Mapping of Beta Distribution for the Study of Dispersed Materials

In the study of polydisperse materials, most of the experimental particle size distributions were obtained on bounded intervals. In these cases, it is also desirable to use bounded models with different shapes to simulate the results of studying polydisperse and powder materials. The beta distribution is often used to approximate results due to the fact that this distribution contains many forms for displaying realizations on a limited interval. With the development of computer technology, there has been an increased interest in the use of beta distribution in the modern practice of analyzing results. Meanwhile, there remains a limitation in the use of the beta distribution that is associated with the choice of distribution shape. The possibilities of using known shape measures for mapping beta distribution in this paper is discusses. On the example of the space of shape measure of kurtosis and skewness, the limited use of only probabilistic measures of shapes is illustrated. It is proposed to use the entropy coefficients as an additional informational parameter of the beta distribution shape. On the base of a features comparison of the entropy coefficients for biased and unbiased beta distributions, recommendations for their application are given. By using the example of beta distributions mapping in the space of asymmetry and the entropy coefficient, it is shown that the synergistic combination of probabilistic and informational measures of the shape allows expanding the possibilities of estimating the shape parameters beta distributions. Two methods to display the positions of realizations of beta distributions is proposed. There are trajectories on a constant ratio of shape and realizations position curve on equal values of one parameter. In particular, the features of the choice of beta distributions with negative skewness are discussed.

Interpretation of Construction Procedure and Physicochemical Characteristics for Soil Layers from Sowangneung (Small Royal Tomb) of Ssangneung (Twin Tombs) in Iksan, Korea

The Iksan Ssangneung (twin tombs), a pair of tombs comprising the Daewangneung (large royal tomb) and the Sowangneung (small royal tomb), were constructed in the typical style of stone tunnel and chamber tombs in the Baekje Kingdom during the Sabi period (538 to 660 AD) of ancient Korea. Soil layers exposed during excavation of Sowangneung in a trench east of the tomb are: the bottommost layer, the ground level layer, the Panchuk (rammed earth) layer of the Baekje, the layer created by a grave robbery, and soil recovered during the Japanese colonial period. Soil samples were obtained by segmenting an easy stratigraphic horizon into sub categorized soil layers, and their material properties were analyzed; they are composed mainly of sandy loam based on the particle size distributions. In the site foundation, loamy sand is packed in the bottommost layer, and sandy loam with high sand and silty sand fills most of the overlying layer. The central and topmost portion of the Baekje layer is composed of loam with high clay content. All soil layers show geochemical behaviors similar to those of the bottommost layer. X-ray diffraction analysis verified kaolinite in all layers, also observed in soil layers displaying high crystallinity. Kaolinite and halloysite were identified by scanning electron microscopy. Thus, we conclude that the Baekje layer of the Sowangneung is composed of sandy loam containing kaolin procured from near the site. An impermeable middle to upper layer was created using viscous loam. The top of the tomb was closed tightly.

Predicting the Particle Size Distributions of Spherical Particle Sets from Synthetic Images: A Comparison of 9 Classic Image Features

The two objectives of this paper were to demonstrate use the of the discrete element method for generating synthetic images of spherical particle configurations, and to compare the performance of 9 classic feature extraction methods for predicting the particle size distributions (PSD) from these images. The discrete element code YADE was used to generate synthetic images of granular materials to build the dataset. Nine feature extraction methods were compared: Haralick features, Histograms of Oriented Gradients, Entropy, Local Binary Patterns, Local Configuration Pattern, Complete Local Binary Patterns, the Fast Fourier transform, Gabor filters, and Discrete Haar Wavelets. The feature extraction methods were used to generate the inputs of neural networks to predict the PSD. The results show that feature extraction methods can predict the percentage passing with a root-mean-square error (RMSE) on the percentage passing as low as 1.7%. CLBP showed the best result for all particle sizes with a RMSE of 3.8 %. Better RMSE were obtained for the finest sieve (2.1%) compared to coarsest sieve (5.2%).

Measurement report: Vertical profiling of particle size distributions over Lhasa, Tibet: Tethered balloon-based in-situ measurements and source apportionment

In-situ measurements of vertically resolved particle size distributions based on a tethered balloon system were carried out for the first time in the highland city of Lhasa over the Tibetan Plateau in summer 2020, using portable optical counters for the size range of 0.124~32 μm. The vertical structure of 112 aerosol profiles was found to be largely shaped by the evolution of the boundary layer (BL), with a nearly uniform distribution of aerosols within the daytime mixing layer and a sharp decline with the height in the shallow nocturnal boundary layer. During the campaign, the average mass concentration of particulate matters smaller than 2.5 μm in aerodynamic diameter (PM2.5) within the BL was around 3 μg m−3, almost four times of the amount in the free troposphere (FT), which was rarely affected by surface anthropogenic emissions. Though there was a lower level of particle mass in the residual layer (RL) than in the BL, a similarity in particle mass size distributions (PMSDs) suggested that particles in the RL might be of the same origin as particles in the BL. This was also in consistence with the source apportionment analysis based on the PMSDs. Three distinct modes were observed in the PMSDs for the BL and the RL. One mode was exclusively coarse particles up to roughly 15 μm and peaked around 5 μm. More than 50 % of total particle mass was often contributed by coarse mode particles in this area, which was thought to be associated with local dust resuspension. The mode peaking over 0.5~0.7 μm was representative of biomass burning on religious holidays and was found to be most pronounced on holiday mornings. The contribution from the religious burning factor rose from about 25 % on non-holidays to nearly 50 % on holiday mornings. The mode dominated by particles smaller than 0.3 μm was thought to be associated with combustion related emissions and/or secondary aerosol formation. In the FT coarse mode particles only accounted for less than 10 % of the total mass and particles larger than 5 μm were negligible. The predominant submicron particles in the FT might be related to secondary aerosol formation and the aging of existed particles. To give a full picture of aerosol physical and chemical properties and better understand the origin and impacts of aerosols in this area, intensive field campaigns involving measurements of vertically resolved aerosol chemical compositions in different seasons would be much encouraged in the future.

Evaluation of convective cloud microphysics in numerical weather predictionmodel with dual-wavelength polarimetric radar observations

<p>The representation of microphysical processes in numerical weather prediction models remains a main source of uncertainty until today. To evaluate the influence of cloud microphysics parameterizations on numerical weather prediction, a convection permitting regional weather model setup has been established using 5 different microphysics schemes of varying complexity (double-moment, spectral bin, particle property prediction (P3)). A polarimetric radar forward operator (CR-SIM) has been applied to simulate radar signals consistent with the simulated particles. The performance of the microphysics schemes is analyzed through a statistical comparison of the simulated radar signals to radar measurements on a dataset of 30 convection days.</p> <p>The observational data basis is provided by two polarimetric research radar systems in the area of Munich, Germany, at C- and Ka-band frequencies and a complementary third polarimetric C-band radar operated by the German Weather Service. By measuring at two different frequencies, the<br />dual-wavelength ratio is derived that facilitates the investigation of the particle size evolution. Polarimetric radars provide in-cloud information about hydrometeor type and asphericity by measuring, e.g., the differential reflectivity ZDR.</p> <p>Within the DFG Priority Programme 2115 PROM, we compare the simulated polarimetric and dual-wavelength radar signals with radar observations of convective clouds. Deviations are found between the schemes and observations in ice and liquid phase, related to the treatment of particle size distributions. Apart from the P3 scheme, simulated reflectivities in the ice phase are too high. Dual-wavelength signatures demonstrate issues of most schemes to correctly represent ice particle size distributions. Comparison of polarimetric radar signatures reveal issues of all schemes except the spectral bin scheme to correctly represent rain particle size distributions. The polarimetric information is further exploited by applying a hydrometeor classification algorithm to obtain dominant hydrometeor classes. By comparing the simulated and observed distribution of hydrometeors, as well as the frequency, intensity and area of high impact weather situations (e.g., hail or heavy convective precipitation), the influence of cloud microphysics on the ability to correctly predict high impact weather situations is examined.</p>

Use of machine learning for unraveling hidden correlations between particle size distributions and the mechanical behavior of granular materials

A data-driven framework was used to predict the macroscopic mechanical behavior of dense packings of polydisperse granular materials. The discrete element method, DEM, was used to generate 92,378 sphere packings that covered many different kinds of particle size distributions, PSD, lying within 2 particle sizes. These packings were subjected to triaxial compression and the corresponding stress–strain curves were fitted to Duncan–Chang hyperbolic models. An artificial neural network (NN) scheme was able to anticipate the value of the model parameters for all these PSDs, with an accuracy similar to the precision of the experiment and even when the NN was trained with a few hundred DEM simulations. The estimations were indeed more accurate than those given by multiple linear regressions (MLR) between the model parameters and common geotechnical and statistical descriptors derived from the PSD. This was achieved in spite of the presence of noise in the training data. Although the results of this massive simulation are limited to specific systems, ways of packing and testing conditions, the NN revealed the existence of hidden correlations between PSD of the macroscopic mechanical behavior.