Numerical Study Research Articles

Effect of Thermal Convection and Mass Transfer on Particle Motion during Sedimentation: A Numerical Study

Effect of Thermal Convection and Mass Transfer on Particle Motion during Sedimentation: A Numerical Study

Development of an efficient vehicle-to-grid method for massive electric vehicle aggregation

The growing adoption of renewable energy and electric vehicles (EVs) has contributed to environmental sustainability; nevertheless, integration of these products into the power grid has become complex owing to their unpredictable nature and variable energy demands. A significant challenge lies in the realization of large-scale, coordinated control of EVs to serve as an alternative to traditional energy storage systems. This challenge is underscored by the complexity of optimization in large-scale cooperative control problems and the difficulty in reducing such problems to an easily manageable and practical level in real-world application. In response to these challenges, a practical mechanism for the integration of EVs into a vehicle-to-grid concept is proposed in this study. In this approach, the constraints involved in merging multiple EVs into a fictitious clustered energy storage unit, which are often neglected, are given renewed focus. An iterative multi-stage optimization method is introduced that includes an EV aggregation clustering model, multi-tier optimization model, and recursive framework. Here, marked efficacy for larger EV fleets is demonstrated for this method, providing optimal charging and discharging schedules for each vehicle while a high degree of precision is maintained. With the proposed technique, validated through numerous case studies using historical data, the global optimum solution is largely approximated, with a marginal deviation of 4 %. In addition, robustness of the model is demonstrated under varying pricing scenarios, with a computation time that increases in a linear manner, rather than exponentially, as the number of EVs increases. Meanwhile, compared with conventional methods, the technique proposed in this study has the capacity to fulfill the charging demands of all users with reduced charging expense, demonstrating the high precision and efficacy of the technique.

A Numerical Study of Integrated Linear Reconstruction for Steady Euler Equations Based on Finite Volume Scheme

A Numerical Study of Integrated Linear Reconstruction for Steady Euler Equations Based on Finite Volume Scheme

Numerical study on the effect of temperature rise of humeral bone nails in magnetic resonance imaging based on the finite-element method.

Humeral fracture is a common long bone fracture in orthopedic clinical diagnosis and treatment. To investigate the local temperature increase owing to changes in the specific absorption ratio (SAR) of the human body caused by humeral bone nails during magnetic resonance imaging (MRI). A refined geometric model of the upper body was constructed via data segmentation and post-processing using the digital human image dataset. Finally, the geometric model was imported into COMSOL, a 3-T magnetic resonance coil was built, and the operating frequency (128 MHz) was set to analyze the SAR of the bone-nail pair and temperature changes. The analysis of the changes after bone-nail implantation under different tissue conditions revealed that the SAR and temperature after implantation and fixation were three times higher than those before, and the areas with abrupt changes in SAR and temperature were primarily concentrated in the bone-nail area. In MRI, metal implants can cause local elevation of the SAR near the implant in the human body, resulting in a temperature increase around the implant. Consequently, long-term scanning can damage the human body.

Identifying Oxidative Stress-Related Genes (OSRGs) as Potential Target for Treating Periodontitis Based on Bioinformatics Analysis.

Periodontitis (PD) is a multifactorial inflammatory disease that is closely associated with periodontopathic bacteria. Numerous studies have demonstrated oxidative stress (OS) contributes to inflammation and is a prime factor in the development of PD. It is imperative to explore the function of newly discovered hub genes associated with OS in the advancement of PD, thereby identifying potential targets for therapeutic intervention. The goal of the current study was to identify the oxidative -stress-related genes (OSRGs) associated with periodontitis (PD) development using an integrated bioinformatics method. DEGs from GEO gene-expression data were identified using the "limma" package. We obtained OSRGs from GeneCards and utilized a Venn diagram to uncover differentially expressed OSRGs (DEOSRGs). After receiving the DEOSRGs, we employed Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) analytical tools to examine their possible functions and pathways in PD. Receiver operating characteristic (ROC) curves screened for hub genes of PD. RT-qPCR and western blot analysis were used to detect DEOSRG expression in mouse ligature-induced periodontitis gingival tissues. The investigation identified 273OSRGs. Based on PPI analysis, we recognized 20 OSRGs as hub genes. GO and KEGG enrichment analysis indicated that these hub genes were predominantly enriched in leukocyte migration, lymphocyte proliferation, and humoral immune response, and associated with leukocyte trans-endothelial migration, cytokine-cytokine receptor interaction, and NF-κB signaling pathway. Following ROC analysis, VCAM1, ITGAM, FCGR3A, IL1A, PECAM1, and VCAM1were identified as PD prognostic gene. RT-qPCR and western blot analyses confirmed that the expression ITGAM, FCGR3A, and PECAM1 were significantly elevated in the gingival tissues obtained from mice. This investigation revealed that ITGAM, FCGR3A, and PECAM1 may have a crucial function in the advancement of PD.

Numerical Study on Fault Current Characteristics of Self-Shielded HTS DC Cables Made of REBCO Tapes in Parallel With Stainless Steel Tapes

Numerical Study on Fault Current Characteristics of Self-Shielded HTS DC Cables Made of REBCO Tapes in Parallel With Stainless Steel Tapes

Experimental and Numerical Study on the Seismic Response of a Surface Frame Structure–Soil–Double-Tunnel System

Experimental and Numerical Study on the Seismic Response of a Surface Frame Structure–Soil–Double-Tunnel System

Modulation of the Immune System Mechanisms using Probiotic Bacteria in Allergic Diseases: Focus on Allergic Retinitis and Food Allergies.

Allergic illnesses occur when an organism's immune system is excessively responsive to certain antigens, such as those that are presented in the environment. Some people suffer from a wide range of immune system-related illnesses including allergic rhinitis, asthma, food allergies, hay fever, and even anaphylaxis. Immunotherapy and medications are frequently used to treat allergic disorders. The use of probiotics in bacteriotherapy has lately gained interest. Probiotics are essential to human health by modulating the gut microbiota in some ways. Due to probiotics' immunomodulatory properties present in the gut microbiota of all animals, including humans, these bacterial strains can prevent a wide variety of allergic disorders. Probiotic treatment helps allergy patients by decreasing inflammatory cytokines and enhancing intestinal permeability, which is important in the battle against allergy. By altering the balance of Th1 and Th2 immune responses in the intestinal mucosa, probiotics can heal allergic disorders. Numerous studies have shown a correlation between probiotics and a reduced risk of allergy disorders. A wide range of allergic disorders, including atopic dermatitis, asthma, allergic retinitis and food allergies has been proven to benefit from probiotic bacteria. Therefore, the use of probiotics in the treatment of allergic diseases offers a promising perspective. Considering that probiotic intervention in the treatment of diseases is a relatively new field of study, more studies in this regard seem necessary.

Numerical Study on Physical Reservoir Computing With Josephson Junctions

Numerical Study on Physical Reservoir Computing With Josephson Junctions

Decomposition by Approximation With Pulse Waves Allowing Further Research on Sources of Voltage Fluctuations

Voltage fluctuations are common disturbances in power grids. Initially, it is necessary to selectively identify individual sources of voltage fluctuations to take actions to minimize the effects of voltage fluctuations. Selective identification of disturbing loads is possible using a signal chain consisting of demodulation, decomposition, and assessment of the propagation of component signals. The accuracy of such an approach is closely related to the applied decomposition method. The paper presents a new method for decomposition by approximation with pulse waves. The proposed method allows for a correct identification of selected parameters, that is, the frequency of changes in the operating state of individual sources of voltage fluctuations and the amplitude of voltage changes caused by them. The article presents results from numerical simulation studies and laboratory experimental studies, based on which the estimation errors of the indicated parameters were determined by the proposed decomposition method and other empirical decomposition methods available in the literature. The real states that occur in power grids were recreated in the research. The metrological interpretation of the results obtained from the numerical simulation and experimental research is discussed.

Numerical Simulation Study of the Optimal Parameters for Dust Reduction by an External Air Curtain (EAC) in Continuous Mining Tunnel

Numerical Simulation Study of the Optimal Parameters for Dust Reduction by an External Air Curtain (EAC) in Continuous Mining Tunnel

Performance Optimization and Numerical Simulation Study of Sludge Low-Temperature Drying System

Performance Optimization and Numerical Simulation Study of Sludge Low-Temperature Drying System

Mesoscale Numerical Study on Time-Dependent Nonuniform Steel Corrosion–Induced Damage in Recycled Aggregate Concrete Systems

Mesoscale Numerical Study on Time-Dependent Nonuniform Steel Corrosion–Induced Damage in Recycled Aggregate Concrete Systems

Advancements in artificial intelligence-driven techniques for interventional cardiology.

This paper aims to thoroughly discuss the impact of artificial intelligence (AI) on clinical practice in interventional cardiology (IC) with special recognition of its most recent advancements. Thus, recent years have been exceptionally abundant in advancements in computational tools, including the development of AI. The application of AI development is currently in its early stages, nevertheless new technologies have proven to be a promising concept, particularly considering IC showing great impact on patient safety, risk stratification and outcomes during the whole therapeutic process. The primary goal is to achieve the integration of multiple cardiac imaging modalities, establish online decision support systems and platforms based on augmented and/or virtual realities, and finally to create automatic medical systems, providing electronic health data on patients. In a simplified way, two main areas of AI utilization in IC may be distinguished, namely, virtual and physical. Consequently, numerous studies have provided data regarding AI utilization in terms of automated interpretation and analysis from various cardiac modalities, including electrocardiogram, echocardiography, angiography, cardiac magnetic resonance imaging, and computed tomography as well as data collected during robotic-assisted percutaneous coronary intervention procedures. Thus, this paper aims to thoroughly discuss the impact of AI on clinical practice in IC with special recognition of its most recent advancements.

Numerical study of transitions in lid-driven flows in shallow cavities

In this article, three dimensional (3D) lid-driven flow in shallow cavities with a unit square base are studied. The numerical solution of the Navier–Stokes equations modeling incompressible viscous fluid flow in a cavity is obtained via a methodology combining a first order accurate operator-splitting scheme, a L 2 -projection Stokes solver, a wave-like equation treatment of the advection and finite element space approximations. Numerical results of a lid-driven flow in a cubic cavity show a good agreement with those reported in literature. The critical Reynolds numbers (Re cr ) for having flow with increasing of oscillating amplitude (a Hopf bifurcation) in different shallow cavities are obtained and associated oscillating modes are studied.

An Overview of the Free Energy Principle and Related Research.

The free energy principle and its corollary, the active inference framework, serve as theoretical foundations in the domain of neuroscience, explaining the genesis of intelligent behavior. This principle states that the processes of perception, learning, and decision making-within an agent-are all driven by the objective of "minimizing free energy," evincing the following behaviors: learning and employing a generative model of the environment to interpret observations, thereby achieving perception, and selecting actions to maintain a stable preferred state and minimize the uncertainty about the environment, thereby achieving decision making. This fundamental principle can be used to explain how the brain processes perceptual information, learns about the environment, and selects actions. Two pivotal tenets are that the agent employs a generative model for perception and planning and that interaction with the world (and other agents) enhances the performance of the generative model and augments perception. With the evolution of control theory and deep learning tools, agents based on the FEP have been instantiated in various ways across different domains, guiding the design of a multitude of generative models and decision-making algorithms. This letter first introduces the basic concepts of the FEP, followed by its historical development and connections with other theories of intelligence, and then delves into the specific application of the FEP to perception and decision making, encompassing both low-dimensional simple situations and high-dimensional complex situations. It compares the FEP with model-based reinforcement learning to show that the FEP provides a better objective function. We illustrate this using numerical studies of Dreamer3 by adding expected information gain into the standard objective function. In a complementary fashion, existing reinforcement learning, and deep learning algorithms can also help implement the FEP-based agents. Finally, we discuss the various capabilities that agents need to possess in complex environments and state that the FEP can aid agents in acquiring these capabilities.

Experimental and Numerical Study of Hydraulic Characteristics and Pressurization Deterioration Mechanism of a Three-Stage Mixed-Flow Electrical Submersible Pump Under Gas-Liquid Condition

Abstract Electrical submersible pump (ESP) is extensively utilized in industrial sectors such as petroleum, chemical, and nuclear energy. However, ESPs experience pressurization deterioration due to the high gas volume fraction (GVF), resulting in the pressurization failure. In this paper, a three-stage mixed-flow ESP with closed impeller structure is detailed analysis. The interstage hydraulic characteristics and pressurization deterioration mechanism of the mixed-flow ESP are investigated at various rotational speeds and inlet conditions by combining experimental and simulation. The population balance model (PBM) and renormalization group (RNG) k − ε model are employed. As the liquid flowrate increases, the ESP experiences a “three-stage” downward trend in pressurization. It is discovered that the first booster stage has a lower inflow velocity and flow separation degree compared to the subsequent booster stages, resulting in a greater liquid-phase pressurization capacity. The gas–liquid pressurization exhibits a wave-shaped downward trend due to significant deterioration in stage-wise pressurization when the liquid flowrate is low. Once the inlet gas volume fraction (IGVF) reaches the first critical GVF, the gas aggregates on the impeller's suction surface are removed at the impeller outlet, creating an annular air mass, which creates a chaotic vortex absorbing the fluids' kinetic energy.

Reducing shallow foundation rotation due to reverse faulting using predefined shear planes

Permanent ground displacement due to faulting during strong earthquakes threatens the stability of civil structures. To protect surface foundations from faulting, several mitigation techniques have been developed. Owing to the limitations of earlier mitigation strategies for unidentified reverse faulting conditions, this research presents a novel mitigation system, including the installation of sliders beneath the wings of a V-shaped concrete element, to reduce footing rotation. To evaluate the effectiveness of the suggested mitigation strategy, concrete strength tests, a physical centrifuge test and a comprehensive numerical study for different fault locations and dip angles were conducted for the first time. Results demonstrated that the placement of the sliders beneath the V-shaped wings had a significant effect on dissipating the fault dislocation. Compared to the previous mitigation techniques, the proposed system decreased the foundation rotation from a high value (11°) to a low value (3·2°) for the worst-case scenarios. Furthermore, the friction coefficients among sliders and concrete, the concrete wing inclination angle, length and thickness were critical parameters to design the optimum proposed system. In contrast, soil layer thickness and relative density did not significantly affect the footing rotation.

Wake Formation Flow Physics and Boundary Layer Analysis on the Sides of the Isosceles Triangular Cylinder with Apex Pointing Downstream

Boundary layer interaction with downstream flow structures was numerically studied to find the region of inactivity behind an 75° isosceles triangular cylinder with apex pointing downstream at intermediate Reynolds numbers (Re = 520, 640, 840 and 1040). The Standard k-ε model in OpenFOAM was used in the study. Numerical results were validated against Particle Image Velocimetry data. Results revealed the stable region of inactivity characterized by low turbulent kinetic energy and vorticity. The onset of secondary vortex and separation point, independent of Reynolds number, was identified. The onset of the secondary vortex was located at (x = 2 mm) from the base and (y = 1.5 mm) from the apex on either side of the cylinder. The ratio of modulus of absolute primary z-component of vorticity, |ω_z^1 |, to the modulus of secondary z-component of vorticity, |ω_z^2 |, was found to be approximately equal to 1.2. This ratio is invariant of the Reynolds number of the study. These findings have practical implications. The unique properties of the inactivity region forms an ideal location that can be used for injecting fluid, placing measurement probe, active flow control and drag reduction. The research problem is formulated in the introduction. Literature is reviewed next providing the background. Details about the range of parameters, governing equations, numerical study details and software used are given in the methodology section. The results section gives the numerical results, verified by mesh refinement test and validated against experimental results. The results are finally discussed in the next section.

Current Insight on the Role of Glucokinase and Glucokinase Regulatory Protein in Diabetes.

The glucokinase regulator (GCKR) gene encodes an inhibitor of the glucokinase enzyme (GCK), found only in hepatocytes and responsible for glucose metabolism. A common GCKR coding variation has been linked to various metabolic traits in genome-wide association studies. Rare GCKR polymorphisms influence GKRP activity, expression, and localization. Despite not being the cause, these variations are linked to hypertriglyceridemia. Because of their crystal structures, we now better understand the molecular interactions between GKRP and the GCK. Finally, small molecules that specifically bind to GKRP and decrease blood sugar levels in diabetic models have been identified. GCKR allelic spectrum changes affect lipid and glucose homeostasis. GKRP dysfunction has been linked to a variety of molecular causes, according to functional analysis. Numerous studies have shown that GKRP dysfunction is not the only cause of hypertriglyceridemia, implying that type 2 diabetes could be treated by activating liver-specific GCK via small molecule GKRP inhibition. The review emphasizes current discoveries concerning the characteristic roles of glucokinase and GKRP in hepatic glucose metabolism and diabetes. This information has influenced the growth of directed molecular therapies for diabetes, which has improved our understanding of lipid and glucose physiology.