Computational Modeling Methods Research Articles

Methods of computer modeling of electromagnetic field propagation in urban scenarios for Internet of Things

Abstract The paper investigates software solutions to simulate radio waves propagation in urban environment, specifically for the context of the Internet of Things (IoT). The purpose of the study is to provide a comprehensive guide for utilizing the Altair Feko software to obtain a detailed representation of the IoT network, its associated parameters and how they relate to the modeling of electromagnetic fields. It is beneficial to simulate the network during the design stage to obtain valuable data and make necessary adjustments. The key features of a Sigfox or other low power IoT network can be obtained by simulation, enabling an evaluation of the network design prior to its actual implementation. Given the growing demand for IoT devices and networks, researching the optimal design and performance of such networks is of great importance. This underscores the need for continuous exploration of effective methods to achieve efficient design and performance of IoT networks.

Моделирование обрушения горной породы

Introduction. At present, the study of the movement of falling rock (collapses of rocks), the causes of their occurrence, as well as their influence on other slope processes is an important field of research. These studies are based on both theoretical knowledge and experimental observations. Separately, it is necessary to highlight the methods of mathematical and computer modeling, which are widely used in cases where experimental data are not enough. The study of the influence of the slope angle and the slope height (on which the falling rock mass was located at the initial moment of time) on the affected area was made using the methods of mathematical and computer modeling. Materials and methods. Two-fluid model based on the continuum approach and the kinetic theory of granular gas was used for a theoretical study of the movement of falling rock along the slope. The model takes into account the fluidization of the falling mass flow, and its implementation does not require the use of powerful computing resources. The modeling results obtained using the two-fluid model were compared with the results of an experimental study on the collapse of dolomite particles (average diameter 5 mm) carried out in the laboratory. The values of the slope height varied from 35 cm to 86 cm, the values of the slope angle varied from 35 degrees to 53 degrees in accordance with the experimental data. The results and discussion. The graphs of the distance of the rock run after its collapse in the dependence of the slope height and the slope angle were plotted. It is found that the calculation results are in good agreement with the experimental data (the relative error does not exceed 11%) for relatively large values of the slope angle. The results of calculations of the distance of the rock run are somewhat overestimated relative to the experimental data for small values of the slope angle. It is found that the affected area of dolomite particles increases with increasing slope angle and slope height. It should be noted that the affected area depends more on the values of the slope angle than on the height of the slope. If the slope is steeper (the value of the slope angle is greater), than the affected area is greater. Conclusion. The calculation results obtained using the two-fluid model describe the experimental data of the rock collapse satisfactorily. The results of calculations of the distance of the rock run are in good agreement with the experimental data (the relative error does not exceed 11%) for relatively large values of the slope angle. It is found that the distance of the rock run after its collapse depends more on the values of the slope angle than on the height of the slope. Resume. The research results can be useful in estimating the distance of the rock run after its collapse, as well as in developing a methodology for assessing affected areas during rock falls. The direction of future research is to improve the two-fluid model based on the continuum approach and the kinetic theory of granular gas for describing of real rock falls.

Using coherent processing algorithms for direction finding of UAV acoustic signals

Small UAVs are often used in a group, since their signals are highly correlated, their resolution is reduced when using non-coherent processing.
 The article analyzes the well-known methods of coherent processing of acoustic signals in order to increase the resolution in the direction finding of signals with a high degree of correlation. Obtaining qualitative indicators of the analyzed algorithms was carried out by the method of statistical computer modeling in the Matlab environment.
 Based on the simulation results, it is shown that coherent signal processing methods are the most stable in conditions of low signal-to-noise ratios, while non-coherent ones show the best results in the region of high signal-to-noise ratio, while coherent algorithms can potentially distinguish more targets. the WAVES coherent algorithm performs better in the high signal-to-noise ratio region, but loses to the CSSM algorithm in the low signal-to-noise region.
 To increase the efficiency of coherent processing of multipath signals, it applies spatial filtering of the inputsignal.

Implications of using nanoparticles on the performance and safety of nuclear systems

Nanotechnology, and particularly nanoparticles (NPs), command significant attention across diverse fields such as medicine, chemistry, and engineering. NPs are integrated into a base material through various techniques, such as dispersion, coating, and encapsulation. This review intends to offer an evaluation of the implementation of NPs in nuclear energy and radiation protection. Particular focus is given to nanofluids - suspensions of NPs in a base fluid - and their impact on the thermal–hydraulic and neutronic attributes of nuclear reactor cores. Discussion centers on how substituting water with nanofluids as a coolant can modify these characteristics. Computational modeling and experimental methods of nanofluids are examined, highlighting an enhanced heat transfer capacity under both regular and emergency reactor conditions. The influence of nanofluids on neutronic parameters, aiming for optimized operation and safety of nuclear reactors, is also explored. Furthermore, the potential of NPs for radiation shielding is considered, underlining their capability to attenuate radiation effects. Examples are drawn from the addition of NPs to containment structures in nuclear facilities and their use in radiotherapy, as well as enhancing radiation shielding in imaging techniques. Finally, the review encompasses the effects of nanofluids on surface oxidation and corrosion. The importance of these factors lies in their substantial impact on heat transport properties and potential for corrosive damage.

A Pulsed Current Application to the Deformation Processing of Materials.

A review of studies on the electroplastic effect on the deformation process in various conductive materials and alloys for the last decade has been carried out. Aspects, such as the mode and regimes of electric current, the practical methods of its introduction into materials with different deformation schemes, features of deformation behavior accompanied by a pulsed current of different materials, structural changes caused by the combined action of deformation and current, the influence of structural features on the electroplastic effect, changes in the physical, mechanical, and technological properties of materials subjected to plastic deformation under current, possible mechanisms and methods of physical and computer modeling of the electroplastic effect, and potential and practical applications of the electroplastic effect are considered. The growing research interest in the manifestation of the electroplastic effect in such new modern materials as shape-memory alloys and ultrafine-grained and nanostructured alloys is shown. Various methods of modeling the mechanisms of electroplasticity, especially at the microlevel, are becoming the most realistic approach for the prediction of the deformation behavior and physical and mechanical properties of various materials. Original examples of the practical application of electropulse methods in the processes of drawing, microstamping, and others are given.

Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients.

Recent clinical research describes a subset of glioblastoma patients that exhibit REP prior to the start of radiation therapy. Current literature has thus far described this population using clinicopathologic features. To our knowledge, this study is the first to investigate the potential of conventional radiomics, sophisticated multi-resolution fractal texture features, and different molecular features (MGMT, IDH mutations) as a diagnostic and prognostic tool for prediction of REP from non-REP cases using computational and statistical modeling methods. The radiation-planning T1 post-contrast (T1C) MRI sequences of 70 patients are analyzed. An ensemble method with 5-fold cross-validation over 1000 iterations offers an AUC of 0.793 ± 0.082 for REP versus non-REP classification. In addition, copula-based modeling under dependent censoring (where a subset of the patients may not be followed up with until death) identifies significant features (p-value < 0.05) for survival probability and prognostic grouping of patient cases. The prediction of survival for the patients' cohort produces a precision of 0.881 ± 0.056. The prognostic index (PI) calculated using the fused features shows that 84.62% of REP cases fall under the bad prognostic group, suggesting the potential of fused features for predicting a higher percentage of REP cases. The experimental results further show that multi-resolution fractal texture features perform better than conventional radiomics features for prediction of REP and survival outcomes.

Simulation of heat transfer in a nanoparticle enhanced phase change material to design battery thermal management systems: A lattice Boltzmann method study

BackgroundIn the present paper, the natural convection of phase change material (PCM) in a T-shaped cavity is investigated by the lattice Boltzmann method (LBM). In recent years, LBM has become a powerful method for computational modeling of a broad variety of complex fluid flow concerns, including the simulation of the melting process in PCMs. MethodsThe enclosure contains CuO nanoparticles and two constant temperature heat sources on the sidewalls of the cavity. The obtained results are presented in different Rayleigh numbers (Ra=103–105), cavity angles (θ = 0 − 90), the volume fraction of nanoparticles (ϕ = 0 − 0.03), and aspect ratios. Results show that the PCM melting speed is lower for low Rayleigh numbers compared to when the number is equal to 105 by 50%. To alter melting time in a specific Ra number, adding nanoparticles, changing cavity slope, and aspect ratio are investigated. Results show that the melting rate is little affected by the addition of nanoparticles but, generally adding nanoparticles delays PCM melting. Significant findingsRaising the distance between the battery and the top of the cavity is known to delay melting by 70% when the distance ratio increase from H1/H = 0.25 to 075, whereas increasing or lowering the distance between the batteries does not affect the melting time. Such a study can be used to design battery thermal management systems (BTMS).

Natural convection of rectangular cavity enhanced by obstacle and fin to simulate phase change material melting process using Lattice Boltzmann method

In recent years, the lattice Boltzmann method has become a powerful method for computational modeling of various complex fluid flow concerns, including the simulation of the melting process in phase change materials. In the present paper, the natural convection of phase change materials in a cavity is simulated, and the effect of adiabatic obstacle and fin is investigated by the lattice Boltzmann method. The obtained results are presented in different Rayleigh numbers (Ra = 103-105), and cavity angles (θ=-90to90) in three scenarios (without adiabatic fin and obstacle, with an adiabatic obstacle, and with adiabatic fin). The investigation across various cavity angles, with adiabatic obstacles and fins, demonstrates a consistent trend of effective melting process delay by up to 50%, underscoring the significant impact of these adiabatic features on PCM behavior. Adiabatic obstacles induce localized melting delays due to unmelted zones around them. Streamlines highlight vortices formed by obstacles, and elevated Nusselt numbers correlate with accelerated melting facilitated by adiabatic fins. Modifying the adiabatic fin height from Yf = 0.1 to Yf = 0.7 leads to a doubling of melting time at around 80% PCM melting. Conversely, decreasing fin height from 0.5 to 0.7 extends the complete melting time by approximately 10%, showcasing the influential role of fin height in shaping PCM melting behaviour.

Analysis of the consequences of breakdowns of hydraulic structures and the occurrence of emergency situations

The problem of prevention and elimination of emergencies remains very urgent. The prediction of area flooding as a result of seasonal floods, or due to emergency situations is important and is solved on the basis of various technologies. Computer modeling methods based on direct hydrodynamic calculations of surface water dynamics seem to be an effective direction. Of particular interest is modeling the dynamics of filling of hydraulic structures in mountainous terrain. In this article, features of destruction of hydraulic structures in emergency situations on water bodies. The article also mentions the methods of monitoring water levels in the reservoirs. A description of flood monitoring technology is given, the results of its practical use in some regions are discussed, and directions for further development are outlined. This information is subsequently used to predict the emergency situation.

Study on Dynamic Characteristics of Magnetic Coagulation of Fe-Based Fine Particles in Iron and Steel Industry

Fine dust, represented by Fe-based fine particles and emitted from the production process of the iron and steel industry, is the primary factor causing many diseases represented by industrial pneumoconiosis, and ultra-low dust emission has always been a thorny problem to be solved urgently. To explore the magnetic coagulation effect of Fe-based fine particles in the magnetic field when removing them from industrial flue gas by the magnetic field effect in the iron and steel industry, using FLUENT software, magnetic dipole force was added between particles through user defined function (UDF) based on the computational fluid dynamics-discrete phase model (CFD-DPM) method so that the collision process of particles was then equivalent to their mutual trapping process. Next, the effects of particle size, particle volume fraction, external magnetic field strength, and particle magnetic susceptibility on the magnetic coagulation process were comprehensively studied. Meanwhile, the proton balance equation (PBE) was solved using the partition method on the basis of the computational fluid dynamics-population balance model (CFD-PBM) to compare the coagulation removal effect under random and aligned orientations of magnetic dipoles, respectively. The results showed that the magnetic coagulation strength under the random orientation of magnetic dipoles was greater than that under the aligned orientation. When the particle size of Fe-based fine particles increased from 0.5 μm to 1.5 μm, the magnetic coagulation coefficient decreased from 0.5414 to 0.2882, and the difference in the removal efficiency under the two different orientations of magnetic dipoles became smaller. When the particle volume fraction increased from 0.01 to 0.03, the magnetic coagulation coefficient increased from 0.2353 to 0.5061, and the difference in the removal efficiency under two orientations was enlarged. When the applied external magnetic field strength increased from 0.5 T to 1.0 T, the magnetic coagulation coefficient increased from 0.3940 to 0.5288, and the magnetic susceptibility increased from 0.0250 to 0.0500, the coagulation coefficient increased from 0.3940 to 0.5288, and the difference under two orientations basically stayed unchanged.

Motion and dispersion characteristics of graphite particles in a bottom-blown converter for molten slag reduction

In order to improve the recovery of iron resource in molten basic oxygen furnace (BOF) slag through carbothermal reduction, bottom blowing argon gas is used to promote the mixing efficiency between molten slag and graphite particles in the converter. In the present work, computational fluid dynamic-discrete particle model (CFD-DPM) method coupled with the reduction kinetic model of FeO in molten slag by graphite particle is employed to simulate the multiphase system composed of molten slag, graphite particle and argon gas. The motion and dispersion characteristics of graphite particles in a bottom-blown converter were mainly investigated, and the decrease of graphite particle diameter caused by the reduction reaction was considered. A particle dispersion index was also proposed to evaluate the distribution uniformity of graphite particles in the molten slag. In addition, the effects of initial particle diameter and bottom tuyere arrangement on the dispersion characteristics of graphite particles were further discussed. The results show that the proportion of large-sized particles is higher in the inactive-flow zone and the motion trajectory of graphite particles can be divided into two regimes. Graphite particles with an initial diameter of 5 mm and the converter with six bottom-blown tuyeres are more conducive to the homo-dispersion of graphite particles.

ATOMISTIC SIMULATION OF SELF- DIFFUSION AND DIFFUSION Co ALONG SYMMETRIC TILT GRAIN BOUNDARIES [2¯1 ¯1 0] IN α-Ti

The structure, point defects, self-diffusion, and diffusion of Co for four energetically preferred grain boundaries (GB) with the tilt axis [21 1 0] in α-Ti are being investigated by computer modeling methods. The structure and energies of the boundaries and the energies of the formation of point defects in GB, were calculated by molecular static modeling. The dependencies of point defect formation energies on the distance from the grain boundary plane are demonstrated. The coefficients of grain boundary self-diffusion are calculated by the method of molecular dynamics. The results of self-diffusion modeling are compared with the available experimental data. The simulation of grain boundary diffusion of the impurity Co in titanium is also performed. It is shown that the structure of GB affects the parameters of grain-boundary diffusion both in the case of self-diffusion and in the case of impurity diffusion, and the coefficients of grain-boundary diffusion may differ by several orders of magnitude depending on the structure.

Agent-Based Model to Analyze the Role of the University in Reducing Social Exclusion

While conventional innovation has boosted economic growth in certain regions, it has not contributed to closing the social and economic gap in most developing countries. Humanity is going through a historic moment of great challenges. One of them is social exclusion, a matrix of factors that prevent human beings from achieving well-being: poverty, hunger, inequality, lack of access to basic resources and services, and lack of social ties that help improve these circumstances, among others. This study holds two hypotheses: (1) in this context, inclusive innovation emerges as a response to the inability of conventional innovation to contribute to solve the persistent challenge of social exclusion and (2) universities—key actors in innovation dynamics—should play a fundamental role in the generation of inclusive innovation, especially considering their natural commitment to society. Although the role of the university in innovation has been widely acknowledged and studied, no formal theoretical model has represented inclusive innovation in developing countries adopting a systemic, complex, adaptive, and functional approach and incorporating a diversity of agents, interactions, capabilities, learning processes, knowledge, and directionalities—this would enable us to understand the role of the university in inclusive innovation. This paper argues that innovation dynamics should be understood from a systemic perspective and using computational modeling and simulation methods, so that the inherent complexity of these systems can be taken into account. The analysis of innovation scenarios based on a formal theoretical model and its operationalization through computer simulation should contribute to the understanding of the role of the university in these system dynamics, which can be used to propose effective strategies to strengthen its participation. Therefore, this paper proposes a formal systemic agent-based conceptual model that can be used to study the role of the university in inclusive innovation and establish guidelines to improve its performance. This study implemented standard computer modeling and simulation, specifically adapted for agent-based modeling. The results obtained from the simulation scenarios were comparatively analyzed using statistical tests (ANOVA and Tukey) to determine the presence of statistically significant differences. As the main finding of the research, the proposed conceptual model was validated and proved to be useful for studying the role of the university in reducing social exclusion in the Global South, through the design and execution of computer simulation scenarios.

Statistical analysis and modeling of wrinkle defects in continuous-fiber composite forming processes

Wrinkles are detrimental to the performance of composite components. The prediction of wrinkling during forming is challenging because of complex material interactions. Current computational modeling methods are capable of wrinkle prediction but only for one given set of parameters per simulation cycle. To improve the prediction of wrinkles, this article presents a theory that correlates wrinkle formation with factors such as composite layup and part dimension. This theory allows one to perform design of experiments for physical or virtual forming tests and obtain binomial probability distributions capable of wrinkle prediction. The theory is validated with forming experiment data and demonstrates its potential in wrinkle prediction.

AI anxiety? Comparing the sociotechnical imaginaries of artificial intelligence in UK, Chinese and Indian newspapers

A growing body of work insists that artificial intelligence (AI) must be regarded as a sociotechnical imaginary in the manner of earlier technologies. In this paper we contribute to this field by investigating the representation of AI across very different countries and across time to assess whether these representations are influenced by their socio-economic, political, and cultural contexts, and whether they change over time. By applying computational topic modelling methods, we investigate narratives around AI in 9 newspapers across the UK, China and India over a time frame of 12 years, from 2011 to 2022. Our results indicate that there are both dystopian and utopian narratives associated with AI, as indeed there have been with other technologies. Debates about AI tend very much to reflect national priorities, preoccupations, hopes and fears, all of which are projected onto emerging AI technologies.

VII congress of biophysicists of Russia

The problems and results of research in biophysics, which were devoted to the VII Congress of Biophysicists of Russia (Krasnodar, April 17-23, 2023, http://rusbiophysics.ru/db/conf.pl), are discussed. The results of fundamental and applied research in the field of molecular biophysics, cell biophysics, biophysics of complex multicomponent systems were presented at plenary, sectional and poster sessions. The structure and dynamics of biopolymers, the fundamental mechanisms underlying the impact of physicochemical factors on biological systems, membrane and transport processes were actively discussed. Much attention was paid to new experimental methods of biophysical research, methods of bioinformatics, computer and mathematical modeling as essential research tools at all levels of organization of living systems. Along with the fundamental problems of studying the biophysical mechanisms of regulation of processes at the molecular, subcellular and cellular levels, much attention was paid to applied research in the field of biotechnology and environmental monitoring. Works in the field of medical biophysics were especially widely represented. During the Congress, the National Council for Biophysics was formed.

Computational study of the mechanical behavior of the astrocyte network and axonal compartments in the mouse optic nerve head.

Glaucoma is a blinding disease characterized by the degeneration of the retinal ganglion cell (RGC) axons at the optic nerve head (ONH). A major risk factor for glaucoma is the intraocular pressure (IOP). However, it is currently impossible to measure the IOP-induced mechanical response of the axons of the ONH. The objective of this study was to develop a computational modeling method to estimate the IOP-induced strains and stresses in the axonal compartments in the mouse astrocytic lamina (AL) of the ONH, and to investigate the effect of the structural features on the mechanical behavior. We developed experimentally informed finite element (FE) models of six mouse ALs to investigate the effect of structure on the strain responses of the astrocyte network and axonal compartments to pressure elevation. The specimen-specific geometries of the FE models were reconstructed from confocal fluorescent images of cryosections of the mouse AL acquired in a previous study that measured the structural features of the astrocytic processes and axonal compartments. The displacement fields obtained from digital volume correlation in prior inflation tests of the mouse AL were used to determine the displacement boundary conditions of the FE models. We then applied Gaussian process regression to analyze the effects of the structural features on the strain outcomes simulated for the axonal compartments. The axonal compartments experienced, on average, 6 times higher maximum principal strain but 1800 times lower maximum principal stress compared to those experienced by the astrocyte processes. The strains experienced by the axonal compartments were most sensitive to variations in the area of the axonal compartments. Larger axonal compartments that were more vertically aligned, closer to the AL center, and with lower local actin area fraction had higher strains. Understanding the factors affecting the deformation in the axonal compartments will provide insights into mechanisms of glaucomatous axonal damage.

REALIZATION OF THE LAWS OF DYNAMIC SYMMETRY IN THE DESIGN OF THE ENVIRONMENT BY MEANS OF COMPUTER THREE-DIMENSIONAL MODELING

The purpose of the study is to determine the possibilities of applying the laws of dynamic symmetry in the design of the environment through computer three-dimensional modeling.&#x0D; Methodology. The study used methods of historical and logical analysis of the problem of the role of dynamic symmetry in nature, art, and design; methods of comparison, comparison, and generalization. According to the transdisciplinary approach, dynamic symmetry is considered as a phenomenon of nature, a concept of geometry, and a principle of forming in design. &#x0D; Results. The concepts of "dynamic symmetry" and "phyllotaxis" in the research of D. Hambidge have been analyzed, and the peculiarities of using the above categories to analyze the proportions of art and design objects have been revealed. It has been established that modern methods of computer modeling make it possible to use the laws of dynamic symmetry in environmental design. On the examples of design developments of interior elements (lighting fixtures, spiral staircases), the methods of combining the patterns of change in the shape and size of model elements, orientation in space, and on the plane have been shown. &#x0D; The scientific novelty of the research is in the implementation of approaches to the laws of dynamic symmetry in the design of the environment through computer three-dimensional modeling. &#x0D; Practical significance. The results of the study can be used in the study of the disciplines "3D modeling in environmental design", "Fundamentals of computer design", "Environmental design", "Design of furniture and equipment".

Electrochemical explorations, SEM/EDX analysis, and quantum mechanics/molecular simulations studies of sustainable corrosion inhibitors on the Cu-Zn alloy in 3% NaCl solution

Essential oils (EOs) have recently gained attention as sustainable corrosion inhibitors due to their biodegradability, environmental friendliness, low cost, and easy availability. This paper aimed to investigate the synergistic impact of the active green molecules from two essential oils (EO16/EO18) derived from A. visnaga (L.) Lam on the corrosion of Cu-Zn alloy (brass) in a 3% NaCl environment using open circuit potential (OCP), potentiodynamic polarization (PDP), and electrochemical impedance spectroscopy (EIS), surface morphology analysis and computer modelling methods, including density functional theory (DFT) and Monte Carlo (MC) simulations. The electrochemical results demonstrated show that EO18 and EO16 at an ideal concentration of 2 (g/L) and after 6 h of immersion at 298 K, exhibited high inhibition efficiencies (ƞPDP(%), reaching a maximum of 93.04% and 84.57%, respectively. Moreover, the PDP experiments revealed that EO16 and EO18 acted as mixed inhibitors, with a predominant cathodic inhibitory behavior. Additionally, EO16 and EO18 have strong inhibitory efficacy throughout a wide temperature range (298–338 K), especially EO18. Also, the thermodynamic parameters showed a good relationship with the inhibition efficiency of the EO16/EO18. The examination of surface morphology (SEM/EDX) indicated less degradation of the metallic surface in the presence of both EOs in a corrosive environment, with the formation of resistant protective films, particularly notable for EO18. These findings confirmed the adsorption of EOs molecules onto the active sites of the brass surface, facilitated by the synergistic action of various phytochemical elements, including oxygenated monoterpenes/sesquiterpenes (minor compounds) and major compounds (Croweacin/Linalool). Furthermore, computational investigations utilizing DFT and MC simulations revealed a significant adsorption of EO18/EO16 compounds (both major and minor components) onto Cu (111) and Zn (110) surfaces. The MC simulation results further demonstrate that the synergistic effect of EO18 compounds on Cu/Zn surfaces results in the formation of stronger and more protective layers compared to the synergistic effect of EO16 compounds. These computational findings align well with the results obtained from experimental inhibition efficiencies. The combination of experimental and computational approaches enhanced our understanding of the corrosion inhibition mechanism and underscored the potential of EOs as sustainable and effective corrosion inhibitors. These findings can serve as a useful guideline for future research aimed at investigating the synergistic inhibitory impact of the components present in the green extracts.

Overview of Research of Machine Learning in Air Traffic Management

As the demand for air traffic has grown at a fast pace in recent years, the efficiency and safety of air traffic management is facing greater challenges with limited airspace resources. As an important part of the civil aviation air traffic system, the existing air traffic management capability is no longer able to meet the demand of air traffic growth. Machine learning, as an advanced method of current computer modelling, has shown good application value and promise for application in air traffic management. This paper starts by introducing the application methods and modelling process for machine learning in air traffic management, followed by the current research status of machine learning in three areas, namely air traffic flow management, air traffic services and airspace management, respectively, and finally points out the challenges and further development outlook of applying machine learning to air traffic management. Overall, the introduction of machine learning into air traffic management represents a major trend with significant implications for its development.