A simulation and optimization design method and system for EMU snow plows based on multi-physics field coupling

Abstract

This application discloses a simulation and optimization design method for EMU snow plows based on multi-physics coupling. This method achieves efficient snow plow design through a three-stage process: physical modeling, experimental verification, and parameter optimization. The method collects environmental parameters for line snowfall and snow density, establishes a soft-ball dynamics model for snow particles based on the Hertz-Mindlin with JKRCohesion contact model, and combines this with a discrete phase model to simulate the snow-covering process on a wall. The simulation accuracy is verified through snow removal experiments. A parameterized snow plow model is constructed, and shape parameters are introduced to modify the driving resistance coefficient, evaluate snow removal performance, and select the optimal geometric parameter combination. The vehicle model is integrated with the front end, and the snow removal resistance formula under multi-body coupling is modified to generate snow plow selection recommendations. Simulating different snow depth and speed conditions, a correlation model is established, and the maximum safe speed threshold that meets operational constraints is output. Through multi-physics coupling simulation and parameter optimization, the limitations of traditional design reliance on experience are overcome, achieving coordinated optimization of snow removal efficiency, driving safety, and structural strength.

Description

Technical Field

[0001] The present invention relates to the technical field of rail vehicle obstacle removal, and in particular to a method and system for simulating and optimizing the design of a snow plow for clearing obstacles on an EMU based on multi-physical field coupling. Background Art

[0002] Frequent snowfalls significantly restrict railway transportation capacity in high-altitude and cold regions. The impact of snow on rail transport is primarily due to the resistance created by snow removal, which impacts train operation, and the environmental impact of snow removal, such as deepening snow on adjacent railway tracks. When high-speed trains travel on snow-covered routes, accumulated snow along the track collides with the train body. The resulting snow resistance in the direction of travel increases the strain on the train's power system. The snow removal process can also affect the train's wheel load, reducing stability and comfort, and in severe cases, posing a risk of overturning.

[0003] At present, the most common method to deal with snow accumulation on the tracks is to use a snow plow device at the front of the train to clear the snow above the rails into the snow ditch to ensure the train can continue to run. The snow plow is a plow-type snow removal device installed at the front of the train. This device is an important structure for snow protection during driving. In recent years, the structural design of the plow-type snow removal device has become an important part of the structural design of railway locomotives in some countries. In the early stage of snowfall, when the depth of the snow does not seriously affect driving safety, the train with a plow-type snow removal device installed at the front can smoothly clear the snow above the rails into the snow ditch, thereby ensuring the train can continue to run. Such autonomous snow removal measures can effectively improve the transportation efficiency of trains under snowy conditions, thereby greatly reducing the negative impact caused by snowfall.

[0004] However, existing snowplow designs rely primarily on experience and physical testing, lacking in-depth research into the mechanical mechanisms of snow removal under varying snow depths, vehicle speeds, and snowplow geometries. This approach is not only costly and time-consuming to test, but also makes it difficult to adjust and optimize the design in response to rapidly changing operating conditions. In particular, in conditions of deep snow or high-speed train travel, snow removal resistance can suddenly increase, causing wheel load reduction to exceed safety design standards and pose a serious threat to train safety.

[0005] Therefore, there is an urgent need for a multi-physics field coupling simulation and analysis method for the design of snow plow shape and the determination of vehicle speed limit. Summary of the Invention

[0006] Purpose of the invention: In order to overcome the above shortcomings, the purpose of the present invention is to provide a method and system for simulating and optimizing the design of snow plows for EMUs based on multi-physics field coupling.

[0007] To solve the above technical problems, the present invention provides a simulation and optimization design method for a snow plow for EMUs based on multi-physics field coupling, comprising:

[0008] Step S1: Collect the snow environment parameters of the target route, which include at least snowfall data and snow accumulation data; Step S2: Establish a soft ball model of snow particles and use the Hertz-MindlinwithJKRCohesion contact model to calculate the contact force between snow particles; Step S3: Based on the wall snow cover criterion and using a discrete phase model to simulate snow cover, and then verify the correctness of the snow removal resistance formula in the snow cover simulation by comparing with the preset snow removal experimental data; Step S4: Use a parametric modeling method to establish a snow plow model, and introduce shape parameters into the snow removal resistance formula based on the simulation results. Simplify the driving resistance coefficient, and then evaluate the snow removal performance of the snow plow under the preset shape parameter combination, and determine the snow removal resistance formula of the snow plow; Step S5: Add the front end to the snow plow model to form a whole vehicle model and simulate according to the whole vehicle model, and then correct the snow removal resistance formula and generate snow plow selection suggestions; Step S6: Simulate at a preset snow depth environment and driving speed to obtain the vehicle force and snow removal path under different snow depths and driving speed conditions, and then generate the maximum speed that can meet the actual operation requirements under different snow depth conditions according to the preset constraints; Step S7: Output the optimal design parameters of the snow plow.

[0009] Among them, the target line is selected by the test personnel according to actual needs.

[0010] In one aspect, in step S2, the method for establishing a soft ball model of snow particles includes: the soft ball model simplifies the contact process between particles into the damped vibration of a spring oscillator, and its motion equation is: Where x is the displacement from the equilibrium position, m is the mass of the oscillator, c and k are the spring damping coefficient and elastic coefficient respectively;

[0011] Among them, the normal elastic coefficient k n for: Where E and v are the elastic modulus and Poisson's ratio of the particle material, respectively, R is the particle radius, and the subscripts i and j represent the particles i and j that are in contact, respectively. When particles i and j are of homogeneous materials and have equal particle sizes, then k n for Among them, the tangential elastic coefficient k t for: Among them, α is the normal overlap, G i and G j are the shear moduli of particles i and j, respectively. When particles i and j are of homogeneous materials and have the same particle size, k t for

[0012] On the one hand, the method for determining the damping coefficient includes: if the spring oscillator with mass m is in the critical damping state, the mechanical energy decays at the fastest speed, and at this time the normal damping coefficient c n and the tangential damping coefficient c t They are:

[0013] Or, the damping coefficient is coupled with the restitution coefficient e:

[0014] Here, e is determined by experiment.

[0015] On the one hand, in step S2, the method of defining the Hertz-MindlinwithJKRCohesion contact model includes: the JKR normal force is based on the overlap δ and the interaction parameter, the surface energy γ: Where E* is the equivalent Young's modulus, R* is the equivalent radius; damping force The expression is: in, is the equivalent mass, is the normal component of the relative velocity, β and S n The expression is: Tangential force F t The expression is: Among them, S t is the tangential overlap, G* is the equivalent shear modulus, is the tangential component of the relative velocity; where the tangential force is affected by Coulomb friction μ s F n Limit, where μ s is the coefficient of static friction, and rolling friction exerts a torque on the contact surface: τ i =-μ r F n R i ω i , where μ r is the rolling friction coefficient, R i is the distance from the contact point to the center of mass, ω i is the unit angular velocity vector of the object at the contact point; the maximum gap between particles with non-zero cohesion is calculated by the following formula: When δ<δ c , the model returns 0; when the particles are not actually in contact and the interval is less than δ c When , the cohesion reaches its maximum value, so the maximum cohesion is: The force required to separate the two particles depends on the surface tension of the liquid γs and wetting angle.

[0016] On the one hand, in step S3, the discrete phase model includes: discrete phase model continuity and momentum conservation equations are: Among them, ρ f , u and P are the density, velocity and pressure of the fluid respectively, t is time, g is the acceleration of gravity, α f is the fluid volume fraction, Calculate, V c 、V p are the total volume of the control unit and the volume of the particle respectively; F f-p is the force between the air phase and the snow phase, mainly the particle drag force F drag ; τ is the fluid viscous stress tensor, Calculate, where I is the unit tensor, μ f is the fluid viscosity including kinematic viscosity and turbulent viscosity; for the snow phase, the snow particles are regarded as spheres, and the force balance equation is: Among them, m p 、u p and ρ p are the mass, velocity and density of snow particles respectively, and F is the additional external field; Among them, d p is the snow particle size, C D is the particle drag coefficient, which is calculated based on the smooth ball model by the particle Reynolds number R e,p It is determined by a set of empirical constants a1-a3 determined by the range; Calculate particle trajectories: The differential equation is solved along each coordinate direction to obtain the trajectory of the discrete phase; the traditional SIMPLE method is used to solve the control equation of the fluid phase, and then combined with the explicit time integration method to solve the motion equation of the particles in the flow field.

[0017] On the one hand, in step S3, the wall snow cover criterion includes: the incident angle α is less than the capture angle α t When the snow particles are captured by the wall; the local friction speed is lower than the preset threshold friction speed, the snow particles will not be carried away by the wind and will accumulate; the particle collision speed u p Less than the critical capture velocity u trap When the wall has a larger viscosity on the particles, the particles will be captured; when the particles meet the above three wall-covering criteria, the snow particles will be captured by the wall and stably accumulate on the wall to achieve wall-covering.

[0018] On the one hand, in step S4, the method of establishing a snow plow model using a parametric modeling method includes: step S41: selecting a horizontal plane, and determining the specific position and length of the line segment by the angle and the vehicle width; step S42: selecting the vertical symmetry plane of the snow plow, and determining the snow plow leading edge line segment and the angle of attack constraint line by the total height and the lower half height of the snow plow; step S43: generating the snow plow lower half plane from the determined line segment; step S44: projecting the bottom surface line to the snow plow upper edge plane by converting the entity reference to determine the reference plane where the rear spline curve is located; step S45: determining the rear spline curve by the suppression angle and the vehicle width, and the boundary conditions are tangent to the plane determined by the angle of attack, suppression angle constraint, and suppression angle height constraint; step S46: determining the front spline curve, and the boundary conditions are the protruding distance of the front end of the snow plow and tangent to the angle of attack plane and line segment; step S47: generating surfaces and planes to complete the modeling of half of the snow plow, and then mirroring the entity to complete the snow plow modeling.

[0019] On the one hand, in step S4, the method further includes: the snow removal resistance calculation expression is: F p =ChwρV 2 , where h is the snow removal depth, w is the snow removal width, ρ is the snow density, and V is the driving speed; C is the calculated driving resistance coefficient, C = C0 + C1 + C2, where C0 is linearly interpolated by looking up the preset table, and C1 = 0.028 (δ + 2γ) × 10 -4 , δ is the suppression angle, γ is the angle of attack; in C2: when θ<45°, C2={5.64+0.008×(45-θ)}×10 -4 When 45°≤θ≤90°, C2={5.64+0.0025×(θ-45)}×10 -4 , where θ is the inclination angle.

[0020] On the one hand, in step S6, the method includes: taking the wheel weight reduction rate not exceeding the preset value and combining the clear vision of the driver as the constraint condition, simulating the driving speed under the preset snow depth environment to generate the maximum speed that can meet the actual operating requirements under different snow depth conditions.

[0021] The present application also provides a simulation and optimization design method for a snow plow for clearing obstacles on a train based on multi-physics field coupling using the method, comprising: a data acquisition module for collecting snow environment parameters of a target line, wherein the environment parameters include at least snowfall data and snow accumulation data; a model processing module for establishing a soft ball model of snow particles and using the Hertz-MindlinwithJKRCohesion contact model to calculate the contact force between snow particles; a simulation module for performing snow simulation based on the wall snow covering criterion and using a discrete phase model, and then verifying the correctness of the snow removal resistance formula in the snow covering simulation by comparing with preset snow removal experimental data; a resistance verification module for establishing a snow plow model for clearing obstacles using a parametric modeling method. model, and based on the simulation results, shape parameters are introduced into the snow removal resistance formula to simplify the driving resistance coefficient, and then the snow removal performance of the obstacle clearing snow plow under the preset shape parameter combination is evaluated, and the snow removal resistance formula of the obstacle clearing snow plow is determined; the obstacle clearing selection module is used to add the front end to the obstacle clearing snow plow model to form a whole vehicle model and simulate according to the whole vehicle model, and then correct the snow removal resistance formula and generate obstacle clearing snow plow selection suggestions; the design optimization module is used to simulate at a speed formed by a preset snow depth environment, obtain the vehicle force and snow removal path under different snow depths and driving speed conditions, and then generate the maximum speed that can meet the actual operation requirements under different snow depth conditions according to the preset constraints; the design output module is used to output the optimal design parameters of the obstacle clearing snow plow.

[0022] The above technical solution of the present application has the following advantages over the prior art:

[0023] 1. A wedge-shaped snow-covered test was conducted using real snow and compared with simulations. This validated the wall capture criterion's effectiveness in simulating snow accumulation, which can be subsequently applied to vehicle snow accumulation simulations. A plow-type snow removal test was simulated using the discrete element method. The error between the simulated and experimental snow removal resistance values was approximately 3.5%. The variation of snow removal resistance with vehicle speed, snow depth, snow width, and snow density was consistent with the snow removal resistance formula, demonstrating the reliability of the Hertz-Mindlin with JK R Cohesion collision model.

[0024] 2. Snow removal simulations were conducted for various snowplow shapes and vehicle heads. The influence of shape parameters on snow removal resistance was determined, and the basis for the correction coefficient in the snow removal resistance formula was determined. Predicting snow removal resistance in advance provides a reference for vehicle power configuration. By comprehensively considering snow removal resistance and snow removal path, optimal snowplow shape parameters were generated.

[0025] 3. Compared with traditional empirical design, it can more accurately predict snow removal resistance and its impact on vehicle safety; it can quickly adjust to different railway lines for snowfall, different snow densities, and different vehicle types; and by using multi-physics field coupling simulation, multiple solutions can be batch tested on the computer, significantly shortening the design cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0027] Figure 1 Schematic diagram of the shape parameters of the snow plow provided by an embodiment of the present invention.

[0028] Figure 2 Schematic diagram of a model of two soft balls in contact with each other provided by an embodiment of the present invention.

[0029] Figure 3 This is a simplified schematic diagram of the soft ball model provided in an embodiment of the present invention for processing the contact force between particles.

[0030] Figure 4 Schematic diagram of a low / normal temperature snowfall simulation wind tunnel provided by an embodiment of the present invention.

[0031] Figure 5 Schematic diagram of a test device provided in an embodiment of the present invention.

[0032] Figure 6 Schematic diagram of the wall snow cover criterion provided by an embodiment of the present invention.

[0033] Figure 7 Schematic diagram of two wedge-shaped models provided by embodiments of the present invention.

[0034] Figure 8 Schematic diagram of snow covering wedge No. 1 provided in an embodiment of the present invention.

[0035] Figure 9 Schematic diagram of snow covering the second wedge-shaped body provided by an embodiment of the present invention.

[0036] Figure 10 It is a schematic diagram of a simulation calculation domain provided by an embodiment of the present invention.

[0037] Figure 11 3. It is a schematic diagram comparing the snow-covering results of wedge No. 1 provided in an embodiment of the present invention.

[0038] Figure 12 This is a cloud diagram and flow field diagram of the wedge-shaped particle capture criterion No. 1 provided in an embodiment of the present invention.

[0039] Figure 13 3. It is a schematic diagram comparing the snow-covering results of wedge No. 2 provided in an embodiment of the present invention.

[0040] Figure 14 This is a cloud diagram and flow field diagram of the particle capture criteria of the No. 2 wedge provided in an embodiment of the present invention.

[0041] Figure 15 It is a schematic diagram of regression fitting of DEM simulation data provided by an embodiment of the present invention.

[0042] Figure 16 Schematic diagram of snow density-snow removal resistance provided by an embodiment of the present invention.

[0043] Figure 17 Schematic diagram of model width-snow removal resistance provided by an embodiment of the present invention.

[0044] Figure 18 Schematic diagram of snow removal depth-snow removal resistance provided by an embodiment of the present invention.

[0045] Figure 19 Schematic diagram of simulation results of openable and closed snow plows provided in an embodiment of the present invention.

[0046] Figure 20 It is a schematic diagram of simulation results of the 60-45-0-55 vehicle model provided by an embodiment of the present invention.

[0047] Figure 21 Schematic diagram of the locations of pressure measuring points provided by an embodiment of the present invention.

[0048] Figure 22 3 is a schematic diagram comparing snow-discharge widths at different suppression angles provided by an embodiment of the present invention.

[0049] Figure 23 3 is a schematic diagram comparing snow-discharge heights at different suppression angles provided by an embodiment of the present invention.

[0050] Figure 24 It is a schematic diagram of the numerical simulation calculation domain provided by an embodiment of the present invention.

[0051] Figure 25 Schematic diagram of the height of the downward pressure wing tip provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0053] Considering the snow accumulation on the track caused by snowfall, a snow removal device is installed at the head of the train to help the train to clear snow at high speed in the early stage of snow formation. For the snow removal plow at the head of the EMU, the snow removal resistance generated by it during the snow removal process is of special concern in this application. This parameter is affected by many factors such as driving speed, snow removal device shape and snow environment. In particular, referring to the parameters given by the Japan Railway Technical Research Institute, the shape of the snow removal plow is mainly controlled by four parameters, namely, the opening angle α, the inclination angle β, the attack angle γ, and the suppression angle δ. For specific reference, Figure 1 shown.

[0054] This application uses discrete element numerical calculation methods to evaluate different combinations of shape parameters of the aforementioned snowplows, assessing their snow-clearing performance under these different parameter combinations. This also determines a formula for predicting the snow-clearing resistance of the snowplows, providing data support for traction verification. Numerical simulation calculations are not constrained by experimental conditions and can consider various phenomena or conditions separately, conducting in-depth research on the mechanisms of these phenomena and obtaining quantitative results for nonlinear problems. During the engineering design process, calculations can determine the impact of different parameter changes on optimization objectives, facilitating the comparison of multiple options. This approach offers advantages over various model tests and vehicle tests, such as short research cycles and low costs.

[0055] The discrete element method is a material analysis method for discrete particles. The basic idea of the discrete element method is to separate the discontinuity into a collection of rigid elements, so that each rigid element satisfies the equation of motion, and solves the equation of motion of each rigid element using the time-step iteration method, and then obtains the overall motion form of the discontinuity. Using the discrete element method for simulation and analysis can directly obtain a large amount of complex behavioral information of discrete materials and particle-scale behavioral information that is difficult to measure, and can provide advanced solutions for the motion, force, heat and energy transfer of particle flows. In addition, the discrete element method can use simple equations to simulate the quasi-static and dynamic behavior of highly complex systems, making the solution simple and feasible. The discrete element method can also accurately predict and analyze the mechanical behavior of materials that cannot be explained and analyzed by continuous medium theory.

[0056] The discrete element method considers a discrete body as a collection of discrete particle units with a certain shape and mass, with each particle being a unit. To facilitate analysis, the following assumptions are made:

[0057] (1) The particles are rigid bodies, and the deformation of the particle system is the sum of the deformations of the contact points of these particles;

[0058] (2) The contact between particles occurs in a very small area, that is, point contact;

[0059] (3) The particle contact characteristic is soft contact, that is, rigid particles are allowed to overlap to a certain extent at the contact point. The overlap between particles is very small compared to the particle size, and the deformation of the particles themselves is also much smaller than the translation and rotation of the particles.

[0060] (4) In each time step, the perturbation cannot propagate from any particle to its neighboring particles simultaneously. At all times, the net force acting on any particle can be uniquely determined by the interactions between the particles in contact with it.

[0061] The most important point in the discrete element method is to use an efficient computational method to determine the contact between three-dimensional particles. The form of contact can be the contact between blocks of any shape, and the geometric and physical characteristics of the contact can be described.

[0062] The soft ball model simplifies the normal force between particles into a spring and a damper, and the tangential force into a spring, a damper, and a slider. It introduces parameters such as the elastic coefficient and the damping coefficient, does not consider the deformation of the particle surface, calculates the contact force based on the normal overlap and tangential displacement between particles, does not consider the contact force loading history, has low computational intensity, and is suitable for numerical calculations of engineering problems. The hard ball model completely ignores the size of the particle contact force and the details of the particle surface deformation. The contact process is simplified to a collision process that is completed instantly. The post-collision velocity is directly given as the result of the force-time integration during the contact process. The energy dissipation during the collision is expressed by the coefficient of restitution. The hard ball model is mainly used for numerical simulations of fast-moving, low-concentration particle systems. Due to the high concentration of snow particles during snow removal by trains and the errors caused by the deep simplification of the actual physical process by hard ball particles, this application uses a soft ball model for simulation calculations.

[0063] The soft ball model simplifies the contact process between particles into the damped vibration of a spring oscillator, and its motion equation is: Where x is the displacement from the equilibrium position, m is the mass of the oscillator, c and k are the spring damping coefficient and elastic coefficient, respectively.

[0064] From the motion equation, it can be seen that the restoring force on the particle is proportional to the displacement, and the viscous resistance it experiences is proportional to the velocity but in the opposite direction. Therefore, the energy of the spring oscillator gradually decays. As the damping increases, the spring oscillator exhibits underdamped vibration, critically damped vibration, and overdamped vibration, respectively.

[0065] refer to Figure 2 As shown in the figure, particle i contacts particle j at point C under the action of inertia or external force. The dotted line indicates the position of particle i when contact begins. As the two particles move relative to each other, the particle surfaces gradually deform and generate contact force. The soft ball model does not consider the deformation details and only calculates the normal overlap α and tangential displacement δ to obtain the contact force.

[0066] The soft ball model sets springs, dampers, sliders, and couplers between particles i and j. The couplers are used to determine the pairing relationship of the contacting particles without introducing any force. In the tangential direction, if the tangential force exceeds the yield value, the two particles slide under the action of the normal force and friction, and this is achieved by the sliding resistance. The soft ball model requires the introduction of parameters such as the elastic coefficient k and the damping coefficient c to quantify the role of the spring, damper, and slider. Figure 3 shown.

[0067] The normal force is the resultant of the elastic force and the damping force of the spring and the normal damper acting on particle i, refer to Figure 2 and Figure 3 For two-dimensional particles, the elastic force is proportional to the overlap, and the damping force is proportional to the relative velocity of the particles, so F nij =(-k n α-c n v ij ·n)n, α is the normal overlap; v ij is the velocity of particle i relative to particle j, v ij =v i -v j ; n is the unit vector from the center of particle i to the center of particle j, k n and c n are the normal elastic coefficient and normal damping coefficient of particle i.

[0068] For three-dimensional spherical particles, according to Hertz contact theory, F nij Expressed as The tangential force F nij Indicated as F nij =-k t δ-c τ v ct , where k t and c τ are the tangential elastic coefficient and tangential damping coefficient; v ct is the slip coefficient of the contact point, δ is the tangential displacement of the contact point, which is not necessarily the same as the sliding velocity vector v in three-dimensional motion. ct The direction is consistent.

[0069] Sliding velocity vector v ct For: v ct =v ij -(v ij ·n)n+R i ω i ×n+R j ω j ×n, where R i and R j are the radii of particles i and j respectively; ω i and ωj are the angular velocities of particles i and j, respectively.

[0070] If |F tij |>μ s |F nij |, particle i slides, and the tangential force is F tij =-μ s |F nij |n t , that is, Coulomb's friction law, where μ s is the static friction coefficient.

[0071] Tangential unit vector n t Determined by the following formula: The resultant force and moment acting on particle i are F ij =F nij +F tij , T ij =r in ×F tij , r is the distance from the particle center of mass to the contact point. When the particle concentration is high, particle i can contact several particles at the same time, and the total force and total torque acting on particle i are:

[0072] F i =∑ j (F nij +F tij ), T i =∑ j (R i n×F tij ).

[0073] The damping coefficient of the elastic coefficient introduced by the soft ball model is related to parameters such as the elastic modulus and Poisson's ratio of the granular material, but it cannot be measured directly and needs to be calibrated. n Determined by Hertz contact theory:

[0074] Where E and v are the elastic modulus and Poisson's ratio of the particle material, respectively, R is the particle radius, and the subscripts i and j represent the particles i and j that are in contact, respectively. When particles i and j are of homogeneous materials and have equal particle sizes, then k n for

[0075] Tangential elastic coefficient k t for: Among them, α is the normal overlap, G i and G j are the shear moduli of particles i and j, respectively. When particles i and j are of homogeneous materials and have the same particle size, k t for

[0076] During the particle contact process, k n and k t It is related to the normal overlap and needs to be calculated in real time according to the contact process, but the amount of calculation is very large. For the convenience of calculation, the soft ball model usually assumes that the elastic coefficient and damping coefficient remain unchanged throughout the contact process, ignoring details such as loading history and deformation.

[0077] If a spring oscillator with mass m is in a critical damping state, the mechanical energy decays at the fastest speed. At this time, the normal damping coefficient c is n and the tangential damping coefficient c t They are:

[0078]

[0079] Or, the damping coefficient is coupled with the restitution coefficient e:

[0080] Here, e is determined by experiment.

[0081] Particles interact with each other and with walls, establishing contact through the forces acting on them. Particles in relative motion are said to be in contact when they physically contact and generate corresponding forces. Each contact is classified into a contact stiffness model, a sliding model, and a bonding model based on the constitutive relationship. In the contact stiffness model, the contact force and relative displacement are linearly related. The sliding model allows two contacting entities to slide relative to each other and separate, while the bonding model provides normal tensile and tangential shear strengths at each contact point. In this project, the Hertz-Mindlin with JKRCohesion model was selected to simulate snow particles.

[0082] The Hertz-Mindlin with JKR (Johnson-Kendall-Roberts) Cohesion model is a cohesive contact model that allows users to simulate strongly cohesive systems, such as dry powders or wet granules. In this model, the implementation of the normal elastic contact force is based on the Johnson-Kendall-Roberts theory.

[0083] The Hertz-Mindlin (no slip) model, the default model used in EDEM, provides accurate and efficient force calculations. In this model, the normal force component is based on Hertzian contact theory, and the tangential force model is based on the work of Mindlin-Deresiewicz. Both the normal and tangential forces have damping components. Tangential friction follows Coulomb's friction law. Rolling friction is implemented using a contact-independent, directional, constant torque model.

[0084] Normal force Where E* is the equivalent Young's modulus and R* is the equivalent radius, which is defined as:

[0085] Among them E i 、v i 、R i and E j 、v j 、R j are Young's modulus, Poisson's ratio and the radius of the contact sphere, respectively.

[0086] In addition, the damping force The expression is: in, is the equivalent mass, is the normal component of the relative velocity, β and S n The expression is:

[0087] e is the coefficient of restitution.

[0088] Tangential force F t Depends on the tangential overlap δ t and tangential stiffness S t , that is: F t =-S t δ t ,in,

[0089] G* is the equivalent shear modulus; hence, the tangential force F t The expression is:

[0090] in, is the tangential component of the relative velocity; where the tangential force is affected by Coulomb friction μ s F n Limit, where μ s is the coefficient of static friction, and rolling friction exerts a torque on the contact surfaces:

[0091] τ i =-μ r F n R i ω i , where μ r is the rolling friction coefficient, R i is the distance from the contact point to the center of mass, ω i is the unit angular velocity vector of the object at the contact point.

[0092] Hertz-MindlinwithJKRCohesion uses the same method as the Hertz-Mindlin(noslip)contactmodel to compute the following forces: tangential elastic force, normal dissipative force, and tangential dissipative force.

[0093] The JKR normal force is based on the overlap δ and the interaction parameter, surface energy γ:

[0094] Where, E* is the equivalent Young's modulus, R* is the equivalent radius;

[0095] This model provides attractive cohesion even when the particles are not in direct contact. The maximum gap between particles with non-zero cohesion is calculated as follows: When δ<δ c , the model returns 0;

[0096] When the particles are not actually in contact and the separation is less than δ c When , the cohesion reaches its maximum value, thus, the maximum cohesion is called pull-outforce, which is: The friction calculation differs from the Hertz-Mindlin (noslip) contact model in that it depends on the positive repulsive component of the JKR normal force. Therefore, the JRK friction model provides a higher friction when the cohesive component of the contact force is larger. This model is designed for fine, dry particles, but it can also be used to simulate wet particles. The force required to separate two particles depends on the surface tension of the liquid, γ s and wetting angle.

[0097] The simulation study of the wind-snow coupling effect, in which the simulation treatment of the snow phase can be divided into the Euler method and the Lagrangian method. For the former, the snow phase is treated as a continuous medium that interpenetrates the fluid (air) phase. The gas and solid phases in the control body are distinguished by introducing the phase volume fraction, and the conservation equations of each phase are solved. For the latter, the fluid (air) phase is the continuous phase, and the snow phase is treated as a discrete particle system. The coupling of the gas and solid phases is realized through parameters such as the particle drag force, and the exchange of phase quantity and momentum is calculated. Among them, the Lagrangian method represented by the discrete phase model is widely used in the simulation study of wall snow.

[0098] Its continuity and momentum conservation equations are:

[0099] Among them, ρ f , u and P are the density, velocity and pressure of the fluid respectively, t is time, g is the acceleration of gravity, α f is the fluid volume fraction,

[0100] α f =1-V p / V c Calculate, V c 、V p are the total volume of the control unit and the volume of the particle respectively; F f-p is the force between the air phase and the snow phase, mainly the particle drag force F drag ; τ is the fluid viscous stress tensor,

[0101] Calculate, where I is the unit tensor, μ f The fluid viscosity includes kinematic viscosity and turbulent viscosity, which can be applied in the selected turbulence models, such as the RANS Reynolds average model, the LES large eddy model and the DNS direct simulation model.

[0102] For the snow phase, the snow particles are regarded as spheres, and the force balance equation is: Among them, m p 、u p and ρ p are the mass, velocity and density of snow particles respectively, and F is the additional external field;

[0103] Among them, d p is the snow particle size, C D is the particle drag coefficient, which is calculated based on the smooth ball model by the particle Reynolds number R e,p It is determined by a set of empirical constants a1-a3 determined by the range;

[0104]

[0105] Calculate particle trajectories: The differential equation is solved along each coordinate direction to obtain the trajectory of the discrete phase.

[0106] Based on this, the traditional SIMPLE method is used to solve the control equation of the fluid (air) phase, and combined with the explicit time integration method, the motion equation of particles in the flow field can be solved.

[0107] When snow particles hit the wall, whether they can adhere to the wall and accumulate stably requires a suitable wall-covering criterion to determine. The movement behavior of the snow particles after the collision with the wall is the key to determining whether it is covered with snow. The movement behavior after the collision mainly depends on the collision characteristics between the particles and the wall and the flow field of the wall. From the energy point of view, the collision between the snow particles and the wall is an inelastic collision. The degree of kinetic energy loss after the collision determines the adhesion or rebound behavior. However, the loss of kinetic energy of the snow particles (consumption or conversion into bonding internal energy) is related to many complex factors such as the crystal type of the snow itself, moisture content, and viscosity between crystals. Therefore, it cannot be described only by the traditional collision recovery coefficient or splash function. In addition, whether the snow particles can stably or continuously adhere to the wall under the action of the flow field is also related to the wall shear stress (friction velocity). When the wall friction velocity u * Greater than a certain threshold friction speed u *t When the snow particles on the wall are eroded, they cannot be stably adhered to the wall. Figure 6 As shown in Figure 2, the wall snow cover criteria include the following three:

[0108] The incident angle α is smaller than the capture angle α t When , the snow particles are captured by the wall;

[0109] When the local friction velocity is less than the preset threshold friction velocity, the snow particles will not be carried away by the wind and will accumulate;

[0110] Particle collision velocity u p Less than the critical capture velocity u trap When , the wall's viscous force on the particles is larger, and the particles will be captured;

[0111] When the particles meet the above three wall-covering criteria, the snow particles will be captured by the wall and stably accumulate on the wall to achieve snow covering the wall.

[0112] For example, this application uses different wedge-shaped bodies to conduct surface snow cover experiments to explore the distribution of snow on the surfaces of different wedge-shaped bodies. At the same time, numerical simulation methods are used to conduct numerical simulation research on the experiments.

[0113] Specifically, the experiment was based on the reference Figure 4 and Figure 5 The experiment was conducted in a low / normal temperature snowfall simulation wind tunnel shown in the figure. This wind tunnel is located in a closed environment and is a direct current type. It has a total length of 14m, a test section length of 6m, and a cross-sectional side length of 1.2. The maximum experimental wind speed of the wind tunnel is 10m / s. The experimental research can be carried out using real snow under the low temperature (below -20℃) and snowy conditions in Harbin in winter. The experimental equipment mainly includes four parts:

[0114] Wind tunnel equipment system: includes fans, wind tunnel chambers and other components to provide a stable and uniform wind environment for snow cover experiments.

[0115] Snowfall system: It consists of two components: a vibrating screen for spreading particles and a water mist gun, which provide a uniform and stable snow source with a certain water content for snow covering experiments.

[0116] Model system: It consists of two parts: the experimental model and the rigid bracket. The bracket has considerable weight and rigidity, so that the model will not be blown down, deformed, or affect the original wind field under strong wind conditions.

[0117] Data acquisition system: includes an anemometer (Ulead UT363 series) and a scanner to obtain experimental wind speed and model surface snow cover results; the snow sensor uses the FlowCapt snow sensor series.

[0118] The overall idea of the snow-covering experiment is to use a uniform and stable wind field inflow to simulate the stable static flow field of natural wind speed, and transport the snow particles falling from the vibrating screen to the surface of the model. During this process, the high-pressure water mist gun emits a fine and uniform water mist. The water mist gun can generate a water pressure of 3.5MPa and can shoot out high-pressure fine water mist. This high-pressure fine water mist is a highly atomized water mist with a diameter of 10-100μm. It combines with snow particles (diameter of about 700-900μm) in the wind field to form a thin water film on the surface of the snow particles to simulate the non-negligible adhesion caused by the water content of snow particles under actual conditions. It also combines with snow particles during the snow blowing process, so that the snow particles have a certain water content and finally adhere to the surface of the model.

[0119] The wedge model is divided into the following two shapes: one is a right-angle wedge, including two sizes, and the other is a general wedge; Figure 7 shown.

[0120] The snow particles used in the experiment were freshly collected local natural snowfall, with a density of 320 kg / m³ and an average particle size of 0.8 mm. The test temperature was below -25°C, with a snow flux of 3.5 kg / m²·h. All four models were oriented vertically into the wind at 0°C, with a wind speed of 5 m / s and a blowing time of 10 minutes.

[0121] The results of snow covering of wedge No. 1: mass before snow covering 1850g, mass after snow covering 1970g, and mass of accumulated snow 120g.

[0122] After the snow covering is completed, a Leica MS-60 total station scanner is used for scanning and measurement. After scanning, any distortion points that appear during the measurement process must first be corrected or deleted, and then the pre-processed point cloud data is interpolated. After interpolation, the shape cloud map of the original model and the model surface after snow covering can be obtained. Finally, the two sets of data before and after snow covering need to be subtracted to obtain the snow depth on the model surface and draw a snow depth cloud map. Figure 8As shown, the snow accumulation on the top and slopes is concentrated in the upper-middle area. The peak snow depth on the top is 1.07 times the extreme snow depth on the slopes. However, the snow accumulation on the top first increases and then decreases with the increase of the distance from the top, while the snow accumulation on the slopes decreases with the increase of the distance from the top, and the snow accumulation tends to extend toward the lower right corner.

[0123] The snow-covered results of wedge No. 2: mass before snow-covering 3040g, mass after snow-covering 3195g, and mass of accumulated snow 155g.

[0124] After snow cover, a Leica MS-60 total station scanner was used to measure and obtain the final snow cover point cloud data. The distortion points that appeared during the measurement process were corrected or deleted, and the initially processed point cloud data was interpolated. Finally, the two sets of data before and after snow cover were subtracted to obtain the snow depth on the model surface and draw a snow depth cloud map. From the overall snow cover map, it can be seen that only sporadic snow particles are attached to the slope with an inclination of 72°, so they can be ignored. In the following, only the snow distribution on the top surface and the 45° slope will be discussed. Figure 9 As shown, the snow accumulation on the top and slopes is concentrated in the middle and upper areas. The peak snow depth on the top is 1.05 times the extreme snow depth on the slopes and appears in the middle and left areas. However, the snow accumulation on the top increases first and then decreases with the increase of the distance from the top, while the snow accumulation on the slopes decreases with the increase of the distance from the top. A symmetrically distributed peak snow accumulation area appears in the middle and upper areas, and the snow accumulation tends to extend toward the lower right corner.

[0125] The numerical simulation model is carried out using a discrete phase model. Based on the experimental conditions, a numerical simulation model with the same size and boundary as the experimental conditions is established during the numerical simulation process. The finite element model is referenced Figure 10 As shown, the calculation domain size is 3.9m×1.65m×1.65m, the particle introduction position is the same as the position of the vibrating screen in the test, and the particle introduction plane size is 0.74m×0.74m.

[0126] This simulation model uses a structured grid with a minimum grid size of 0.005m and a grid number of 400w. The inflow boundary of the computational domain is a velocity inlet with an inflow velocity of 5m / s. The outlet is a steady outflow. The model surface is a no-slip boundary. The side and bottom boundaries of the computational domain are symmetrical boundaries. The specific flow field boundary conditions are set as shown in the following table:

[0127]

[0128] The fluid was treated as a continuous phase in the simulation, and the Realizable k-ε turbulence model was used for flow simulation. Based on a snow-covered wedge model test, a uniform steady-state flow field was assumed, with an inlet wind speed of 5 m / s. The air density was set to 1.255 kg / m³, and the viscosity to 1.78 × 10⁻⁵. The SIMPLE (Semi-Implicit Method for Pressure Linked Equations) algorithm was used until convergence. A second-order upwind scheme was used for both the pressure and momentum terms, an implicit first-order scheme was used for the time term, and a first-order upwind scheme was used for the turbulent kinetic energy and turbulent diffusivity.

[0129] Snow particles are set as discrete phases in the simulation. The physical properties of snow particles are based on the measured values of snowfall in Harbin, and the skeleton density is 320kg / m3. This paper considers the Rosin-Rammler distribution, and the maximum and minimum particle sizes are set to 0.9mm and 0.7mm respectively, with an average particle size of 0.8mm. The discrete phase boundary conditions are set as follows: the fluid inlet and fluid outlet are set as escape conditions, the side, top and bottom surfaces of the calculation domain are set as rebound conditions, the wedge model is set as a custom wall, and the wall snow cover criterion is embedded to determine the adhesion of particles after collision with the wall. In the custom capture, the rebound motion of particles when the judgment conditions are not met is taken into account. The tangential velocity recovery coefficient (the ratio of the tangential velocity after rebound to the tangential velocity before rebound) is 0.7, the normal velocity recovery coefficient is 0.1, and the random walk model is turned on in the simulation.

[0130] Comparison of snow coverage results for wedge No. 1:

[0131] refer to Figure 11 As shown in the figure, a comparison of the snow cover cloud map shows that the overall distribution of the cloud map is similar to the experimental results, showing a trend of more snow cover in the upper part and less snow cover in the lower part. The peak snow cover on the top is in the upper middle region, while the peak snow cover in the slope experiment is in the middle region. In the simulation results, the peak snow cover on the slope is in the upper right region, and the snow cover shows a decreasing trend from the upper right to the lower left. There is a certain error, but the overall result is consistent with the experiment.

[0132] Result analysis:

[0133] refer to Figure 12 As shown in the figure, the incident angle and friction velocity of the inclined particles increase diagonally toward the lower left corner, the incident angle of the top surface particles is smaller, and it shows an increasing trend from top to bottom. There is an area with low friction velocity in the upper area of the top surface, which is mainly due to the blunt body flow, which reduces the wind speed and causes the particle accumulation area. The particle collision velocity in the middle area of the top surface and the middle area of the inclined surface is low, and the particles are easy to accumulate, which is consistent with the experimental results.

[0134] Comparison of snow cover results for wedge No. 2:

[0135] refer to Figure 13 As shown, the comparison of the snow cover cloud map shows that compared with the experimental results, the cloud map distribution is similar as a whole, showing a trend of more snow cover in the upper part and less snow cover in the lower part, but the amount of snow is less than that in the experiment, and the peak of the top snow cover in the experiment is in the upper-middle left area, while the peak of the top surface in the simulation results is in the upper area, the peak of the slope snow cover in the experiment is in the upper-middle area, while the peak of the slope snow cover in the simulation results is in the upper right area, and the snow cover shows a decreasing trend from the upper right to the lower left.

[0136] Result analysis:

[0137] refer to Figure 14 As shown in the figure, the incident angle and friction velocity of the inclined particles increase diagonally toward the lower left corner, and the incident angle of the inclined particles is significantly increased compared with the 36° right-angle wedge. This is mainly due to the change in the inclination angle of the inclined surface, which causes a large change in the flow characteristics of the wind field at this location. The incident angle of the top surface particles is small, and it shows an increasing trend from top to bottom, which makes it easy for particles to accumulate in the upper area. There is an area with low friction velocity in the upper area of the top surface, which is mainly due to the blunt body flow at this location, which reduces the wind speed and causes a particle accumulation area. A concentrated area with low particle collision velocity appears in the middle and upper area of the top surface, and particles are easy to accumulate in this area, which is consistent with the snow accumulation area in this area in the experiment.

[0138] By comparing the snow depth cloud map results above, it can be seen that the snow cover distribution of wedges of different angles and sizes is basically consistent with the simulation results. Since the area with more snow on the wall is more concerned in actual engineering applications, and in order to further quantitatively analyze the snow depth scale of the test and simulation results, this paper selects the snow depth data of the local cross-section of the snow accumulation area in the test and simulation results for analysis; the specific data are shown in the following table:

[0139]

[0140] From the experimental data on snow depth on the top of the model in the table above, it can be seen that the snow depth in the snow accumulation area on the top of the model first increases and then decreases as the distance from the top of the model decreases, which is consistent with the simulation results. At the same time, the difference between the experimental results and the simulation results at the extreme snow depth positions is very small, basically below 4%, and the peak snow depths at local locations are consistent, which is in good agreement.

[0141]

[0142] From the experimental data on snow depth on the model slope in the table above, it can be seen that the snow depth in the snow accumulation area on the model slope first increases as the distance from the top of the model decreases. The snow depth is relatively small at the boundary position, which is consistent with the simulation results. At the same time, the difference between the experimental results and the simulation results at the extreme snow depth position is very small, basically below 4%, which is in good agreement.

[0143] To verify the feasibility of using the discrete element method to simulate target snowplow snow removal, this study further conducted numerical simulation analysis based on snow removal (real snow / surrogate snow) tests using a scaled snowplow model conducted by the Japan Railway Research Institute. The numerical simulations were conducted under the same boundary conditions using the 200 and B60 snowplow models mentioned in Japanese literature.

[0144] Based on this, a quantitative analysis and comparison of the snow removal resistance of snow plows was carried out. Considering that the experimental snow accumulation density is 200kg / m3 according to the literature, based on the porous medium powder particle accumulation theory, the density conversion formula is given as a reference: Among them, ρ s is the particle density, ρ s，0 is the apparent density, β p is the particle volume fraction, ρ a is the air density, take 1.225kg / m 3 .

[0145] Assuming that the accumulation of natural snowfall is random, then β p ≈60% determines the absolute density of a single snow particle to be 333 kg / m 3 Specifically, the snow particles used in the DEM simulation are 333 kg / m 3 On the other hand, the selection of parameters such as friction coefficient in collision parameters (such as elastic modulus, Poisson's ratio, and restitution coefficient) can refer to the following material parameter table and collision parameter table:

[0146] Material parameter table

[0147]

[0148] Collision Parameters

[0149]

[0150] The calculation expression of snow removal resistance is: F p =ChwρV 2 , where h is the snow removal depth, w is the snow removal width, ρ is the snow density, V is the driving speed; C is the calculated driving resistance coefficient,

[0151] Through DEM numerical simulation calculation, it can be found that the simulation results are in good agreement with the test results. The corresponding data comparison is shown in the following table.

[0152]

[0153] Specifically, the average error of the snow plow's snow removal resistance obtained in this simulation study is about 3.5% compared to the experimental prototype. By performing regression fitting analysis on the snow plow's snow removal force under different snow plow operating speed conditions, it can be found that the snow plow's snow removal resistance and speed are roughly quadratic. Figure 15 shown.

[0154] The effect of snow density changes on snow plow resistance was studied, and the following results were obtained through numerical simulation.

[0155]

[0156] By analyzing the simulation results, it can be found that the existence of differences in the physical properties of snow particles also has a certain impact on the simulation results. The relationship between snow density and snow removal resistance is approximately linear. Figure 16 shown.

[0157] In order to explore the effect of snow plow width on snow removal resistance, the modeling method uses a snow plow with "90-45-55-44" shape parameters to build a model. The total width of the snow plow is adjusted based on the width of 2m to establish snow plows of different widths.

[0158] The parameters are as follows:

[0159]

[0160] Since the snow plow's width and height mainly affect the snow plow's snow removal area, the impact of changes in width and height on snow removal resistance is converted into the impact of changes in snow removal area caused by changes in width or height on snow removal resistance. Based on the above working conditions, a simulation was conducted at a vehicle speed of 30m / s, and the results are as follows:

[0161]

[0162] By analyzing the simulation results, it can be found that the snow removal area and the snow removal resistance approximately satisfy a linear relationship, and the change of the snow removal area is similar to the change of the snow plow width. It can be simplified to the snow removal width and the snow removal resistance approximately satisfy a linear relationship. For details, refer to Figure 17 shown.

[0163] To investigate the effect of snow plow depth on snow removal resistance, we varied the snow plow depth. The snow plow model used a plow with 90-45-55-44 dimensions. Similar to the snow plow width, the effect of snow plow depth was considered by varying the snow plow area. Simulations were conducted under the five operating conditions described above at a vehicle speed of 30 m / s. The results are as follows:

[0164] Snow removal depth conditions

[0165]

[0166] Snow removal depth-snow removal resistance

[0167]

[0168] By analyzing the simulation results, it can be found that when the snow removal depth exceeds the height of the snow plow's lower pressure wing, that is, the snow removal depth is normal, it can be considered that the snow removal depth and the snow removal resistance have a linear relationship. However, in extreme cases, that is, when the snow depth is large, the results will not meet the linear relationship due to the influence of the snow plow's shape. For details, refer to Figure 18 shown.

[0169] In summary, the numerical simulation results are in good agreement with the experimental results. At the same time, according to the simulation results of the influence of snow density and driving speed on snow removal resistance, it can be seen that the snow removal resistance of the snow plow satisfies the formula: F p =ChwρV 2 .

[0170] Therefore, by conducting DEM discrete element simulation and experimental comparative analysis on snow removal of a scaled snow plow model, the feasibility of the research method proposed in this study was verified from quantitative and qualitative perspectives. The specific conclusions are as follows:

[0171] (1) The different physical parameters of snow particles have a certain impact on the simulation results. The density and snow removal resistance are linearly related.

[0172] (2) Consistent with the experimental prototype results, the snow removal resistance of the snow plow and the speed are roughly quadratic and satisfy the snow removal resistance calculation formula;

[0173] (3) The snow plow snow removal simulation has a high consistency with the test in the starting mode, disengagement mode and snow removal path during the process;

[0174] (4) The simulated snow removal resistance is in good agreement with the experimental snow removal resistance, with an average error of about 3.5%. The feasibility of using numerical simulation for research is relatively high.

[0175] From this, a model of the snow plow is established:

[0176] In addition to the four angle parameters mentioned above, the snow plow shape should also be selected from the perspective of overall form before building the model. Currently, there are two main types of snow plows: open and close snow plows and closed snow plows.

[0177] The above-mentioned numerical simulation method is used to simulate two different types of snow plows to evaluate the snow plow shape from a qualitative perspective; the simulation results are referenced. Figure 19 shown.

[0178] Comparing simulation results reveals that a retractable snow plow can produce front-end overflow during snow removal. Further investigation of this phenomenon involved calculating the snow removal process at different speeds, with a snow depth of 0.25m. The speeds were 72km / h, 108km / h, 140km / h, and 180km / h.

[0179] As the driving speed increases, the phenomenon of snow overflow at the front end becomes more and more obvious. This phenomenon is not only more detrimental to train operation from the perspective of force, but also because the flying snow is not removed along a regular path, this phenomenon is more detrimental to the control of the snow removal path. The irregular snow removal path may even affect the line of sight in the cab, so a closed snow plow is selected to optimize the appearance parameters.

[0180] The parametric modeling method is used to construct the 3D shape of the snow plow. The specific steps are as follows:

[0181] Select a horizontal plane and determine the specific position and length of the line segment based on the angle and vehicle width;

[0182] Select the vertical symmetry plane of the snow plow, and determine the snow plow leading edge segment and the angle of attack constraint line based on the total height and lower half height of the snow plow;

[0183] Generate the snow plow lower half plane from the determined line segments;

[0184] The base line is projected onto the snow plow upper edge plane by converting the entity reference to determine the reference plane of the rear spline curve;

[0185] The rear spline curve is determined by the restraint angle and vehicle width, and the boundary conditions are tangency to the plane determined by the angle of attack, restraint angle constraint, and restraint angle height constraint.

[0186] Determine the front spline curve with boundary conditions of the snow plow's front end protrusion distance and tangency to the angle of attack plane and line segment;

[0187] Generate surfaces and planes to complete the modeling of half of the snowplow, and then mirror the solid to complete the snowplow modeling.

[0188] A snowplow model was created in SolidWorks. The snowplow model is primarily controlled by the opening angle, inclination angle, angle of attack, and suppression angle. After determining the parameters, the overall shape of the snowplow was determined through geometric relationships. The upper portion of the snowplow was considered a curved surface for snow removal, with spline curves constraining the boundaries. Using the surface function, a snowplow model consisting of four surfaces was generated. Because the snowplow modeling process is cumbersome and requires extensive modeling during optimization, VBA was used to simplify the double-spline snowplow modeling process in SolidWorks into a command flow.

[0189] The snow removal resistance of the snow plow under different combinations of shape parameters: opening angle α, inclination angle β, attack angle γ, suppression angle δ, and height is studied using numerical simulation methods.

[0190] Through DEM numerical simulation calculation, it can be seen that the change of the opening angle has a significant impact on the snow removal resistance. As the opening angle increases, the snow removal resistance of the snow plow increases. The snow removal resistance increases by 2% between 60° and 120°. For details, please refer to the table below:

[0191]

[0192] The data of other inclination angles, suppression angles, and angles of attack are shown in the following table:

[0193] Inclination angle-snow resistance table

[0194]

[0195] As the inclination angle increases, the snow removal resistance changes by about 2%. It can be found that when the inclination angle is about 45°, the snow removal resistance is smaller.

[0196] Suppression Angle-Snow Removal Resistance Table

[0197]

[0198] As the suppression angle increases, the snow removal resistance changes by about 4.7%, and still shows an overall upward trend.

[0199] Angle of attack-snow resistance table

[0200]

[0201] As the angle of attack increases, the snow removal resistance changes by about 7.2%, and the overall trend is still upward.

[0202] Furthermore, the data of snow plow height is shown in the following table:

[0203]

[0204] However, as the height of the cowplow increases, the snow removal resistance increases. Since the height of the cowplow directly affects the snow removal area, the results show that the change in the height of the cowplow has a greater impact on the snow removal resistance. However, different snow removal depths will also affect the snow removal area of snowplows with different cowplow heights. Therefore, the height of the cowplow should be determined according to the actual snow removal depth.

[0205] Therefore, based on the above-mentioned snow removal resistance formula, the shape parameter is introduced into the formula according to the simulation results to simplify the calculation method of the driving resistance coefficient, which is convenient for estimating the driving resistance coefficient of snowplows with different shape parameters based on the existing driving resistance coefficient.

[0206] The driving resistance coefficient C can be estimated by the following formula: C = C0 + C1 + C2, where C0 is linearly interpolated by looking up the following table:

[0207]

[0208] C1=0.028(δ+2γ)×10 -4 , δ is the suppression angle, γ is the angle of attack;

[0209] In C2: when θ<45°, C2={5.64+0.008×(45-θ)}×10 -4 ;

[0210] When 45°≤θ≤90°, C2={5.64+0.0025×(θ-45)}×10 -4 , where θ is the inclination angle.

[0211] In order to consider the influence of the vehicle head on snow removal by the snow plow in actual engineering, a whole vehicle model with the vehicle head added was simulated to analyze the influence of the shape parameters on factors such as snow removal resistance, and to obtain the lateral and vertical forces applied to the whole vehicle during driving.

[0212] The force on the vehicle is analyzed by taking the 60-45-0-55 (opening angle-inclination angle-attack angle-restraint angle) snow plow as an example. The snow depth is 400mm and the driving speed is 40m / s. The simulation results are referenced to Figure 20 shown.

[0213] The snow removal force of the simulation results is summarized as follows:

[0214] (1) In the X direction, the overall snow removal resistance of the vehicle model is 26% greater than that of a single snow plow;

[0215] (2) In the Y direction, the maximum lateral force during snow removal is less than 5 kN;

[0216] (3) In the Z direction, the force is 269.8 kN, and the direction is upward (further determination should be made as to whether the wheel load reduction requirement is not greater than 7%);

[0217] (4) The snow removal resistance of the snow plow part in the whole vehicle model is smaller than that of the single snow plow.

[0218] The Z-direction forces should be further evaluated with reference to the requirements of the European standards. The standards provide corresponding requirements for wheel weight reduction, which states that the wheel weight reduction should not exceed 7%. It can also be found that because the front of the vehicle shares the snow removal resistance, the snow removal resistance of the entire vehicle is reduced compared to the single snow plow simulation. The specific simulation results are as follows:

[0219]

[0220] According to the task book requirements, extract the wall pressure value of the corresponding pressure measuring point. The specific pressure value and pressure measuring point location are referenced Figure 21 With the following table:

[0221]

[0222] Analysis of the simulation results reveals that measuring point 1 does not participate in the snow removal process under this operating condition and therefore has no pressure value. However, for the other measuring points, the central area has a higher pressure. Furthermore, the overall pressure value is less than 1 MPa, which is consistent with the experimental result of 1.18 MPa (snow bed apparent density 200 kg / m³, driving speed 40 m / s, snow depth 40 mm, model scale ratio 1:5). This indicates that the two values are of the same order of magnitude and are reliable. Furthermore, the proposed snow plow design outperforms the experimental snow plow in this respect.

[0223] Due to the mutual influence between the front part of the vehicle and the snowplow model, the influence of the snowplow appearance parameters of the entire vehicle model on the snow removal process is different from that in the case of a single snowplow. Therefore, numerical simulation studies are conducted on the opening angle, which has a more obvious influence, and the angle of attack, which has a greater impact on the snow-facing area, and the snow removal resistance formula for a single snowplow is adjusted.

[0224] In order to consider the influence of the opening angle, the numerical simulation of the single snow plow opening angle condition including the vehicle head model is carried out:

[0225]

[0226] The simulation results show that the vehicle head model significantly influences the movement of snow particles during snow removal. At 60° and 90° opening angles, the vehicle head model is detrimental to reducing snow removal resistance, while at 120°, the presence of the vehicle head model is beneficial. Furthermore, the presence of the vehicle head further restricts the movement of snow particles. While the effect of the opening angle on snow removal resistance in the full vehicle model is smaller than in the case of a single snow plow, it still remains significant.

[0227] Based on the above simulation results, the value of the parameter C0, which takes into account the influence of the opening angle in the driving resistance coefficient given above, is adjusted. Since the resistance of the front part of the vehicle other than the snow plow changes little with the change of the opening angle in the case of the whole vehicle model, this part and the snow plow resistance are simplified into one item, namely the snow removal resistance of the whole vehicle during driving, and considered. At the same time, since the snow depth is the same as the snow plow height, the influence of the front part of the vehicle model on the snow removal area when the opening angle changes is very small. In order to simplify the calculation model, the influence of the rest of the front part on the snow removal area is ignored. In summary, for the whole vehicle model, C0 is adjusted based on the snow removal resistance of the whole vehicle, and the value should be determined according to the following table:

[0228]

[0229] Since the change of angle of attack will have a certain impact on the snow-facing area, the change of snow removal resistance of the vehicle model with the change of angle of attack is also verified; the specific simulation results are shown in the table below:

[0230]

[0231] The simulation results show that the effect of angle of attack changes on the snow removal resistance of the entire vehicle is smaller than that of a single snow plow. This phenomenon is due to the train head model compensating for the changes in snow removal area caused by angle of attack changes in the single snow plow case. However, the snow removal path can be seen to have a certain impact on the distribution of the snow removal path during driving.

[0232] Therefore, the selection recommendations for this application are primarily based on snow removal resistance, with snow removal path and model factors as secondary considerations. However, since the impact of the suppression angle on snow removal resistance is not obvious, and the snow depth considered above is relatively extreme, the suppression angle cannot function properly, and its impact on the snow removal path under these conditions is unclear. Considering the use of snow plows under normal snow depths, this article further considers the impact of the suppression angle on snow removal resistance and path under lower snow depths.

[0233] At a snow depth of 400mm, the impact of the restraint angle on the snow removal path is not significant due to the large amount of snow removed and the high speed. Here, we consider the impact of the restraint angle on snow removal resistance and path under a smaller snow depth. The simulation was performed with a snow depth of 150mm and a driving speed of 30m / s, while keeping all other parameters unchanged. The results are shown in the following table:

[0234]

[0235] refer to Figure 22 and Figure 23 As shown in the figure, the snow removal width increases with increasing suppression angle, and the snow particles move faster during the snow removal process. Comparing the snow removal heights at different suppression angles reveals that at smaller suppression angles, the snow particles tend to be distributed at lower heights in the rear snow removal path, and the distribution of the discharged snow particles is more concentrated and orderly. Clearly, a smaller suppression angle is more beneficial for controlling the movement path of the discharged snow particles during driving.

[0236] Therefore, the following snow plow selection recommendations are given: (1) The opening angle has the most obvious effect on the snow removal resistance. Considering the snow removal resistance, it is recommended to take an opening angle of 60°; (2) When the inclination angle is around 45°, the snow removal resistance in the X direction is the smallest. It is recommended to take an inclination angle of 45°; (3) For the angle of attack, the snow removal path is comprehensively considered: From the above results, it can be seen that the change in the angle of attack has little effect on the snow removal force and snow removal width in the Z direction, but it has a certain effect on the snow removal height, which is reflected in the fact that the snow particles at high places decrease as the angle of attack increases. At the same time, considering that the increase in the angle of attack will lead to an increase in the snow removal resistance, it is recommended to take an angle of attack of 5°; (4) Under the condition of 400mm snow depth, the suppression angle has no obvious effect on the snow removal path, but under the condition of smaller snow depth, the snow removal height is better controlled when the suppression angle is 29°, while having smaller snow removal resistance. It is recommended to take a suppression angle of 29°; In summary, it is recommended to select a snow plow with the shape parameters of 60 (opening angle) - 45 (inclination angle) - 5 (angle of attack) - 29 (suppression angle).

[0237] Snow depth significantly impacts the stress state of the train body during operation. Considering that in actual engineering, trains must not only operate normally in shallow snow conditions but may also operate at a limited speed in conditions with deeper snow depths, this application studies the operating speeds in environments with varying snow depths. Using numerical simulation methods, this application derives the train stress and snow removal paths under varying snow depths and operating speeds. By applying constraints, this application determines the maximum speed that can meet actual operating requirements under varying snow depths, providing a reference for train operation in snowy environments.

[0238] Refer to the first item of the pass / fail criteria for snow plows in Section 5.2.4 of the European standard "DSF / prEN 16251-2011", namely, "the wheel weight reduction should not exceed 7%", to conduct a quantitative assessment of whether the snow plow meets the driving requirements; at the same time, use the second requirement, namely, "during the test, the driver should be able to see clearly" as an auxiliary condition for a qualitative assessment. The wheel weight reduction refers to the load on the wheels caused by the lifting force generated by the snow removal process during the operation of the vehicle. The reduction in wheel weight will have a great impact on driving safety. Since the snow plow is installed at the front end of the lead vehicle, its effect on the wheel weight reduction only exists on the lead vehicle. Here, only the wheel weight reduction of the lead vehicle is considered. Specifically, the following formula should be met:

[0239] Where Δp is the wheel weight reduction caused by snow removal, and p is the average net wheel weight.

[0240] According to the design working conditions, the weight of the leading vehicle is 64t, that is, p = 640kN. From the calculation formula, it can be known that the reduction in wheel weight caused by the snow removal process should satisfy Δp≤44.8kN.

[0241] Therefore, this application selects 150mm, 300mm, 600mm, and 800mm snow depths for numerical simulation, and the snow removal depth is obtained by the lower edge of the snow plow being 150mm above the rail surface. Figure 24 As shown in the figure, considering the influence of snow spreading from the rail surface to the bottom edge of the snow plow on snow removal, the pre-snow spreading depth in the simulation is taken as the snow depth, and the snow removal height is ensured by adjusting the model height. Figure 24 shown.

[0242] According to the above research, as the snow depth increases, the wheel weight reduction caused by snow removal changes significantly, which will lead to a large change in the speed limit. In order to improve the calculation efficiency, the starting value of the driving speed under the deep snow condition is selected based on the simulation results of the shallow snow depth condition. Then, the speed is gradually reduced by 20 km / h until the constraint conditions are met. The specific conditions are summarized in the following table:

[0243]

[0244] Through numerical simulation, the reduction in wheel weight caused by snow removal under various working conditions is obtained. Combined with the snow removal path, the maximum operating speed that meets the requirements under different snow depths is obtained. According to the requirements of the task book, the pressure values of each pressure measuring point at the maximum driving speed are given, and the snow removal resistance in the corresponding direction of travel is given for reference.

[0245] The simulation results show that under the operating conditions of a snow depth of 300 mm (or a snow removal depth of 150 mm), the train can run at a speed of 170 km / h while ensuring that the wheel load reduction and snow removal path meet the requirements. The specific results are as follows:

[0246]

[0247] The pressure at each measuring point on the front of the vehicle when driving at the limit speed under the snow depth condition is as follows:

[0248]

[0249] In summary, under the 300mm snow depth condition, a maximum speed between 170km / h and 190km / h meets the aforementioned wheel weight reduction and driver visibility requirements. The vertical snow-clearing force results show that under this condition, at full speed, the vertical snow-clearing force exceeds the limit by only 23.9kN, 1.53 times the limit. This is significantly different from the vertical snow-clearing force, indicating that snow-clearing conditions are still relatively reasonable.

[0250] The simulation results show that under the operating conditions of a snow depth of 600 mm (or a snow removal depth of 450 mm), the train can run at a speed of 50 km / h while ensuring that the wheel load reduction and snow removal path meet the requirements. The specific results are as follows:

[0251]

[0252] The pressure at each measuring point on the front of the vehicle when driving at the limit speed under the snow depth condition is as follows:

[0253]

[0254] In this operating condition, the snow removal depth exceeds the maximum snow plow height, and other parts of the vehicle body gradually begin to directly participate in the snow removal process. The pressure results also show that under this operating condition, the vehicle body begins to participate in the snow removal process at measuring points 5 and 6.

[0255] The simulation results show that under the operating conditions of a snow depth of 800 mm (or a snow removal depth of 650 mm), the train can run at a speed of 30 km / h while ensuring that the wheel load reduction and snow removal path meet the requirements. The specific results are as follows:

[0256]

[0257] The pressure at each measuring point on the front of the vehicle when driving at the limit speed under the snow depth condition is as follows:

[0258]

[0259] This working condition is the maximum snow depth in this report. Under this working condition, the pressure results show that the structures at all measuring points participate in the snow removal process, and the speed limit is further reduced.

[0260] By observing the pressure distribution on the vehicle's front and snowplow surfaces during snow removal, it can be seen that the lower surface of the vehicle's front protrusion generates greater pressure when the snow depth exceeds the normal snowplow clearance height. In extreme snow conditions, reducing the front protrusion can be considered. Simultaneously, observing the snow removal paths of both a single snowplow and a full vehicle model reveals that the protrusion plays a beneficial role in controlling the height of front-end snow overflow during driving. Therefore, when shortening the protrusion to reduce wheel weight, care should be taken to ensure the cab's line of sight, i.e., the height of front-end snow drift, during driving.

[0261] According to the above-mentioned speed limit and drag lift change results, it can be found that when the snow depth increases from 150mm to 250mm, the snow depth reaches the normal snow-discharging height of the snow plow. During the snow-discharging process, the snow particles are squeezed, resulting in a more unreasonable snow-discharging form (the surface of the vehicle's front structure, except for the snow plow, participates in the snow-discharging process). At the same time, the speed limit changes most significantly at this stage, and the speed limit is reduced from 170km / h to 190km / h at 150mm to 70m / h to 90km / h. Therefore, when the vehicle needs to operate in an environment with a deeper snow depth, the normal operating height of the snow plow, that is, the height of the downforce wing tip, should be increased during the design. Figure 25 shown.

[0262] Therefore, this application uses the two evaluation criteria of wheel weight reduction less than 7% and snowfall path that ensures visibility from the cab as the evaluation criteria. The maximum train speed under different snow depths is explored through numerical simulation methods. The results are summarized in the following table:

[0263]

[0264] The following conclusions can be drawn from the above results: (1) In order to reduce the amount of wheel weight loss during snow removal, the distance from the snow plow to the front end of the vehicle can be shortened while ensuring the driver's cab's line of sight; (2) When the operating environment has a large snow depth, the snow plow height should be appropriately increased to ensure the rationality of the snow removal process.

[0265] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A simulation and optimization design method for EMU snow plow based on multi-physics field coupling, characterized in that: The following steps are involved: Step S1: collecting snow environment parameters of the target route, wherein the environment parameters include at least snowfall data and snow accumulation data; Step S2: Establish a soft ball model of snow particles and use the Hertz-MindlinwithJKRCohesion contact model to calculate the contact force between snow particles; Step S3: Based on the wall snow cover criterion and using the discrete phase model, a snow cover simulation is performed, and then the correctness of the snow removal resistance formula in the snow cover simulation is verified by comparing it with the preset snow removal experimental data; Step S4: A parametric modeling method is used to establish a snowplow model. Based on the simulation results, shape parameters are introduced into the snow removal resistance formula to simplify the driving resistance coefficient. The snow removal performance of the snowplow under the preset shape parameter combination is then evaluated, and the snow removal resistance formula of the snowplow is determined. Step S5: Adding a vehicle head to the snow plow model to form a complete vehicle model and performing simulation based on the complete vehicle model, thereby revising the snow removal resistance formula and generating a snow plow selection recommendation; Step S6: Simulating the vehicle at a preset snow depth and driving speed to obtain the vehicle forces and snow removal paths under different snow depth and driving speed conditions, and then generating the maximum speed that can meet actual operating requirements under different snow depth conditions based on preset constraints; Step S7: Output the optimal design parameters of the snow plow.

2. The method for simulation and optimization design of a snow plow for EMU obstacle removal based on multi-physics field coupling according to claim 1 is characterized in that: In step S2, the method for establishing the snow particle soft ball model includes: The soft ball model simplifies the inter-particle contact process into the damped vibration of a spring oscillator, and its motion equation is: Where x is the displacement from the equilibrium position, m is the mass of the oscillator, c and k are the spring damping coefficient and elastic coefficient respectively; Among them, the normal elastic coefficient k n for: Where E and v are the elastic modulus and Poisson's ratio of the particle material, respectively, R is the particle radius, and the subscripts i and j represent the particles i and j that are in contact, respectively. When particles i and j are of homogeneous materials and have equal particle sizes, then k n for Among them, the tangential elastic coefficient k t for: Among them, α is the normal overlap, G i and G j are the shear moduli of particles i and j, respectively. When particles i and j are of homogeneous materials and have the same particle size, k t for 3. The method for simulating and optimizing the design of a snow plow for an EMU based on multi-physics field coupling according to claim 2 is characterized in that: Methods for determining the damping coefficient include: If a spring oscillator with mass m is in a critical damping state, the mechanical energy decays at the fastest speed. At this time, the normal damping coefficient c is n and the tangential damping coefficient c t They are: Or, the damping coefficient is coupled with the restitution coefficient e: Here, e is determined by experiment.

4. The method for simulating and optimizing the design of a snow plow for an EMU based on multi-physics field coupling according to claim 1 is characterized in that: In step S2, the method for defining the Hertz-MindlinwithJKRCohesion contact model includes: The JKR normal force is based on the overlap δ and the interaction parameter, surface energy γ: Where, E* is the equivalent Young's modulus, R* is the equivalent radius; Damping force The expression is: in, is the equivalent mass, is the normal component of the relative velocity, β and S n The expression is: Tangential force F t The expression is: Among them, S t is the tangential overlap, G* is the equivalent shear modulus, is the tangential component of the relative velocity; where the tangential force is affected by Coulomb friction μ s F n Limit, where μ s is the coefficient of static friction, and rolling friction exerts a torque on the contact surfaces: τ i =-μ r F n R i ω i , where μ r is the rolling friction coefficient, R i is the distance from the contact point to the center of mass, ω i is the unit angular velocity vector of the object at the contact point; The maximum gap between particles with non-zero cohesion is calculated by the following formula: When δ<δ c , the model returns 0; When the particles are not actually in contact and the separation is less than δ c When , the cohesion reaches its maximum value, so the maximum cohesion is: The force required to separate the two particles depends on the surface tension of the liquid γ s and wetting angle.

5. The method for simulation and optimization design of a snow plow for EMU obstacle removal based on multi-physics field coupling according to claim 1 is characterized in that: In step S3, the discrete phase model includes: The continuity and momentum conservation equations of the discrete phase model are: Among them, ρ f , u and P are the density, velocity and pressure of the fluid respectively, t is time, g is the acceleration of gravity, α f is the fluid volume fraction, α f =1-V p / V c Calculate, V c 、V p are the total volume of the control unit and the volume of the particle respectively; F f-p is the force between the air phase and the snow phase, mainly the particle drag force F drag ; τ is the fluid viscous stress tensor, Calculate, where I is the unit tensor, μ f is the fluid viscosity including kinematic viscosity and turbulent viscosity; For the snow phase, the snow particles are regarded as spheres, and the force balance equation is: Among them, m p 、u p and ρ p are the mass, velocity and density of snow particles respectively, and F is the additional external field; Among them, d p is the snow particle size, C D is the particle drag coefficient, which is calculated based on the smooth ball model by the particle Reynolds number R e,p It is determined by a set of empirical constants a1-a3 determined by the range; Calculate particle trajectories: Solve the differential equation along each coordinate direction to obtain the trajectory of the discrete phase; The traditional SIMPLE method is used to solve the governing equations of the fluid phase, and then combined with the explicit time integration method to solve the equations of motion of particles in the flow field.

6. The method for simulating and optimizing the design of a snow plow for an EMU based on multi-physics field coupling according to claim 5 is characterized in that: In step S3, the wall snow cover criteria include: The incident angle α is smaller than the capture angle α t When , the snow particles are captured by the wall; When the local friction velocity is less than the preset threshold friction velocity, the snow particles will not be carried away by the wind and will accumulate; Particle collision velocity u p Less than the critical capture velocity u trap When , the wall's viscous force on the particles is larger, and the particles will be captured; When the particles meet the above three wall-covering criteria, the snow particles will be captured by the wall and stably accumulate on the wall to achieve snow covering the wall.

7. The method for simulating and optimizing the design of a snow plow for an EMU based on multi-physics field coupling according to claim 1 is characterized in that: In step S4, the method of establishing the snow plow model using the parametric modeling method includes: Step S41: Select a horizontal plane and determine the specific position and length of the line segment based on the opening angle and the vehicle width; Step S42: Selecting a vertical symmetry plane of the snow plow, and determining the snow plow leading edge segment and the attack angle constraint line based on the snow plow total height and the lower half height; Step S43: generating the snow plow lower half plane based on the determined line segments; Step S44: Projecting the bottom line onto the snow plow upper edge plane by converting the entity reference to determine the reference plane where the rear spline curve is located; Step S45: Determine the rear spline curve based on the restraint angle and the vehicle width, with the boundary conditions being tangency to the plane determined by the angle of attack, restraint angle constraint, and restraint angle height constraint; Step S46: determining a front spline curve, wherein the boundary conditions are the protruding distance of the front end of the snow plow and the tangency with the angle of attack plane and the line segment; Step S47: Generate curved surfaces and planes to complete the modeling of half of the snow plow, and then mirror the entity to complete the snow plow modeling.

8. The method for simulating and optimizing the design of a snow plow for an EMU based on multi-physics field coupling according to claim 1 or 7, characterized in that: In step S4, the method further includes: The calculation expression of snow removal resistance is: F p =ChwρV 2 , where h is the snow removal depth, w is the snow removal width, ρ is the snow density, and V is the driving speed; C is the calculated driving resistance coefficient, C = C0 + C1 + C2, where C0 is linearly interpolated by looking up the preset table, and C1 = 0.028 (δ + 2γ) × 10 -4 , δ is the suppression angle, γ is the angle of attack; in C2: when θ<45°, C2={5.64+0.008×(45-θ)}×10 -4 ; When 45°≤θ≤90°, C2={5.64+0.0025×(θ-45)}×10 -4 , where θ is the inclination angle.

9. The method for simulating and optimizing the design of a snow plow for an EMU based on multi-physics field coupling according to claim 1 is characterized in that: In step S6, the method includes: With the wheel load reduction rate not exceeding the preset value and the driver's clear vision as constraints, simulations are performed at preset driving speeds in snow depth environments to generate the maximum speed that can meet actual operating requirements under different snow depth conditions.

10. A method for simulating and optimizing the design of a snow plow for clearing obstacles on an EMU based on multi-physics field coupling, using the method according to any one of claims 1 to 9, characterized in that: include: A data acquisition module, configured to acquire snow environment parameters of a target route, wherein the environment parameters include at least snowfall data and snow accumulation data; Model processing module, used to establish a soft ball model of snow particles and use the Hertz-MindlinwithJKRCohesion contact model to calculate the contact force between snow particles; The simulation module is used to simulate snow cover based on the wall snow cover criterion and adopt a discrete phase model. The correctness of the snow removal resistance formula in the snow cover simulation is verified by comparing it with the preset snow removal experimental data. The resistance determination module is used to establish a snowplow model using a parametric modeling method. Based on the simulation results, shape parameters are introduced into the snow removal resistance formula to simplify the driving resistance coefficient. The snow removal performance of the snowplow under the preset shape parameter combination is then evaluated, and the snow removal resistance formula of the snowplow is determined. An obstacle removal model selection module is used to add a vehicle head to the obstacle removal snow plow model to form a complete vehicle model and perform simulation based on the complete vehicle model, thereby revising the snow removal resistance formula and generating obstacle removal snow plow model selection recommendations; Design an optimization module to simulate the speed of the preset snow depth environment, obtain the train force and snow removal path under different snow depth and driving speed conditions, and then generate the maximum speed that can meet the actual operating requirements under different snow depth conditions based on the preset constraints; Design an output module to output the optimal design parameters of the snow plow.

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