Prediction of random packing density and flowability for non-spherical particles by deep convolutional neural networks and Discrete Element Method simulations

Effect of Particle Shape on the Flow of an Hourglass

The flow rate of a granulate out of a cylindrical container is studied as a function of particle shape for flat and elongated ellipsoids experimentally and numerically. We find a nonmonotonic dependence of the flow rate on the grain aspect ratio a/b. Starting from spheres the flow rate grows and has two maxima around the aspect ratios of a/b≈0.6 (lentil-like ellipsoids) and a/b≈1.5 (ricelike ellipsoids) reaching a flow rate increase of about 15% for lentils compared to spheres. For even more anisometric shapes (a/b=0.25 and a/b=4) the flow rate drops. Our results reveal two contributing factors to the nonmonotonic nature of the flow rate: both the packing fraction and the particle velocity through the orifice are nonmonotonic functions of the grain shape. Thus, particles with slightly nonspherical shapes not only form a better packing in the silo but also move faster through the orifice than spheres. We also show that the resistance of the granulate against shearing increases with aspect ratio for both elongated and flat particles; thus change in the effective friction of the granulate due to changing particle shape does not coincide with the trend in the flow rate.

Hydrodynamics and effect of velocity on particle filtration due to bridging in water-saturated porous media using CFD-DEM simulation

Particle bridging owing to the confinement of the pore structure affects the transport and retention of particles in porous media. Particle motion driven by gravities were well investigated, whose filtration is mainly affected by the ratio of the particle diameter to the pore throat size of the medium. However, particles whose motions are driven by the fluid is essential to be investigated for particle separation from the carrying fluid. In this study, the motion of particles was driven by the liquid when passing through a water-saturated porous medium. The fluid-particle flow in a porous medium was modeled using computational fluid dynamics-discrete element method. The motion of particles in the slurry was traced in the porous medium, which enabled particle clogging to be directly precited by the interaction between the particles and pore surfaces by assessing the exact location of each particle. The pressure and flow field of the liquid were investigated, and the variation in flow path owing to particle clogging was predicted. The hydrodynamic study also showed that the Stokes number and particle concentration determined the particle clogging at the pore throats of the porous medium. Increasing the fluid velocity of particles such that the Stokes number was almost equal to 1 increased the separation efficiency of particles. Further increasing the fluid velocity reduced the residence time, which reduced the separation efficiency of the particles.

Calibration and Validation of Flow Parameters of Irregular Gravel Particles Based on the Multi-Response Concept

The discrete element method (DEM) often uses the angle of repose to study the microscopic parameters of particles. This paper proposes a multi-objective optimization method combining realistic modeling of particles and image analysis to calibrate gravel parameters, after obtaining the actual static angle of repose (αAoR_S) and dynamic angle of repose (βAoR_D) of the particles by physical tests. The design variables were obtained by Latin hypercube sampling (LHS), and the radial basis function (RBF) surrogate model was used to establish the relationship between the objective function and the design variables. The optimized design of the non-dominated sorting genetic algorithm II (NSGA-II) with the actual angle of repose measurements was used to optimize the design to obtain the best combination of parameters. Finally, the parameter set was validated by a hollow cylinder test, and the relative error between the validation test and the optimized simulation results was only 3.26%. The validation result indicates that the method can be reliably applied to the calibration process of the flow parameters of irregular gravel particles. The development of solid–liquid two-phase flow and the wear behavior of centrifugal pumps were investigated using the parameter set. The results show that the increase in cumulative tangential contact forces inside the volute of centrifugal pumps makes it the component most likely to develop wear behavior. The results also illustrate the significant meaning of the accurate application of the discrete element method for improving the efficient production of industrial scenarios.

Emerging Artificial Intelligence (AI) Technologies Used in the Development of Solid Dosage Forms

Artificial Intelligence (AI)-based formulation development is a promising approach for facilitating the drug product development process. AI is a versatile tool that contains multiple algorithms that can be applied in various circumstances. Solid dosage forms, represented by tablets, capsules, powder, granules, etc., are among the most widely used administration methods. During the product development process, multiple factors including critical material attributes (CMAs) and processing parameters can affect product properties, such as dissolution rates, physical and chemical stabilities, particle size distribution, and the aerosol performance of the dry powder. However, the conventional trial-and-error approach for product development is inefficient, laborious, and time-consuming. AI has been recently recognized as an emerging and cutting-edge tool for pharmaceutical formulation development which has gained much attention. This review provides the following insights: (1) a general introduction of AI in the pharmaceutical sciences and principal guidance from the regulatory agencies, (2) approaches to generating a database for solid dosage formulations, (3) insight on data preparation and processing, (4) a brief introduction to and comparisons of AI algorithms, and (5) information on applications and case studies of AI as applied to solid dosage forms. In addition, the powerful technique known as deep learning-based image analytics will be discussed along with its pharmaceutical applications. By applying emerging AI technology, scientists and researchers can better understand and predict the properties of drug formulations to facilitate more efficient drug product development processes.

Discrete Element Simulation Study of the Accumulation Characteristics for Rice Seeds with Different Moisture Content

To study the accumulation characteristics of rice seeds with different moisture content, an accurate model of rice seeds was established by 3D scanning technology. The accumulation state of rice seeds by the “point source” accumulation method was analyzed by proportioning and measuring the simulation parameters with different moisture content. The accumulation process was simulated at 10.23%, 14.09%, 17.85%, 21.77%, 26.41% and 29.22% moisture content, respectively. The velocity and force state of the seeds were visually analyzed by using the accumulation process with a moisture content of 29.22%. The accumulation process was divided into four stages according to the velocity characteristics of the seeds. The average force and kinetic energy of the rice seeds outside the cylinder were obtained, and the average force of the rice seeds outside the cylinder was proved to be the direct cause of the velocity change during the accumulation process. The mechanical characteristics of rice seeds in the quasi-static accumulation stage were partitioned and systematically analyzed. The force distribution of the “central depression” structure of rice seeds with a moisture content of 10.23%, 14.09% and 17.85% on the horizontal surface appeared. The higher the moisture content of rice seeds, the more likely the typical “circular” force structure appeared, and the more uniformly the force on the horizontal surface was distributed in the circumference direction.