Theoretical Mathematical Model Research Articles

Effects of chip breaker groove and tool nose radius on progressive tool wear and behavior when turning GH3536 nickel-based superalloys

Nickel-based superalloys are typically challenging to machine and often exhibit severe tool wear during the turning process, thus attracting substantial research attention. This study not only explores the tool wear mechanisms when turning GH3536 nickel-based superalloys but also introduces a focus on the role of tool geometry configuration. Tools with different chip breaker groove structures and nose radii were investigated, with results indicating the tool wear mechanisms were predominantly abrasive and adhesive. For the 04HA chip breaker, which has a 9° rake angle and a "V"-shaped bottom, there is a single point of impact with the chip during cutting, while the 04VP3 chip breaker, with a 15° rake angle and a flat bottom, has two points of impact with the chip during cutting, increasing chip deformation and favoring chip breaking, thereby suppressing the formation of built-up edge. Additionally, changes in the nose radius influenced the degree of work hardening and consequently influence the severity of notch wear, with the nose radius also impacting the chip flow angle. Based on this finding, a theoretical mathematical model describing the initial chip flow angle was established and validated. Additionally, an examination of the effects of varying operating parameters on tool life was conducted, revealing that cutting speed was the most significant factor affecting tool life, followed by feed rate and depth of cut.

Research on optimization of basic rail top bending prediction model

Since the basic rail of the switch needs to have a certain bending angle when the train changes direction, top bending is an important link in the production process of the basic rail. The three-point pressure top bending method is simple, flexible and widely used. In this study, the traditional three-point pressure bending is optimized, the influence of the pick width in the model is considered, a corresponding rebound model is established, and the model is applied to the pressure bending process of the basic rail. The bilinear strengthening model of the material was used to construct the bending moment expressions at different positions during the top bending process, and the relationship between the load and bending deflection in the elastic stage and elastic-plastic stage was obtained. The final top bending prediction model was obtained by combining the load-deflection model in the bending stage and the rebound stage. The correctness of the theoretical mathematical model was verified by establishing finite element simulations, and the theoretical calculation results were compared with the experimental results. The results showed that the top bend prediction optimization model established in this study had high feasibility and met the machining accuracy requirements.

A narrative review on Quantum Finance Theory

This paper systematically reviews the historical development trajectory and current situation of quantum financial theory research through an in-depth review of relevant literature. This paper summarizes the history of the concept of quantum finance, elaborates on the development stages of quantum finance, and explains the contributions of representative work in different periods in various aspects, such as theoretical frameworks, mathematical models, and algorithmic tools, which is of great significance to the research path of quantum finance. In addition, the paper introduces in detail the research progress of domestic and foreign body problems, such as quantum algorithm optimization of investment portfolios, quantitative pricing of derivatives, quantum neural networks, etc., laying a good foundation for future in-depth study of this interdisciplinary subject. Overall, this paper systematically reviews the development history of quantum finance research, provides readers with a macro understanding framework by sorting out the progress in different research fields, and has significant reference value for interdisciplinary exchanges.

Effect of food environment on the ability of microorganisms to form biofilms

AbstractThe aim of this study is to understand the effect of the food product's, even in trace amounts, in the biofilm formation potential of Escherichia coli and Staphylococcus aureus. Bacterial adhesion was tested using three different food media, in which bacterial cells were adapted before determining how this would affect their adhesion score and biofilm formation potential: sterilized milk, mineral water, and pasteurized commercial orange juice. After characterization of the bacteria and the adhesion support by the contact angle, the adhesion score obtained by the theoretical mathematical model XDLVO developed by van Oss was compared to that obtained by the experimental approach, in order to examine the role of non‐specific (thermodynamic) and specific (biological) interactions in adhesion. Changes in the membrane composition of bacteria, as a function of acclimation conditions, were evaluated by infrared spectroscopy. Results reveal variability in the spectral region between 900 and 1200 cm−1 specific to capsular polysaccharides (CPS) or lipopolysaccharides (LPS), and also in the range between 2800 and 3000 cm−1 characteristic of membrane lipids and phospholipids. Biofilm formation by S. aureus and E. coli was affected by commercial UHT milk. This led to increased hydrophobicity and total adhesion energy within the environment of these strains. However, no correlation was observed between the adhesion scores obtained through experimental and theoretical approaches. This underscores the necessity of considering the specific characteristics of individual food products in understanding the phenomenon of biofilm formation. Understanding, the mechanism, the variations in bacterial surface characteristics, and the conditions that favor or disadvantage the formation of biofilms, may have important implications, in the development of preventive and curative strategies, to control of food‐borne infections.

Организация энергетического и информационного взаимодействия берегового поста управления с гнпа при обслуживании подводных добычных комплексов

The creation of underwater stations and resident vehicles for use in underwater conditions, as well as technologies that provide the full range of measures necessary for the placement, maintenance, bringing, reception and release of an underwater vehicle is a new direction in underwater robotics. When developing such systems, it is necessary to solve a number of tasks, some of which are traditional, for example, the tasks of approaching an underwater vehicle to the station and underwater docking. Other tasks are new, such as providing contactless battery charging and high-speed contactless information exchange between the station and the device, as well as organizing high-speed communication of the station with a remote coastal control post. The domestic experience of research on such tasks is limited to solving particular issues. The representation of the energy and information interaction system of an underwater service station, an uninhabited underwater vehicle and a coastal control post in the form of a single complete complex is unknown from the literature. For this reason, mention task is relevant, the solution of which will ultimately improve the efficiency of hydrocarbon production. The present functional diagram, which combines the channels of energy and information interaction into a single complex. The principle of constructive implementation of the basic elements is defined. Accepted research methods include interrelated theoretical analysis, mathematical modeling, and experimental research.

Brownian motion based multi-objective particle swarm optimization methodology and application in binary classification

The particle swam optimization (PSO) method has been widely applied in evolutionary computation and related industrial areas. However, the optimization performance of traditional PSO is usually influenced by particle updating displacement and velocity, which is randomly distributed and makes the solution solving process inefficient. To deal with this challenge, a Brownian particle motion and Euler-Maruyama (EM) methodology-based model is proposed to improve the performance of the multi-objective PSO. The EM based PSO principles and algorithm framework are introduced by theoretical analysis and mathematical modeling, and then the optimization performance of four comparing PSO models is tested, while the feasibility of the new PSO4 is verified by typical benchmark problems. Afterwards, the characterizations of the EM based PSO method are investigated from the perspective of particle motion mechanism analysis, binary classification application and various optimization performance comparison. The result manifests that the new PSO4 features higher accuracy and less running time among 24 related optimization methods for 4 typical standard datasets of Ionosphere, Breast, Diabetes and Wine. Finally, the multi-objective optimization and classification application potential of the Brownian motion and EM based PSO4 is demonstrated, and the future work regarding the new PSO model is discussed.

Analysis and integration of mixed method in efficiency studies: Best practices and applications in the renewable energy sector

Mixed-method research is based on multiple methodological strategies, prioritizing high technical rigor with scientific solidity and contributing to practical improvements. Therefore, this study aimed to present a mixed method with analysis and integration in efficiency studies in the renewable energy sector through the relationship between productivity and optimization of results. This mixed-method methodology was proposed in: (i) theoretical mathematical model, (ii) efficiency model (DEA, SFA), technique, definition of ranking and sampling criteria, (iii) variables that influence this efficiency, which will be the bases for research instrument applied at case studies (quanti-quali integration), and (iv) identification of best practices from case studies. The efficiency of each DMU established a ranking (scores), which was used as independent variable in the regression and identified the variables that impact on efficiency. These results enabled the creation of an instrument to identify the best practices that impacts on the efficiency score. Notably, this method can help stakeholders of the sugar-energy production chain achieve their efficiency through best practices generated. It has been applied in other economic sectors, such as education, banking, healthcare, and solid waste, and replication in different periods and countries using variables from public or private databases.•Develop stakeholders of sugar-energy production chain to achieve and improve their efficiency through best practices generated through production analysis.•It has been applied in other economic sectors, such as education, banking, healthcare and solid waste.•There are opportunities for replication in different periods and countries using variables from public or private databases.

Pyrolytic characteristics of abutilon stalk waste using TG-FTIR, Py-GC/MS, and artificial neural networks: Kinetics, thermodynamics, and gaseous products distribution

The physicochemical properties of abutilon stalk (ABUS) were investigated to assess its suitability for pyrolytic conversion into bioenergy and bio-based chemicals based on kinetic triplet state, thermodynamic parameters, decomposition mechanisms and product distributions. The pyrolysis behavior of ABUS was studied using a thermogravimetric analyzer at five heating rates in a nitrogen atmosphere. The Gaussian deconvolution function indicated that the pyrolysis process of ABUS can be successfully modeled as four parallel devolatilization events (R2 > 0.99), divided into the devolatilization of pseudo drying (PO-D), hemicellulose (PO-H), cellulose (PO-C), and lignin (PO-L), respectively. The average apparent activation energies were 34.89 kJ/mol for PO-D, 163.02 kJ/mol for PO-H, 169.55 kJ/mol for PO-C, and 107.51 kJ/mol for PO-L using four model-free methods (Ozawa-Flynn-Wall, Kissinger-Akahira-Sunose, Starink, and Tang). The pyrolysis process was accurately predicted using diffusional, power law, geometrical contraction, and order-based reaction mechanism models. Thermal degradation process for each devolatilization event was successfully reconstructed using combined kinetics (R2 > 0.96), providing a useful theoretical basis and mathematical model for predicting the pyrolysis behavior of ABUS. The volatile products produced by ABUS pyrolysis were characterized using an integrated TGA-FTIR and Py-GC/MS system, confirming the proportion of high-energy compounds (benzenes, phenols, ketones, alcohols, aldehydes, acids, esters, hydrocarbons and their derivatives, N-heterocyclic substances, and N-containing compounds) and gas emission levels (CO > CH4 > C-O > H2O > CO2 > CO > C-O(H) > HCN > NH3 > SO2). Artificial neural network (ANN) was used to predict the pyrolysis behaviors of ABUS, and the best ANN model was ANN (5 *11 *1) with high R2 of 0.99998. Based on the kinetics, thermodynamics, and gaseous products distribution, ABUS has great application potential as raw material for bioenergy production.

Resistance bias estimation of a liquid column in a cylindrical conductivity cell with lateral liquid supply

The article shows a physical model of a cylindrical two-electrode conductivity cell with inlet and outlet holes for filling located perpendicular to the cell axis. Based on the finite element method (FEM), the non-uniformity of the current density distribution inside the cell was determined. For a range of geometrical parameters of the cell, the resistance biases of the liquid column with respect to the idealized model—a cell with a uniform current density distribution (without holes) are calculated. A mathematical expression is given that describes calculating the electrolytic conductivity value using the geometrical parameters of a conductivity cell, taking into account the field distortion caused by holes for filling. It has also been found that at the ratio of the cell diameter to the hole diameter D/d ≥ 5, the entire field distortion inside the cell is provided by a liquid column in the holes with a length of only (h ≤ d) mm. Theoretical estimates and mathematical models covered in this article were used to create the primary differential conductivity cell. Structurally, such a cell consists of two tubes of the same diameter but of different lengths, at the edges of which platinized electrodes are placed.

Connecting theory and experiment in cell and tissue mechanics.

Understanding complex living systems, which are fundamentally constrained by physical phenomena, requires combining experimental data with theoretical physical and mathematical models. To develop such models, collaborations between experimental cell biologists and theoreticians are increasingly important but these two groups often face challenges achieving mutual understanding. To help navigate these challenges, this Perspective discusses different modelling approaches, including bottom-up hypothesis-driven and top-down data-driven models, and highlights their strengths and applications. Using cell mechanics as an example, we explore the integration of specific physical models with experimental data from the molecular, cellular and tissue level up to multiscale input. We also emphasize the importance of constraining model complexity and outline strategies for crosstalk between experimental design and model development. Furthermore, we highlight how physical models can provide conceptual insights and produce unifying and generalizable frameworks for biological phenomena. Overall, this Perspective aims to promote fruitful collaborations that advance our understanding of complex biological systems.

Passive Electrical and Optical Methods of Ultra-Short Pulse Expansion for Event Timer-Based TDC in PPM Receiver

The energy efficiency of a communication system using pulse position modulation (PPM) can be increased by reducing the duration of the pulses transmitted over the communication channel to several tens of picoseconds. The employment of an event timer device as a time-to-digital converter (TDC) for demodulation allows the use of PPM with many pulse positions and achieves competitive data transfer speeds. However, along with several-picosecond accuracy of modern event timers, they require a pulse duration of several hundred picoseconds for precise detection. This research is devoted to developing passive techniques for precise pulse expansion from tens of picoseconds to hundreds of picoseconds. We propose two methods: the electrical method, which employs a microstrip low-pass filter (LPF), and the optical method, which uses fiber Bragg grating (FBG). This research offers a detailed analysis of distortion-free pulse expansion requirements, the design of prototypes meeting these requirements, and experimental design verification. Theoretical background, mathematical models, and results of experimental validation of the proposed pulse expansion methods within the laboratory transmitted reference pulse-position modulation (TR-PPM) communication system are provided.

Type series of resonant vibration equipment for concrete mixtures compaction and its main parameters calculation method

Introduction. Increasing the energy efficiency of vibration technology poses a number of challenges for its developers. A promising direction for reducing energy costs for driving vibration machines is the use of the resonance phenomenon. Due to the dynamic properties of the oscillatory system, it is possible to significantly reduce the power consumption of resonant vibration equipment, and in some cases, improve the quality of the products. The purpose of this article is to develop a standard range of resonant vibration equipment for compacting concrete mixtures and a methodology for calculating its main parameters.The object of research is the oscillatory system of a vibrating machine, consisting of two masses connected by the elastic and dissipative elements. In addition, the first mass is connected to a fixed base through elastic and dissipative elements.Materials and methods. The basic principles of theoretical mechanics, mathematical modelling and statistical processing of results were used in the research.Results. According to the research results, it was established that with an increase in the mass ratio of the oscillatory system, the dynamic coefficient decreases, and the width of the resonant zone increases. The regression equations are given. It has been established that with increasing rigidity of the working body, the dynamic coefficient decreases, and the horizontal section on the frequency response, the width of which does not change significantly, shifts to the region of higher frequencies. With an increase in the damping coefficient, the dynamic coefficient decreases, and the width of the resonant zone and the frequency range practically do not change. A wide range of nomenclature and masses of precast reinforced concrete justifies the development of a standard-size range of resonant vibration equipment. A standard range of light (up to 2 tons), medium (2-6 tons) and heavy (6-10 tons) types has been developed. Based on the analysis and generalization of research results, a method for calculating resonant vibration equipment for compacting concrete mixtures has been developed, which makes possible to increase its energy efficiency.

A low-power low-noise amplifier with high CMRR for wearable healthcare applications

Low-noise and low-power operation are important parameters of amplifier intended for wearable healthcare applications. However, along with these parameters the amplifier must be able to deliver high Common Mode Rejection Ratio (CMRR) for mitigating the effect of 50/60 Hz interference. This paper reports capacitively coupled-capacitive feedback amplifier based on Self Cascode Current Mirror-II (SCCM-II) Operational Transconductance Amplifier (OTA). The obtained results for proposed SCCM-II OTA based amplifier shows gain of 39.79 dB, input referred noise of 2.64 μVrms, bandwidth of 0.794-343.4 Hz, area consumption of 0.154 μm2, power consumption of 2.54 μW with ±0.9 V supply. The noise efficiency factor and CMRR values for proposed amplifier are 3.87 and 122.1 dB, respectively. In-depth theoretical interpretations and mathematical modeling of amplifier are validated in 0.18μm standard CMOS process using BSIM3V3 MOS device models. The Gaussian distribution plots of proposed amplifier for 200 Monte-Carlo iterations infer mean values for gain and CMRR are 39.79 dB and 112.96 dB which are very close to their nominal values. The proposed amplifier has been compared with conventional approach as well as with literature and improvement in results is observed in terms of input referred noise, power and CMMR. These advantages of the proposed amplifier make it suitable for non-invasive wearable healthcare applications.

Wygd Study on prediction method of drilling pressure in low permeability oilfield

During the high water cut development stage of low permeability in M block, the overall recovery degree is low, there has 100 infilling wells been drilled since 2020 to improve the development effect. Before drilling, in order to avoid the risk of pressure safety, it is necessary to shut down the peripheral water injection wells to relieve the pressure, at the same time.Since the M Block reservoir is a low permeability reservoir, the pressure relief time after drilling off is long, which increases the impact on the production of developing wells and increases the fluid transportation and the related production costs, while there is no uniform standard for the time, pressure and distance of drilling off. In order to control the production loss of developing wells and save the shut-in cost. This paper applies numerical simulation and reservoir engineering method, by establishing fine geological model, optimizing simulation technology, considering the influence of start-up pressure gradient, establishing theoretical formula and mathematical model of drilling pressure, clarifying the law of drilling-off pressure relief and pressure drop, calculating the formation pressure of single well in the whole area, forming the drilling-off limit and rapid pressure prediction method, through comparison and verification, the overall deviation is 3.8%, and the method is accurate and reliable.

A mathematical model for the motion of a micro-robot consisting of three spheres linked with axially aligned retractable arms in Stokes flow that gives expressions for mean distance moved, mean drift velocity and energy efficiency of the non-reciprocal cyclic swimming motion

The problem studied was the non-reciprocal cyclic swimming motion of three spheres linked with axially aligned retractable arms in Stokes flow. The arms are assumed to be able to retract at a steady speed to half their length, and then at a later time in the motion extend at the same steady speed back to their original length. It is important in the field of medical biology, because this motion could enable a micro-robotic device to swim within the arterial and cellular fluid to perform medical biological procedures such as surgery or drug delivery. The method used was the development of a theoretical mathematical model in low Reynolds number Stokes flow, that takes the mathematical expression for steady motion of a sphere, and from this obtains the motion for three spheres by assuming leading order interactions between the spheres. This gives an interaction matrix from which the important results of the study are obtained which are exact mathematical expressions for the mean distance travelled, mean drift velocity and energy efficiency of the motion. These are that the mean distance travelled is 3a0ln(9/8), the mean drift velocity is (3a0/2A)ln(9/8), and the energy efficiency of the motion is 9a02/(4A2)[ln(9/8)]2, where a0 is the sphere radius and A is the arm length. The conclusions to be drawn from the results are that the most efficient design has the largest ratio for sphere radius in relation to arm length. The novelty of the work is that it gives exact mathematical expressions for distance travelled, velocity and energy efficiency not given previously in the literature.

Microstructure design and optimization of high-sensitivity interdigital capacitive humidity sensor based on uncertainty analysis

The microstructure of the interdigital capacitive humidity sensor plays a critical role in achieving high sensitivity in measurements. Performing a quantitative evaluation to assess the influence of structural parameters on sensor sensitivity is vital for improving the overall performance of the sensor. In this paper, a theoretical mathematical model and a structural uncertainty propagation model are developed for the interdigitated capacitive humidity sensor, considering the uncertain errors that occur during the processing and measurement processes. A quantitative analysis is conducted to investigate the impact of each structural component and its corresponding uncertainty on the output sensitivity. Moreover, by integrating the two constructed models, the paper formulates an optimal equation for sensor performance that mitigates the impact of structural errors on the system while improving sensor sensitivity. Researchers have the flexibility to adjust sensor material, environmental conditions, structural components, and other parameter variables within the model, according to specific application requirements, to achieve the optimal design scheme for a sensor with specific materials and structure. The proposed method and model presented in this article have undergone verification using finite element physical simulation. The sensitivity model achieves an accuracy rate of 94.45%, whereas the uncertainty model achieves an accuracy rate of 81.96%. Additionally, the model reduces the calculation time to 1/550th and the required computational resources to 1/5th of the simulation, thereby greatly improving the efficiency of sensor optimization design.

Is constant needle motion during soft tissue filler injections a safer procedure? A theoretical mathematical model for evaluating patient safety.

The safety rationale behind the constant needle motion injection technique is based on the assumption that due to the constant needle motion and simultaneous soft tissue filler material administration a smaller amount of product per area may be injected into an artery if an artery within the range of the moving needle is inadvertently entered. To perform mathematical calculations for determining the probability for causing intra-arterial product administration when constantly moving the needle during facial aesthetic soft tissue filler injections. This study was designed as a theoretical investigation into the probabilities for causing adverse events due to intravascular injection of soft tissue filler material when constantly moving a 27-G needle during facial soft tissue filler administration. It was revealed that with a higher number of conducted injection passes a greater soft tissue area can be covered by the needle. The odds of encountering an artery within the covered soft tissue volume and the odds of injecting any volume greater than zero into the arterial blood stream increases with the number of performed injection passes. This increase is greatest between 1 and 10 performed injection passes. This model demonstrates that the constant needle motion technique increases the probability of encountering an artery within the treatment area and thus increases the odds for intra-arterial product administration. The constant needle motion technique does not increase safety but rather may increase the odds of causing intra-arterial product administration with the respective adverse consequences for the patient.

Machine Learning

Abstract: Machine learning refers to the study and development of machine learning algorithms and techniques at a conceptual level, focusing on theoretical foundations, algorithmic design, and mathematical analysis rather than specific implementation details or application domains. It aimsto provide a deeper understanding of the fundamental principles and limitations of machine learning, enabling researchers to develop novel algorithms and advance the field. In abstract machine learning, the emphasis is on formalizing and analyzing learning tasks, developing mathematical models for learning processes, and studying the properties and behavior of various learning algorithms. This involves investigating topics such as learning theory, statistical learning, optimization, computational complexity, and generalization. The goalis to develop theoretical frameworks and mathematical tools that help explain why certain algorithms work and how they can be improved. Abstract machine learning also explores fundamental questions related to the theoretical underpinnings of machine learning, such as the trade-offs between bias and variance, the existence of optimal learning algorithms, the sample complexity of learning tasks, and the limits of what can be learned from data. It provides a theoretical foundation for understanding the capabilities and limitations of machine learning algorithms, guiding the development of new algorithms and techniques. Moreover, abstract machine learning serves as a bridge between theory and practice, facilitating the transfer of theoretical insights into practical applications. Theoretical advances in abstract machine learning can inspire new algorithmic approaches and inform the design of real-world machine learning systems. Conversely, practical challenges and observations from realworld applications can motivate and guide theoretical investigations in abstract machine learning. Overall, abstract machine learning plays a crucial role in advancing the field of machine learning by providing rigorous theoretical frameworks, mathematical models, and algorithmic principles that deepen our understanding of learning processes and guide the development of more effectiveand efficient machine learning algorithms.

Algorithm for Automatic Rod Feeding and Positioning Error Compensation for Underground Drilling Robots in Coal Mines.

In the pursuit of automating the entire underground drilling process in coal mines, the automatic rod feeding technology of drilling robots plays a crucial role. However, the current lack of positional accuracy in automatic rod feeding leads to frequent accidents. To address this issue, this paper presents an algorithm for compensating positioning errors in automatic rod feeding. The algorithm is based on a theoretical mathematical model and manual teaching methods. To enhance the positioning accuracy, we first calibrate the pull rope sensor to correct its measurement precision. Subsequently, we establish a theoretical mathematical model for rod feeding positions by employing spatial coordinate system transformations. We determine the target rod feeding position using a manual teaching-based approach. Furthermore, we analyze the relationship between the theoretical rod delivery position and the target rod delivery position and propose an anisotropic spatial difference compensation technique that considers both distance and direction. Finally, we validate the feasibility of our proposed algorithm through automatic rod feeding tests conducted on a coal mine underground drilling robot. The results demonstrate that our algorithm significantly improves the accuracy of rod feeding positions for coal mine underground drilling robots.

Coupling dynamic model of vehicle-wheel-ground for all-terrain distributed driving unmanned ground vehicle

The establishment of a dynamic model of distributed driving unmanned ground vehicle (DDUGV) is the premise of vehicle state identification and high-quality control. However, due to the strong nonlinearity of the DDUGV system itself, the various external influence and the difficulties of real-time calculating, the simulation results based on existing conditions are far from the actual vehicle test results, resulting in the huge gap between the simulation test and the actual engineering practice. Regarding the problem mentioned above, this paper takes the 3-axles DDUGV with variable structure as the research object, and proposes a universal dynamic modeling method for DDUGV, which considers the uncertainty of terrain and variable structural parameters. Firstly, based on Newtonian mechanics, mathematical models of the vehicle's variable structure, body posture, static/dynamic suspension, hydraulic transmission, wheel and in-wheel motors are established. Secondly, taking the wheel as the coupling node between vehicle dynamics and ground mechanics, 3 tire models in typical driving environments are established, which are the magic tire model used in paved road, and the Bekker-Wong-Janosi theoretical model used in sandy and clay soil, respectively. Thirdly, based on the established mathematical model, a universal DDUGV modeling method is proposed. Then, based on the co-calibration of geotechnics and discrete element method (DEM), the particle parameters of sandy soil are obtained and the theoretical mathematical model of wheel-soil interaction is verified and optimized. Finally, based on the established vehicle-wheel-ground coupling model of the 3-axles DDUGV with variable structures, simulation tests are carried out under typical working conditions, which are compared with the results of existing vehicle dynamics software to verify the accuracy, computation efficiency and real-time property. The vehicle-wheel-ground coupling dynamic model established in this paper can provide a reference for the construction of the vehicle dynamic models, the analysis of driving characteristics, and the research of control strategies in complex environment.