Numerical Characteristics Research Articles

Numerical investigation gaseous ammonia basic jet and mixing characteristics in the constant volume vessel

Ammonia is considered a promising zero-carbon fuel under the dual-carbon targets. Systematically understanding ammonia's basic jet and mixing characteristics can promote its practical application in the engine field. Thus, a three-dimensional computational fluid dynamics (CFD) model of constant volume vessel (CVV) is established in this paper, and the forecasting abilities of the Large Eddy Simulation (LES) and Reynolds-Averaged Navier-Stokes (RANS) models for the gaseous ammonia jet process are compared based on the experimental data. Then, the ammonia jet development rule and mixing characteristics under different ambient temperatures, ambient pressures, and fuel temperatures are investigated. The variation laws of key data such as penetration distance, jet area, ammonia mass concentration distribution, equivalence ratio, and turbulent kinetic energy are obtained and analyzed. The results show that during the injection process, the airflow on the left and right sides of the jet rotates in opposite directions. A higher temperature and lower pressure within the CVV lead to a larger flammable range for ammonia jets. Moreover, the distribution of ammonia mass concentration on the radial plane shows a dynamic development law with time, meaning that it first increases to a maximum value, then gradually decreases, and finally remains stable. The farther the radial plane is from the nozzle outlet position, the lower the ammonia mass concentration. In addition, the ammonia jet's turbulent kinetic energy has a large core region and symmetrical distribution along the central axis. At the beginning of the injection, the core length is small, gradually increasing with time and finally stabilizing.

Comparative Analysis of Tool Functionality in Descriptive Statistics of Language R

The article provides methods and procedures of getting numerical statistical characteristics of qualitative and quantitative features by means of language R in the process of preliminary data analysis. The most expedient, in view of simplicity of use and information value of result, specification of functions of language descriptive statistics was shown. The author studied comparative advantages of tools of descriptive statistics of the language with the open software code R in comparison with similar units Python and Ms Excel. A conclusion was drawn about the convenience and appeal of using language R for estimating descriptive statistics for beginners, who have not got specific knowledge in programming. For example, it was shown that in order to get min 12 characteristics of descriptive data statistics grouped by categories only one command will be enough. As for estimating one statistical characteristic for segment of data produced by several grouping features, again only one command is necessary. Findings of the research can be useful for investigators of different fields, both beginners and experts, who work with methods of statistical data processing in academic and practical spheres.

STUDYING THE IMPACT OF THE VOLUME OF WORK AT THE RAILCAR TRANSFER POINT ON THE NEED FOR TRACK STORAGE CAPACITY FOR BOGIES

Purpose. In the context of military aggression by moscow, export cargo flows, which were largely transported by sea from Ukraine's Black Sea ports, were directed towards western land crossings. At the same time, exporters were trying not only to find new logistics routes but also to increase exports to European countries. This resulted in longer queues at land borders, both road and rail, and the turnover of railcars and their individual components became uncontrollable and unpredictable. The reason for the downtime of cargo in railroad cars at the borders was the limited processing capacity of railcar interchange points and the transshipment capacity of cargo terminals at border stations. One of the elements of the cargo border crossing system, the increase of which will increase the volume of export cargo flow, is the points of change of wagons from 1435 mm gauge to 1520 mm gauge. Therefore, the aim of this study is to determine the required track capacity for storing 1520 mm and 1435 mm gauge bogies under conditions of daily uneven transportation and the stochastic nature of the turnover of cars on the territory of European railways. Methodology. Simulation modeling, mathematical statistics, and inventory management methods were used to study the impact of the workload of a railcar transfer point on track capacity. Results. As a result of the research, the numerical characteristics of a random variable of the number of cars transferred to the transfer point during the day, as well as the value of car turnover in Europe, were obtained. A methodology for determining the required track capacity for storing bogies based on simulation modeling of the operation of the railcar interchange points was developed. The dependence of the guaranteed stock of bogies of 1435 mm gauge at the station on the daily number of cars serviced at the railroad car transfer point was obtained. Practical significance. The obtained dependences of the required capacity of the tracks for storing bogies on the daily number of cars arriving at the transfer point will allow us to check and, if necessary, bring the existing track development of border stations into line with the calculated volumes of work.

Unveiling Deep-Seated Gravitational Slope Deformations via Aerial Photo Interpretation and Statistical Analysis in an Accretionary Complex in Japan

The objective of this study was to identify the locations of deep-seated gravitational slope deformations (DGSDs) and define the numerical characteristics of these deformations to contribute to the sustainable management of social infrastructure in the event of an increased disaster. The topographic features of the DGSDs were quantitatively characterized based on their surface morphologies. Topographic features indicative of gravitational deformation in pre-slide topographic maps, such as terminal cliff failures, irregular undulations, and gullies, suggest that progressive deformation occurred over a prolonged period. To track the gravitational deformation over time, we interpreted aerial photographs of DGSDs from 1948 and 2012 associated with deep-seated landslides on the Kii Peninsula induced by Typhoon Talas on 2–5 August 2011. Corresponding numerical analysis of the gravitational deformations using 1 m digital elevation models reveals that landslide areas exhibit eight characteristic influencing factors, demonstrating that characteristic morphologies exist in areas that eventually experience landslides. One such morphological feature is the existence of a gently sloping area in the upper section of the deep-seated landslide mass, which comprises a catchment basin without a corresponding valley or gully. These findings suggest that rainwater penetrates the ground, and degrades and deforms the rock within the landslide mass, causing the slope to fail after torrential rainfall. This study holds great significance for advancing sustainable infrastructure development and management and mitigating environmental changes.

Multi-factor numerical simulation and response characteristics of two-dimensional nuclear magnetic resonance in digital cores

Two-dimensional nuclear magnetic resonance (2D NMR) logging is an important technology for reservoir evaluation in oil and gas exploration and development, and it has unique advantages in fluid identification. However, in oil shale reservoirs, the mechanisms of 2D NMR logging are complex, and the response characteristics of various factors are unclear. Numerical simulation based on digital core is a significant method to study the influences of multiple factors on 2D NMR. This manuscript introduces the construction method of high-resolution three-dimensional (3D) digital core models, and expounds the principle and workflow of 2D NMR numerical simulation. High-resolution 3D digital cores were constructed based on X-CT, MAPS, and QEMSCAN data of Berea sandstone and Daqing Gulong shale, respectively. Based on 3D digital cores, 2D NMR numerical simulations were carried out on temperature/viscosity, echo spacing, and frequency. Then, 2D NMR mechanisms were analyzed and the response characteristics of multiple factors were summarized. The results show that the T2 and T1 of the water peak have a linear negative correlation with temperature/viscosity, and the T2 and T1 of the oil peak have a linear positive correlation. For Daqing Gulong shale, the T2 of the water peak has a linear positive correlation with echo spacing, while for Berea sandstone, echo spacing has little effect. For Berea sandstone, the T1 of the water peak is linearly positively correlated with frequency. For Daqing Gulong shale, the T1 of the water peak and oil peak are both linearly positively correlated with frequency. The response characteristics of multiple factors could provide a reference and basis for the interpretation and evaluation of 2D NMR logging under complex conditions.

The Dynamic Comprehensive Evaluation of the Importance of Cutting Parameters in the Side Milling TC4 Process Using an Integrated End Mill.

In the cutting process, there are many parameters that affect the cutting effect, and the same parameter has different degrees of influence on different performance indicators, which makes it difficult to select key parameters for parameter optimization and parameter combination evaluation while considering multiple performance indicators at the same time. The process of titanium alloy milling with an integrated end mill is studied herein. The values of cutting tool flank face wear and material removal rates are obtained with experimental and analytical methods. Numerical characteristics and causes of the cutting tool flank face wear at different stages are also analyzed. The dynamic, comprehensive evaluation method based on the double incentives model is used to evaluate the dynamic, comprehensive importance of cutting parameters in view of the problem of considering multiple performance indicators and the characteristics of the dynamic change in performance indicators in the cutting process. According to the result of a dynamic, comprehensive evaluation, the cutting parameters with the highest comprehensive importance are selected. Finally, the radar map is used to plot the comprehensive importance of the cutting parameters. The overall comprehensive importance of each cutting parameter is intuitively displayed as well. As a result of the research, the dynamic, comprehensive evaluation method based on the double incentives model has a good application value in the evaluation of tool performance in the cutting process and can quickly select the best tool performance parameter combination; it is established that the most comprehensive parameter is the cutting speed, and the cutting width is the second most important. In turn, the comprehensive importance of the cutting depth is the lowest.

Numerical study flow and heat transfer characteristics of superhydrophobic single tube bundle surface and wake flow

Regarding the design optimization of the heat exchanger, the traditional method of changing the size of the macroscopic structure can no longer meet the increasing demand for heat exchange performance. The superhydrophobic surface can significantly reduce the flow resistance, which is expected to be a new method for the optimal design of heat exchanger. However, when the superhydrophobic effect is applied to a specific type of heat exchanger, its flow and heat transfer performance is not clear. In this paper, the flow and heat transfer model of the traversing superhydrophobic tube bundle is studied. Combined with the FLUENT 18.0 software, the slip boundary conditions of the superhydrophobic surface are deduced, and the flow and heat transfer characteristics of the superhydrophobic tube bundle surface and the wake are numerically analyzed, the influence mechanisms of slip effect on flow and heat transfer characteristics were obtained. Overall, as Ls increases, the comprehensive Nu, also known as heat transfer performance, on the cylindrical surface gradually increases, while the comprehensive Cf, also known as frictional resistance, on the cylindrical surface gradually decreases, indicating that superhydrophobic performance is expected to achieve heat transfer enhancement. The above conclusions can provide a theoretical basis for the optimal design of high-efficiency compact heat exchanger, and have important engineering application significance.

Uncertainty quantification of the lattice Boltzmann method focussing on studies of human-scale vascular blood flow

Uncertainty quantification is becoming a key tool to ensure that numerical models can be sufficiently trusted to be used in domains such as medical device design. Demonstration of how input parameters impact the quantities of interest generated by any numerical model is essential to understanding the limits of its reliability. With the lattice Boltzmann method now a widely used approach for computational fluid dynamics, building greater understanding of its numerical uncertainty characteristics will support its further use in science and industry. In this study we apply an in-depth uncertainty quantification study of the lattice Boltzmann method in a canonical bifurcating geometry that is representative of the vascular junctions present in arterial and venous domains. These campaigns examine how quantities of interest—pressure and velocity along the central axes of the bifurcation—are influenced by the algorithmic parameters of the lattice Boltzmann method and the parameters controlling the values imposed at inlet velocity and outlet pressure boundary conditions. We also conduct a similar campaign on a set of personalised vessels to further illustrate the application of these techniques. Our work provides insights into how input parameters and boundary conditions impact the velocity and pressure distributions calculated in a simulation and can guide the choices of such values when applied to vascular studies of patient specific geometries. We observe that, from an algorithmic perspective, the number of time steps and the size of the grid spacing are the most influential parameters. When considering the influence of boundary conditions, we note that the magnitude of the inlet velocity and the mean pressure applied within sinusoidal pressure outlets have the greatest impact on output quantities of interest. We also observe that, when comparing the magnitude of variation imposed in the input parameters with that observed in the output quantities, this variability is particularly magnified when the input velocity is altered. This study also demonstrates how open-source toolkits for validation, verification and uncertainty quantification can be applied to numerical models deployed on high-performance computers without the need for modifying the simulation code itself. Such an ability is key to the more widespread adoption of the analysis of uncertainty in numerical models by significantly reducing the complexity of their execution and analysis.

Evaluating the Performance and Challenges of Machine Learning Models in Network Anomaly Detection

The application of machine learning algorithms for anomaly detection in network traffic data is examined in this study. Using a collection of network flow records that includes attributes such as IP addresses, ports, protocols, and timestamps, the study makes use of correlation heatmaps, box plots, and data visualization to identify trends in numerical characteristics. After preprocessing, which includes timestamp conversion to Unix format, three machine learning models Support Vector Machine (SVM), Gaussian Naive Bayes, and Random Forest are used for anomaly identification. The Random Forest Classifier outperforms SVM and Naive Bayes classifiers with better precision and recall for anomaly diagnosis, achieving an accuracy of 87%. Confusion matrices and classification reports are used to evaluate the models, and they show that the Random Forest Classifier performs better than the other models in identifying abnormalities in network traffic. These results provide significant value to the field of cybersecurity by highlighting the effectiveness of machine learning models specifically, the Random Forest Classifier in boosting anomaly detection capacities for network environment security.

Active feedback control on acoustic radiation of metastructure shell with low-frequency and broadband characteristics

Due to the tunable characteristics of elastic waves, the vibroacoustic coupling behavior of a mechanical metastructure is a hot topic of underwater vehicles. In this work, a metastructure shell with active feedback control is presented and fabricated. The dynamic effective density and sound pressure level are derived to find the influences of acceleration and displacement feedback control. Different from a single cylinder, a double cylinder structure has both in-phase and anti-phase modes. Numerical results are obtained by Fourier transform and harmonic series expansion. With the introduction of an active feedback control system, the reduction of acoustic radiation shows low-frequency and broadband characteristics. In addition, finite element simulation is applied to support numerical results and present vibroacoustic characteristics. Finally, an experiment is performed in the anechoic chamber to illustrate the quiet metastructure shell, which can be applied to new designs of underwater vehicles.

Regional features of the manifestation of demographic threats in terms of characteristics of marriage, motherhood and childhood

The subject of the research is the regional characteristics of demographic indicators of marriage, motherhood and childhood, their relationship with the indicator of natural growth. At the same time, the natural decline of the population is considered as one of the key threats to the demographic and, consequently, national security of the population of the Russian Federation, since it entails a number of economic and geopolitical risks. The variability of indicators of marriage, motherhood and childhood is considered in the study as a possible source of variability in natural growth. Therefore, the regional peculiarities of the numerical characteristics of each of these features are analyzed in detail. Special attention is paid to regions showing abnormal values. In addition, the nature of the influence of individual indicators of marriage, motherhood and childhood on the natural growth rate has been studied in order to identify key factors determining the variability of the latter. The paper uses methods of exploratory data analysis: elements of descriptive statistics and diagrams to demonstrate trends . The closeness of the relationship between the indicators of the natural growth coefficient and the indicators of marriage, motherhood and childhood is also determined and a model of linear regression of the natural growth coefficient on the total fertility rate is constructed. The authors' contribution to the development of the topic is that, despite the simplicity of the stated methods, the study allowed us to identify regions with specific features in one or more indicators of marriage, motherhood, childhood, as well as natural growth. Further study of the features of regional demographic policy, the state of health, and cultural characteristics of the selected regions will allow us to borrow positive experience and correct negative trends, which should lead to a decrease in the factors determining the depopulation of the Russian population in terms of marriage, motherhood and childhood. The regression model allowed us to conclude that measures are needed to increase the total fertility rate, since, among the factors considered, the natural growth rate turned out to be the most sensitive to this trait, which simultaneously characterizes the propensity of women to motherhood and the intensity of fertility

Kernel multi-granularity double-quantitative rough set based on ensemble empirical mode decomposition: Application to stock price trends prediction

As financial markets grow increasingly complex and dynamic, accurately predicting stock price trends becomes crucial for investors and financial analysts. Effectively identifying and selecting the most predictive attributes has become a challenge in stock trends prediction. To address this problem, this study proposes a new attribute reduction model. A rough set theory model is built by simplifying the prediction process and combining it with the long short-term memory network (LSTM) to enhance the accuracy of stock trends prediction. Firstly, the Ensemble Empirical Mode Decomposition (EEMD) is utilized to decompose the stock price data into a multi-granularity information system. Secondly, due to the numerical characteristics of stock data, a kernel function is applied to construct binary relationships. Thirdly, recognizing the noise inherent in stock data, the double-quantitative rough set theory is utilized to improve fault tolerance during the construction of decision attributes' lower and upper approximations. Moreover, calculate the correlation between conditional and decision attributes, and retain highly correlated conditional attributes for prediction. The kernel multi-granularity double-quantitative rough set based on the EEMD (EEMD-KMGDQRS) model proposed identifies the key factors behind stock data. Finally, the efficacy of the proposed model is validated by selecting 356 stocks from diverse industries in the Shanghai and Shenzhen stock markets as experimental samples. The results show that the proposed model improves the generalization of attribute reduction results through a fault tolerance mechanism by combining kernel function with multi-granularity double-quantitative rough set, thereby enhancing the accuracy of stock trends prediction in subsequent LSTM prediction processes.

Distributional Fairness-aware Recommendation

Fairness has been gradually recognized as a significant problem in the recommendation domain. Previous models usually achieve fairness by reducing the average performance gap between different user groups. However, the average performance may not sufficiently represent all the characteristics of the performances in a user group. Thus, equivalent average performance may not mean the recommender model is fair, for example, the variance of the performances can be different. To alleviate this problem, in this article, we define a novel type of fairness, where we require that the performance distributions across different user groups should be similar. We prove that with the same performance distribution, the numerical characteristics of the group performance, including the expectation, variance, and any higher-order moment, are also the same. To achieve distributional fairness, we propose a generative and adversarial training framework. Specifically, we regard the recommender model as the generator to compute the performance for each user in different groups, and then we deploy a discriminator to judge which group the performance is drawn from. By iteratively optimizing the generator and the discriminator, we can theoretically prove that the optimal generator (the recommender model) can indeed lead to the equivalent performance distributions. To smooth the adversarial training process, we propose a novel dual curriculum learning strategy for optimal scheduling of training samples. Additionally, we tailor our framework to better suit top-N recommendation tasks by incorporating softened ranking metrics as measures of performance discrepancies. We conduct extensive experiments based on real-world datasets to demonstrate the effectiveness of our model.

Assessment of clean production level in phosphate mining enterprises: Based on the fusion group decision weight and limited interval cloud model

To develop a robust cleaner production level assessment model for phosphate mining enterprises, this study investigated the key factors influencing their cleaner production levels. The model incorporates integrated group decision weights and a limited interval cloud model. Based on the comprehensive life cycle of phosphate mining production, we constructed a detailed evaluation index system to assess the cleaner production level of these enterprises. This system defined the quantitative ranges for each level and revealed the interrelationships among the indicators. To ensure comprehensive weight determination, we proposed a method that integrates the expertise of multiple stakeholders. This method leveraged group decision-making principles to effectively resolve conflicts in expert judgments during the weight determination process. In addressing the characteristics of evaluation indicators for the cleaner production level of phosphate mining enterprises, we proposed employing a finite interval cloud model to accurately characterize the distribution of the indicators. Leveraging the finite interval cloud model, we obtained numerical characteristics for various indicators and incorporated the weights of the integrated group decision-making attributes. This approach enabled us to determine the cleaner production level of phosphate mining using a finite interval cloud generator. Validation using five phosphate mining enterprises in Hubei Province, China, confirmed the model's high accuracy. This model demonstrated its applicability in evaluating cleaner production levels for phosphate mining enterprises, offering a theoretical foundation for developing targeted environmental management policies.

Multivariate time series clustering based on fuzzy cognitive maps and community detection

Most time series clustering methods mainly focus on univariate time series (UTS). Compared with UTS, multivariate time series (MTS) consists of multiple components. Although interest in MTS clustering is increasing, its performance is far from satisfactory. Most traditional MTS clustering methods may have two limitations. First, they do not consider both the temporal features of each component and the relationship between the components. Second, they can only identify the local relationship between adjacent data, but cannot obtain long-distance global relationships and capture clusters of arbitrary shapes. In this paper, we develop a method for MTS clustering based on fuzzy cognitive maps (FCMs) and community detection, termed as MTSC-FCM-CD. To overcome the first limitation, we use FCM to represent MTS; FCM can extract temporal features while preserving the relationship between components in MTS. To overcome the second limitation, we use the community detection algorithm to cluster the global relations, which is different from the traditional nearest neighbor distance-based method. In the calculation process, the similarity between FCMs should be used to build a complex network. Existing methods calculate the similarity between FCMs, only considering the numerical characteristics but ignoring the topological structure. To solve this problem, we design a two-stage similarity measure to build a complex network. In comparison to the existing methods, the experimental results on twenty benchmark datasets demonstrate the effectiveness of MTSC-FCM-CD in MTS clustering.

Proton magnetic resonance spectroscopy in children with some neurological and mental disorders accompanied by mental and speech development delay: a prospective study

Proton magnetic resonance spectroscopy in children with some neurological and mental disorders accompanied by mental and speech development delay: a prospective study

Estimation of traditional numerical characteristics of lognormal distribution laws of a one-dimensional random variable in conditions of large volume statistical data

The efficiency of estimating the numerical characteristics of a family of the lognormal distribution law of a onedimensional random variable under conditions of large volumes of statistical data is considered. To circumvent the problem of large samples, methods of discretization the range of values of a random variable based on the formulas of Sturges, Brooks-Carruthers, Heinhold-Gaede and the formula proposed by the authors of this article are used. Data arrays have been generated that make it possible to evaluate the numerical characteristics of the laws of distribution of random variables, taking into account their discrete values. Based on the transformed data arrays, estimates of the mathematical expectation, standard deviation, skewness and kurtosis coefficients were calculated. Estimates of the numerical characteristics of the considered distribution laws under the conditions of a continuous and discrete random variable are compared for different volumes of initial statistical data. The effectiveness of methods for estimating the numerical characteristics of the family of the lognormal distribution law based on the initial statistical data and on the results of transformations of these data using known discretization formulas has been established. The reliability of the comparison of the effectiveness indicators of the studied methods was confirmed by using the Kolmogorov-Smirnov criterion. It is shown that the discretization formula proposed by the authors of this article is better and more effective compared to traditional methods.

On the Transformation of a Stationary Fuzzy Random Process by a Linear Dynamic System

In this paper, stationary random processes with fuzzy states are studied. The properties of their numerical characteristics—fuzzy expectations, expectations, and covariance functions—are established. The spectral representation of the covariance function, the generalized Wiener–Khinchin theorem, is proved. The main attention is paid to the problem of transforming a stationary fuzzy random process (signal) by a linear dynamic system. Explicitform relationships are obtained for the fuzzy expectations (and expectations) of input and output stationary fuzzy random processes. An algorithm is developed and justified to calculate the covariance function of a stationary fuzzy random process at the output of a linear dynamic system from the covariance function of a stationary input fuzzy random process. The results rest on the properties of fuzzy random variables and numerical random processes. Triangular fuzzy random processes are considered as examples.

Experimental investigation and numerical modeling of effect of specimen size on microwave-induced fracturing of diorite

The effect of specimen size on microwave-induced fracturing of rocks has not been well understood. In this study, the experimental tests using open-ended microwave and numerical simulations coupling COMSOL Multiphysics and four-dimensional lattice spring model (4D-LSM) were carried out to investigate the evolution process of microwave-induced cracks, and to elucidate the mechanisms responsible for size-dependent microwave-induced fracturing. The experiments show that with the increase of the specimen dimension, the number of microwave-induced cracks is almost constant, and the total crack length first increases and then decreases, while the maximum crack width and maximum crack depth decrease gradually. A comparison of the experimental and numerical temperature characteristics as well as cracking characteristics indicates that the models established in this study are reasonable in the simulation of the rock fracturing behaviours. The numerical results show that the crack initiation time increases logarithmically with the increase of the specimen dimension. For the small-sized specimens, the microwave-induced cracks started from the outer boundary of the specimen and propagated toward to specimen center and into the depth. For the medium-sized and large-sized specimens, the microwave-induced cracks initiated near the outer boundary of the antenna aperture, and then propagated toward to the center of the specimen and the outer boundary of the specimen. The main reason for the size-dependent of fracturing behavior is that the local fracture energy will dramatically decrease when the crack is approaching the specimen boundary.

NEWTON’S METHOD IN CONSTRUCTING A SINGULAR SET OF A MINIMAX SOLUTION IN A CLASS OF BOUNDARY VALUE PROBLEMS FOR THE HAMILTON - JACOBI EQUATIONS

The non-smooth features of the minimax (generalized) solution of the considered class of Dirichlet problems for equations of Hamiltonian type are due to the existence of pseudo-vertices - singular points of the boundary of the boundary set. The paper develops analytical and numerical methods for constructing pseudo-vertices and their accompanying constructive elements, which include local diffeomorphisms generating pseudo-vertices, as well as markers - numerical characteristics of these points. For markers, an equation with a characteristic structure inherent in equations for fixed points is obtained. An iterative procedure based on Newton’s method for the numerical construction of its solution is proposed. The convergence of the procedure to the pseudovertex marker is proved. An example of the numerical-analytical construction of a minimax solution is given, illustrating the effectiveness of the developed approaches for constructing nonsmooth solutions of boundary value problems.