Modeling the Settling Velocity of a Sphere in Newtonian and Non-Newtonian Fluids with Machine-Learning Algorithms

Assessing and Predicting Physical Stability of Emulsion-Based Topical Semisolid Products: A Review

The emulsion-based topical semisolid dosage forms present a high degree of complexity due to their microstructures which is apparent from their compositions comprising at least two immiscible liquid phases, often times of high viscosity. These complex microstructures are thermodynamically unstable, and the physical stability of such preparations is governed by formulation parameters such as phase volume ratio, type of emulsifiers and their concentration, HLB value of the emulsifier, as well as by process parameters such as homogenizer speed, time, temperature etc. Therefore, a detailed understanding of the microstructure in the DP and critical factors that influence the stability of emulsions is essential to ensure the quality and shelf-life of emulsion-based topical semisolid products. This review aims to provide an overview of the main strategies used to stabilize pharmaceutical emulsions contained in semisolid products and various characterization techniques and tools that have been utilized so far to evaluate their long-term stability. Accelerated physical stability assessment using dispersion analyzer tools such as an analytical centrifuge to predict the product shelf-life has been discussed. In addition, mathematical modeling for phase separation rate for non-Newtonian systems like semisolid emulsion products has also been discussed to guide formulation scientists to predict a priori stability of these products.

New Model for Non-Spherical Particles Drag Coefficients in Non-Newtonian Fluid

The settlement drag coefficient of non-spherical particles (SDCNPs) is a crucial parameter in the field of petroleum engineering. Accurately predicting the SDCNPs in the fluid is essential to the selection and design of proppant and hydraulic design in the fracturing scheme. Although many models for anticipating the SDCNPs have been proposed, none of them can be adopted for non-Newtonian fluid (NNF) and Newtonian fluid (NF). In the investigation, the SDCNPs in NF and NNF are studied experimentally, and the anticipation mode of the settlement drag coefficient of spherical particles (SDCSPs) in different fluids (including Newton, Herschel-Bulkley and power law) is proposed. On this basis, the shape depiction parameter circularity is introduced to develop the SDCNPs. The results exhibit that the predicted values of the SDCNPs model perfectly align with the experimental values, and the average relative errors are 5.70%, 6.24% and 6.72%, respectively. The mode can accurately describe the settlement behavior of non-spherical particles (NSPs) and provide a basis for the application of NSPs in petroleum engineering.

Hindered Settling of Fiber Particles in Viscous Fluids

In the current literature, information can mainly be found about free and hindered settling of isometric particles in Newtonian and non-Newtonian fluids. These conclusions cannot be used to describe the sedimentation of non-isometric particle in non-Newtonian fluids. For this reason, we have carried out systematic experiments and calculated the correlation of the hindered settling velocity of a cloud of non-isometric particles in high-viscosity and pseudoplastic liquid. The experiments were performed in transparent model fluids, namely, glycerine (a Newtonian fluid) and an aqueous solution of carboxylmethylcelulose CMC (a non-Newtonian pseudo-plastic liquid). These fluids have similar rheological properties, for example, the fresh fine-grained cementitious composites HPC/UHPC. The experiments were carried out with steel fibers with a ratio of d/l = 0.3/20. The settling velocity was determined for fiber volumes from 1% to 5%. While it is known from previous studies that for spherical particles the hindered settling velocity is proportional to the porosity of a suspension cloud on exponent 4.8, which was confirmed by our verification experiment, for the studied fiber particles it is proportional to the porosity on exponent 22.1. This great increase in the exponent is an effect of both the shape of the particles and, in particular, a mutual influence that arises from their interweaving and connection in the suspension.

From Static to Dynamic: A Review on the Role of Mucus Heterogeneity in Particle and Microbial Transport

Mucus layers (McLs) are on the front line of the human defense system that protect us from foreign abiotic/biotic particles (e.g., airborne virus SARS-CoV-2) and lubricates our organs. Recently, the impact of McLs on human health (e.g., nutrient absorption and drug delivery) and diseases (e.g., infections and cancers) has been studied extensively, yet their mechanisms are still not fully understood due to their high variety among organs and individuals. We characterize these variances as the heterogeneity of McLs, which lies in the thickness, composition, and physiology, making the systematic research on the roles of McLs in human health and diseases very challenging. To advance mucosal organoids and develop effective drug delivery systems, a comprehensive understanding of McLs' heterogeneity and how it impacts mucus physiology is urgently needed. When the role of airway mucus in the penetration and transmission of coronavirus (CoV) is considered, this understanding may also enable a better explanation and prediction of the CoV's behavior. Hence, in this Review, we summarize the variances of McLs among organs, health conditions, and experimental settings as well as recent advances in experimental measurements, data analysis, and model development for simulations.

The Development of Explicit Equations for Estimating Settling Velocity Based on Artificial Neural Networks Procedure

This study proposes seven equations to predict the settling velocity of sediment particles with variations in grain size (d), particle shape factor (SF), and water temperature (T) based on the artificial neural network procedure. The data used to develop the equations were obtained from digitizing charts provided by the U.S. Interagency Committee on Water Resources (U.S-ICWR) and compiled from the measurement data of settling velocity from several sources. The equations are compared to three existing equations available in the literature and then analyzed using graphical and statistical analysis. The simulation results show the proposed equations produce satisfactory results. The proposed equations can predict the settling velocity of natural particle sediments, with diameters ranging between 0.05 mm and 10 mm in water with temperatures between 0 °C and 40 °C, and shape factor SF ranging between 0.5 and 0.95.

Application of Gamma Attenuation Technique and Artificial Intelligence to Detect Scale Thickness in Pipelines in Which Two-Phase Flows with Different Flow Regimes and Void Fractions Exist

Scale deposits can reduce equipment efficiency in the oil and petrochemical industry. The gamma attenuation technique can be used as a non-invasive effective tool for detecting scale deposits in petroleum pipelines. The goal of this study is to propose a dual-energy gamma attenuation method with radial basis function neural network (RBFNN) to determine scale thickness in petroleum pipelines in which two-phase flows with different symmetrical flow regimes and void fractions exist. The detection system consists of a dual-energy gamma source, with Ba-133 and Cs-137 radioisotopes and two 2.54-cm × 2.54-cm sodium iodide (NaI) detectors to record photons. The first detector related to transmitted photons, and the second one to scattered photons. The transmission detector recorded two signals, which were the counts under photopeak of Ba-133 and Cs-137 with the energy of 356 keV and 662 keV, respectively. The one signal recorded in the scattering detector, total counts, was applied to RBFNN as the inputs, and scale thickness was assigned as the output.