigate the effects of mask properties on model-cough impingement. The spray replicates the presence of both large-sized and small-sized droplets similar to an actual cough, which makes the observations relevant to real-life situations. The spray is impinged on different mask samples with varying properties like porosity, pore size, fabric thickness, and their combinations in multilayer configuration. The effect of mask properties on the droplet penetration volume is studied as it leads to the release of higher pathogen loading into the surroundings. A two step penetration criteria based on viscous dissipation and capillary effects have been applied along with a third criteria based on the porosity of the mask sample that is specifically applicable for the spray impingement. The droplets present in the impinging cough can penetrate through the mask, atomizing into the aerosolization range and thus increasing the infection potential. Hence the effect of mask properties on the droplet size distribution as well as the velocity distribution of the penetrated droplets has been investigated, which will be essential for estimating the range of infection spread. The filtration of virus-emulating nanoparticles as well as the fate of the penetrated respiratory droplets, with a susceptible person in the proximity, has also been investigated.