Solid Shape Research Articles

Current advancements in fluid-flow Problems: A brief review

This specific article is a compilation of a few current studies on fluid flow issues. This article provides an overview of current research on the experimental and theoretical analysis of various nanofluids' thermal conductivity. The current study examines a number of variables that have a significant impact on a nanofluid's ability to transfer heat, including temperature, solid volume fraction, type, size, shape, magnetic field, pH, ultrasonic time, and surfactant. In addition, some plausible and appealing theories that enhance the thermal conductivity of nanofluids are mentioned. The final important heat transmission mechanisms that play a significant part in improving thermal conductivity are Brownian motion, thermophoresis, nanoclustering, interfacial nano-layer, and osmophoresis. Thus, consideration is given to the heat transmission properties of nanofluids. And also discussed some Numerical method to solve fluid flow problems.

Viscosity prediction for dense suspensions of non-spherical particles based on CFD-DEM simulations

Reliable estimation of the viscosity and rheology of dense suspensions formed from non-spherical particles is of high importance for studies of many natural and industrial processes. Still, the complexity of underlying physics makes predicting the viscosity of such suspensions a challenging task, resulting in a lack of models capable of doing so for general suspensions. In this work, we present an approach based on a combination of the computational fluid dynamics (CFD) and discrete element method (DEM) developed for arbitrarily shaped particles and use it to predict viscosity of dense suspensions of spheres, rods, and glitters. Simulation results are compared to available experimental data and commonly used engineering correlations. The developed model can reliably predict suspension viscosity in a wide range of solid volume fractions and particle shapes. The simulations with spherical particles also reveal a shear-thickening trend at increased shear rates, which corresponds to the experimentally observed non-Newtonian behavior.

Preparation, optimization, and characterization of genistein-ginseng long-acting polymeric gel as a breast cancer treatment alternative

To address the prevalent genistein (GST) metabolism and inadequate intestinal absorption, an oral long-acting and gastric in-situ gelling gel was designed to encapsulate and localize the intestinal release of the loaded genistein-ginseng (GST-GNS) solid dispersion. Because of the high breast perfusion of GST upon oral absorption, the GST-GNS solid dispersion was developed to enhance GST's dissolution and penetration while offering a synergistic impact against breast cancer (BC). Physiochemical analysis of the GST-GNS solid dispersion, release analysis, gel characterizations, storage stability, penetration, and in vitro cytotoxicity studies were carried out. GST-GNS solid dispersion showed improved dissolution and penetration as compared to raw GST. GST-GNS solid dispersion homogenous shape particles and hydrophilic contacts were revealed by scanning electron microscopy and Fourier Transform-Infrared analysis, respectively. GST-GNS solid dispersion’s diffractogram shows the amorphous character. A second modification involved creating a gastric in-situ gelling system loaded with GST-GNS solid dispersion. This system demonstrated improved GST penetration employing the solid dispersion, as well as the localizing of the GST release at the intestinal media and antitumor synergism against BC. For a better therapeutic approach for BC, the innovative oral GST long-acting gel encasing the GST-GNS solid dispersion would be recommended.Graphical

Synthesis of Spherical Hexagonal Boron Nitride via Precursor Morphology Control

The growing demand for high‐performance semiconductors, driven by the advancement of emerging industries such as artificial intelligence (AI), necessitates the development of novel materials for thermal management. In this respect, hexagonal boron nitride (h‐BN) has emerged as a promising candidate due to its unique properties. However, challenges arise from its two‐dimensional layered structure, resulting in thermal transfer anisotropy and poor fluidity when mixed with polymers for thermal management. To address these challenges, researchers have attempted to fabricate h‐BN into spherical shapes. In this study, a two‐step synthesis method of spherical h‐BN (s‐BN) particles via control of the precursor morphology and a subsequent thermal reaction is proposed. Therefore, as‐fabricated s‐BN exhibits solid spherical shapes with a uniform size distribution, with a median particle size of 0.955 μm. These s‐BN particles, when integrated into epoxy resin, disperse homogeneously, forming efficient heat transfer networks that achieve a 138% improvement in thermal conductivity compared to h‐BN particles with similar diameters, even at lower viscosities. This can overcome the limitations found in the conventional particle shapes while preserving the advantages of h‐BN. Furthermore, it is anticipated that the s‐BN will be applied in thermal management systems, thereby accelerating advancements in electronic technology.

Development and validation of a clinical decision tool for preoperative micropapillary and solid pattern lung adenocarcinoma of CT≤2cm.

Micropapillary (MP) and solid(S) pattern adenocarcinoma are highly malignant subtypes of lung adenocarcinoma. In today's era of increasingly conservative surgery for small lung cancer, effective preoperative identification of these subtypes is greatly important for surgical planning and long term survival of patients. For this retrospective study, the presence of MP and/or S was evaluated in 2167 consecutive patients who underwent surgical resection for clinical stage IA1-2 lung adenocarcinoma. MP and/or S pattern-positive patients and negative-pattern patients were matched at a ratio of 1:3. The Lasso regression model was used for data dimension reduction and imaging signature building. Multivariate logistic regression was used to establish the predictive model, presented as an imaging nomogram. The performance of the nomogram was assessed based on calibration, identification, and clinical usefulness, and internal and external validation of the model was conducted. The proportion of solid components (PSC), Sphericity, entropy, Shape, bronchial honeycomb, nodule shape, sex, and smoking were independent factors in the prediction model of MP and/or S lung adenocarcinoma. The model showed good discrimination with an area under the ROC curve of 0.85. DCA demonstrated that the model could achieve good benefits for patients. RCS analysis suggested a significant increase in the proportion of MP and/or S from 11% to 48% when the PSC value was 68%. Small MP and/or S adenocarcinoma can be effectively identified preoperatively by their typical 3D and 2D imaging features.

증강현실 기반 입체도형 학습도구 시스템

Recently, realistic contents such as virtual reality(VR) and augmented reality(AR) are widely used for education to provide beneficial learning environments with thee-dimensional(3D) information and interactive technology. Specially, AR technology will be helpful to intuitively understand by adding virtual objects registered in the real learning environment with effective ways. In this paper, we developed an AR learning system using 3D spatial information in the 2D based textbook for studying math related to geometry. In order to increase spatial learning effect, we applied to solid shapes such as prisms and pyramids in mathematics education process. Also, it allows participants to use various shapes and expression methods (e.g., wireframe mode) with interaction. We conducted the experiment with our AR system, evaluated achievement and interest. Our experimental study showed positive results, our results are expected to provide effective learning methods in various classes through realistic visualization and interaction methods.

Influence of Biomass-Modified Asphalt Binder on Rutting Resistance

Biomasses are environmentally friendly additives that lower pollution in pavement engineering because of their biodegradability. On the other hand, to build a safe, long-lasting pavement, rutting prevention is crucial. This study provides a comprehensive review of the efficacy of biomass as recyclable materials in reducing rutting and enhancing characteristics of asphalt mixtures. According to findings, the hydrocarbon polymer properties of lignin and biomass ash improve asphalt binder consistency, hardness, and function at high temperatures. The results showed that biochar, due to its solid shape, enhances the stiffness and viscosity of the mixtures. The high-temperature performance of asphalt binder is improved by bioshell waste, which increases rutting parameters. Thus, biomass like ash, lignin, and biochar can increase asphalt binder rheology and rutting resistance due to chemical forces such as Van der Waals and hydrogen ions. The macroscopic and microscopic investigation also shows higher interaction and better adhesion in bioasphalt. However, asphalt binders containing bio-oil exhibited no unique behaviors due to their lubricant impact. Based on the estimation of the life cycle assessment (LCA), it was determined that biomass utilization has the potential to decrease the cost and CO2 emissions of pavement engineering by as much as 10% and more than three times, respectively. An examination of recyclability revealed that biomass utilization can decrease the requirement for additional stabilizers by as much as 20%.

Shape optimization of embedded solids using implicit Vertex-Morphing

One of the biggest challenges in optimizing the shape of complex solids is the requirement to maintain a reasonable mesh quality not only at the boundary but also for the bulk discretization of the interior. Thus, additional regularization and, in many cases, re-meshing of the structure during the iterative process is unavoidable with a Lagrangian description. By tracking the shape update using an Eulerian representation, embedded boundary methods are a promising technique for eliminating mesh distortion problems.This work consistently combines the unique features of implicit Vertex-Morphing and embedded boundary methods, facilitating the node-based shape optimization of solids with industrial complexity. One of the crucial elements for solving the primal problem on a fixed background grid is an efficient and robust quadrature scheme. To this end, we incorporate the open-source C++ framework QuESo (https://github.com/manuelmessmer/QuESo) developed for the numerical integration of arbitrarily complex embedded solids defined by oriented boundary meshes, e.g., in STereoLithography (STL) format. Meanwhile, applying the Helmholtz/Sobolev-based (implicit) filter to the vertices of the embedded boundary mesh not only exploits the extensive design space of node-based optimization but also ensures robust control over the feature size. To realize the above methodology, this work introduces a novel sensitivity analysis that yields mesh-independent shape gradients with respect to the immersed boundary. Through a specific sensitivity weighting, we recover a continuous gradient field from discrete values calculated at the nodes of the background grid. In addition, the presented workflow ensures robust enforcement of challenging geometrical constraints, including minimum wall thicknesses and design space limitations.We critically assess our approach with benchmarks and structures of industrial relevance. In all examples, trivariate B-Spline bases span the background grid, providing highly accurate finite element solutions and shape sensitivities at every iteration. Moreover, the elimination of mesh distortion problems enables a successful termination at the local optimum, even for large shape modifications.

Are Aesthetic Preferences Affected by Gender and Age Differences?

Aesthetic preferences can be crucial for any brand that considers consumer favorites as essential. Form and shape are the most fundamental design elements concerned for all kinds of products and services. Gender and age differences were the main explanatory variables of interest in this study. The perceptions of two forms comprising representation and geometry associated with two contrary shapes involving solid versus airy, were examined. One hundred and eighty respondents participated by using convenience sampling method. Data collection was obtained through an online questionnaire survey and analyzed by ANOVA statistics. The findings suggested that the representational form is recommended for all ages. All genders appreciate it when it combines with solid shape. In addition, the design of geometric form with airy shape is suggested for LGBTQ and younger clients. The implications in this research offer the insight design guidelines for the use of gender and age differences concerned businesses.

Unveiling Acoustic Cavitation Characterization in Opaque Chambers through a Low-Cost Piezoelectric Sensor Approach

This study investigates the characterization of acoustic cavitation in a water-filled, opaque chamber induced by ultrasonic waves at 20 kHz. It examines the effect of different acoustic radiator geometries on cavitation generation across varying electrical power levels. A cost-effective piezoelectric sensor, precisely positioned, quantifies cavitation under assorted power settings. Two acoustic radiator shape configurations, one with holes and another solid, were examined. The piezoelectric sensor demonstrated efficacy, corroborating with existing literature, in measuring acoustic cavitation. This was achieved through the Fast Fourier Transform (FFT) analysis of voltage data, specifically targeting sub-harmonic patterns, thereby providing a robust method for cavitation detection. Results demonstrate that perforated geometries enhance cavitation intensity at lower power levels, while solid shapes predominantly affect cavitation axially, exhibiting decreased activity at minimal power. The findings recommend using two different shape geometries on the acoustic radiator for efficient cavitation detection, highlighting intense cavitation on radial walls and cavitation generation on the bottom. Due to the stochastic nature of cavitation, averaging data is critical. The spatial limitation of the sensor necessitates prioritizing specific areas over complete coverage, with multiple sensors recommended for comprehensive cavitation pattern analysis.

Training flexible spatial-cognitive estimation strategies using augmented reality

We investigated the potential for augmented reality (AR) as a training aid for spatial estimation skills. Though there are many tools to support spatial judgments, from measuring cups to rulers, not much is known about training spatial skills for retention and transfer. Display of AR was manipulated to train the spatial skill of portion estimation. In Experiment 1, an AR-aided strategy of creating smaller portions out of a larger example amount was compared to a no-AR control condition. This manipulation was based on previous non-AR experiments where amorphous foods were better estimated when divided into smaller portions. There was a significant benefit of estimating using a solid AR shape. In Experiment 2, cognitive anchoring was manipulated. Using meaningful AR anchors resulted in the best performance and most learning. We conclude that spatial estimation skills can be combined with mental strategies and trained via AR.

Gas effects on horizontal ribbon growth

Three-phase (solid, melt, and gas) and two-phase (solid and melt) models of horizontal ribbon growth were compared to identify the significance of different gas effects. The boundary conditions at the melt–gas and solid–gas interfaces for two-phase simulations were obtained from decoupled simulations of the gas phase. The results showed that the gas shear stress strongly changes the flow and temperature fields and the position of the triple-phase line. Also, the gas pressure distribution determined the vertical position of the triple-phase line. In the absence of growth angle effects, the results of the two-phase model with specified convective heat transfer coefficient, shear stress, and pressure as boundary conditions along the gas phase interface closely matched that of the three-phase model. Even with non-zero growth angle effects, the two-phase model with all the boundary conditions agreed well with three-phase simulation results, despite increased deviations at higher pull speeds. Finally, the results indicated that gas-induced velocities are significant compared to the Marangoni and buoyancy velocities, which could lead to flow instabilities and the variations in solid shape as observed in HRG experiments.

Millimeter wave-based patient setup verification and motion tracking during radiotherapy.

Position verification and motion monitoring are critical for safe and precise radiotherapy (RT). Existing approaches to these tasks based on visible light or x-ray are suboptimal either because they cannot penetrate obstructions to the patient's skin or introduce additional radiation exposure. The low-cost mmWave radar is an ideal solution for these tasks as it can monitor patient position and motion continuously throughout the treatment delivery. To develop and validate frequency-modulated continuous wave (FMCW) mmWave radars for position verification and motion tracking during RT delivery. A 77GHz FMCW mmWave module was used in this study. Chirp Z Transform-based (CZT) algorithm was developed to process the intermediate frequency (IF) signals. Absolute distances to flat Solid Water slabs and human shape phantoms were measured. The accuracy of absolute distance and relative displacement were evaluated. Without obstruction, mmWave based on the CZT algorithm was able to detect absolute distance within 1mm for a Solid Water slab that simulated the reflectivity of the human body. Through obstructive materials, the mmWave device was able to detect absolute distance within 5mm in the worst case and within 3.5mm in most cases. The CZT algorithm significantly improved the accuracy of absolute distance measurement compared with Fast Fourier Transform (FFT) algorithm and was able to achieve submillimeter displacement accuracy with and without obstructions. The surface-to-skin distance (SSD) measurement accuracy was within 8mm in the anterior of the phantom. With the CZT signal processing algorithm, the mmWave radar is able to measure the absolute distance to a flat surface within 1mm. But the absolute distance measurement to a human shape phantom is as large as 8mm at some angles. Further improvement is necessary to improve the accuracy of SSD measurement to uneven surfaces by the mmWave radar.

Current advancements in fluid-flow Problems: A brief review

This specific article is a compilation of a few current studies on fluid flow issues. This article provides an overview of current research on the experimental and theoretical analysis of various nanofluids' thermal conductivity. The current study examines a number of variables that have a significant impact on a nanofluid's ability to transfer heat, including temperature, solid volume fraction, type, size, shape, magnetic field, pH, ultrasonic time, and surfactant. In addition, some plausible and appealing theories that enhance the thermal conductivity of nanofluids are mentioned. The final important heat transmission mechanisms that play a significant part in improving thermal conductivity are Brownian motion, thermophoresis, nanoclustering, interfacial nano-layer, and osmophoresis. Thus, consideration is given to the heat transmission properties of nanofluids. And also discussed some Numerical method to solve fluid flow problems.

A theoretical model for estimating the thermal conductivity of granular geomaterials with low porosity

The thermal conductivity has an important influence on the geotechnical and geoenvironmental engineering. Most existing thermal conductivity models appear to estimate the thermal conductivity of soils samples with large porosity, few of them were proposed to model the thermal conductivity of soil samples with low porosity. In this study, a theoretical model was proposed to estimate the thermal conductivity of unsaturated granular geomaterials with low porosity. Two parameters (i.e., ζ and β) were introduced. The parameter ζ can be applied to control porosity and solid particle shape, while the parameter β can be used to control water content. Mathematical comparison showed that the proposed thermal conductivity model fell within the Wiener bounds. The new model and five thermal conductivity models were evaluated with experimental data, showing that the new model accurately estimates the relationship between thermal conductivity and saturation for granular geomaterials with low porosity.

Formulation Antiseptics Artisan Soap From Extract Secang Wood (Caesalpinia sappan L.) And Kaffir Lime Leaves (Citrus hystrix DC.)

Solid soap as a body skin cleanser can be used as a souvenir. Kaffir lime leaves and sappanwood are natural ingredients and contains good anti-bacterial compound. This paper aims to explain how to make solid soap with shape of watermelon using thick extracts of kaffir lime leaves and sappanwood with a concentration of 1%. Kaffir lime leaf and sappanwood extracts were each made by soaking 300 g of powder in 500 mL pro analysis ethanol solvent for 48 hours and thickening using a rotary evaporator at a temperature of approximately 50 °C. The soap is made using the cold method (cold process). The research results showed that the soap had a fresh smell, solid shape, soft texture and color resembling the shape of a watermelon. Has a pH of 9.8 ± 0.4 and free fatty acids of 1.39 ± 0.2, according SNI Soap. Artisan soap is also antiseptic and able to inhibit Escherichia coli and Staphylococcus aureus bacteria.

Electrode Material Optimization for Microbial Fuel Cells Using Bamboo Charcoal Powder and Bokuju

Bamboo is a fast-growing plant in Southeast Asia, Africa, and Latin America. Due to its rapid growth, bamboo is considered a problem because it rapidly invades forested areas and alters the original ecosystem. On the other hand, it is regarded as a material that is readily available and very accessible in many countries and has great potential for both ecological and social purposes. Therefore, bamboo was employed as the material for the electrodes of microbial fuel cells in this study. Typically, biochar used for electrodes is chemically activated to remove impurities and increase its surface area. However, chemical treatment of biochar can have a negative impact on the activity of microorganisms. The Bamboo charcoal powder, prepared by heat-treating powdered bamboo for one hour under air at 500°C, contained about 75% carbon and had a porous structure. Therefore, the material could be used as an electrode material for microbial fuel cells without complicated and time-consuming treatment processes. Some of these treatment processes include chemical treatment, and chemically treated biochar may impact the environment. Bamboo charcoal, which does not require this chemical treatment process, is effective as an electrode material for microbial fuel cells. Bokuju, a common kind of drawing ink in Japan and mainly composed of carbon black, was used as a binder for the prepared bamboo charcoal. The reason for using powdered bamboo charcoal with Bokuju is that it is easier to obtain a solid electrode shape by a drying process. We used this electrode in a floating microbial fuel cell and optimized the ratio of Bamboo charcoal powder and Bokuju in the electrode. By evaluating the performance of the microbial fuel cell using the Bamboo charcoal Bokuju electrode, we were able to improve the effectiveness of the electrode material.

Metal Scrap to Hydrogen: Manufacture of Hydroreactive Solid Shapes via Combination of Ball Milling, Cold Pressing, and Spark Plasma Sintering.

Two sorts of tablets were manufactured from ball-milled powder (aluminum scrap and copper) by cold pressing and spark plasma sintering. Their microstructure, phase, and elemental compositions were investigated via scanning electron microscopy, X-ray diffraction analysis, and energy-dispersive X-ray spectroscopy. New phases, Al2Cu and MgCuAl2, were detected in the samples. Their microstructure was formed by welded scrap particles, the intermetallides, and Cu-rich regions located majorly along 'interparticle boundaries' and, to a lesser extent, within small, micro- and nanosized 'intraparticle spots'. The tablets were sealed with adhesive, so only the top surface was exposed to the environment, and tested in a chlorine aqueous solution for hydrogen generation performance. For both sample sorts, hydrogen yields of nearly 100% were achieved. The sintered tablets reacted faster than the cold-pressed ones: at 60, 70, and 80 °C, their entire 'conversion into hydrogen' took ~80, 40, and 30 min. vs. ~220, 100, and 70 min. The experimental kinetic curves were fitted with a contracting geometry equation, and those for the sintered samples were approximated with higher precision. The key effect of the additive was to enhance hydrogen evolution through the galvanic corrosion of Al in the regions adjacent to the intermetallic inclusions and Cu-rich spots.

Quantitative analysis of 3D seam shape according to easing conditions for efficient sewing using muslin

This study quantitatively analyzes the data of 3D seam shapes that alter according to easing conditions. By numerically approaching easing, which is only taught using traditional methods, this study suggests a method of analyzing the changes in 3D surface area, volume, and seam shape. The 3D data of the completed samples were obtained through a 3D scanner, the solid shapes were analyzed using reverse engineering, and a new program was developed. The shape, area, and volume of the data were analyzed, and the deformation rate was measured using the radius of curvature. Linear seam lines were bent because of the mechanical pushing inflicted by the garment with easing. The area increased dramatically as the ease amount increased when the seam lines were short, whereas it was relatively unaffected when seam lines were long. The radii of curvature for curved seam lines show that, for all samples, the waveform is high at the center where the seam is. The peak value did not increase for curved seams when the ease amount increased. The sum of the areas increased with a larger radius of curvature for the curved seams. It is a crucial reference for easing in garments regarding quantitative changes in seam shapes and volumes according to easing type and amount.

Modeling of the Sintered Density in Cu-Al Alloy Using Machine Learning Approaches.

In powder metallurgy materials, sintered density in Cu-Al alloy plays a critical role in detecting mechanical properties. Experimental measurement of this property is costly and time-consuming. In this study, adaptive boosting decision tree, support vector regression, k-nearest neighbors, extreme gradient boosting, and four multilayer perceptron (MLP) models tuned by resilient backpropagation, Levenberg-Marquardt (LM), scaled conjugate gradient, and Bayesian regularization were employed for predicting powder densification through sintering. Yield strength, Young's modulus, volume variation caused by the phase transformation, hardness, liquid volume, liquidus temperature, the solubility ratio among the liquid phase and the solid phase, sintered temperature, solidus temperature, sintered atmosphere, holding time, compaction pressure, particle size, and specific shape factor were regarded as the input parameters of the suggested models. The cross plot, error distribution curve, and cumulative frequency diagram as graphical tools and average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), standard deviation (SD), and coefficient of correlation (R) as the statistical evaluations were utilized to estimate the models' accuracy. All of the developed models were compared with preexisting approaches, and the results exhibited that the developed models in the present work are more precise and valid than the existing ones. The designed MLP-LM model was found to be the most precise approach with AAPRE = 1.292%, APRE = -0.032%, SD = 0.020, RMSE = 0.016, and R = 0.989. Lately, outlier detection was applied performing the leverage technique to detect the suspected data points. The outlier detection discovered that few points are located out of the applicability domain of the proposed MLP-LM model.