Dust suppression method and system based on heavy haul railway tunnel dust transport flow field prediction

Abstract

The application provides a dust suppression method and system based on dust migration flow field prediction of heavy haul railway tunnel, and relates to the technical field of track engineering. The method comprises the following steps: obtaining parameter information of the heavy haul railway tunnel, constructing a physical model and test parameters based on the parameter information; performing simulation test based on the physical model and the test parameters, and obtaining test data in the test process; constructing a dust migration flow field prediction model of the heavy haul railway tunnel according to the test data, and obtaining a dust concentration field and a dust trajectory field of the heavy haul railway tunnel through the dust migration flow field prediction model; optimizing a dust suppression spraying scheme according to the dust concentration field and the dust trajectory field, and obtaining an optimal dust suppression spraying scheme of the heavy haul railway tunnel; and performing dust suppression spraying on the heavy haul railway tunnel through the optimal dust suppression spraying scheme. The application solves the problem that the existing dust suppression method does not consider the influence of dust migration trajectory and concentration distribution on dust suppression effect.

Description

Technical Field

[0001] This invention relates to the field of railway engineering technology, and more specifically, to a dust suppression method and system based on the prediction of dust transport flow field in heavy-haul railway tunnels. Background Technology

[0002] Currently, coal remains the primary energy source consumed in China. Due to the uneven distribution of coal resources—abundant in the west and scarce in the east, and rich in the north and poor in the south—over 70% of coal needs to be transported from high-yield regions such as Shanxi and Inner Mongolia to various parts of the country via heavy-haul trains. When heavy-haul coal trains pass through tunnels, the airflow field at the cross-section inevitably changes, causing coal to scatter and coal dust particles to become suspended on the surface. This results in significant resource waste and greatly impacts train safety and the health of workers.

[0003] Although numerous dust control measures are currently implemented in heavy-haul railway tunnels, there is a lack of thorough understanding of the dust transport patterns at the tunnel entrances, exits, and interiors when heavy-haul coal trains pass through. Consequently, there is a lack of targeted dust suppression measures. For example, the common method of spraying dust suppressants, which involves uniformly spraying dust suppressants inside the tunnel, significantly increases material consumption costs and has poor dust control effects.

[0004] Therefore, there is an urgent need for a dust suppression method that considers the measurement and understanding of dust transport trajectory and concentration distribution, so as to provide a reference for the optimized layout of dust suppression measures in actual heavy-haul railway tunnels. Summary of the Invention

[0005] The purpose of this invention is to provide a dust suppression method and system based on the prediction of dust transport flow field in heavy-haul railway tunnels, so as to improve the above-mentioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0006] Firstly, this application provides a dust suppression method based on the prediction of dust transport flow field in heavy-haul railway tunnels, including:

[0007] Obtain parameter information of heavy-haul railway tunnels, and construct physical models and test parameters based on the parameter information. The parameter information includes tunnel length, actual wind speed, train running speed and dust leakage intensity.

[0008] Simulation experiments are conducted based on physical models and experimental parameters to obtain test data during the experiment. The test data includes dust trajectory data, dust concentration data, and wind speed data.

[0009] Based on the test data, a dust transport flow field prediction model for heavy-load railway tunnels was constructed, and the dust concentration field and dust trajectory field of heavy-load railway tunnels were obtained through the dust transport flow field prediction model.

[0010] The optimal dust suppression spraying scheme for heavy-haul railway tunnels was obtained by optimizing the dust concentration field and dust trajectory field.

[0011] Dust suppression spraying was carried out on heavy-haul railway tunnels using the optimal dust suppression spraying scheme.

[0012] Secondly, this application also provides a dust suppression system based on the prediction of dust transport flow field in heavy-haul railway tunnels, comprising:

[0013] The acquisition module is used to acquire parameter information of heavy-haul railway tunnels, and construct physical models and test parameters based on the parameter information. The parameter information includes tunnel length, actual wind speed, train running speed and dust leakage intensity.

[0014] The test module is used to conduct simulation tests based on physical models and test parameters, and to obtain test data during the test process. The test data includes dust trajectory data, dust concentration data, and wind speed data.

[0015] The construction and prediction module is used to construct a dust transport flow field prediction model for heavy-load railway tunnels based on test data, and obtain the dust concentration field and dust trajectory field of heavy-load railway tunnels through the dust transport flow field prediction model.

[0016] The optimization module is used to optimize the dust suppression spraying scheme based on the dust concentration field and dust trajectory field, so as to obtain the optimal dust suppression spraying scheme for heavy-haul railway tunnels.

[0017] The dust suppression module is used to suppress dust in heavy-haul railway tunnels using the optimal dust suppression spraying scheme.

[0018] The beneficial effects of this invention are as follows: By constructing a physical model of a heavy-haul railway tunnel and conducting simulation experiments, the tunnel route and the length of the open section can be freely adjusted. Parameter mapping simulates dust transport flow fields under multiple working conditions, and test data is collected. A predictive model is constructed using the experimental data, and parameters are calibrated to improve the prediction accuracy of dust concentration and trajectory fields. The spraying scheme is optimized based on the predicted dust distribution characteristics, breaking through the traditional uniform spraying mode and achieving dual-dimensional control of concentration and trajectory. Furthermore, by establishing a classic working condition library and adaptive extrapolation, the optimal dust suppression scheme for complex working conditions can be quickly obtained, reducing material consumption costs while improving dust suppression effects.

[0019] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the dust suppression method based on the prediction of dust transport flow field in heavy-haul railway tunnels as described in an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of the physical model described in the embodiments of the present invention;

[0023] Figure 3 This is a schematic diagram of the movable roadbed steel frame described in an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of the train model described in an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] Example 1:

[0028] This embodiment provides a dust suppression method based on the prediction of dust transport flow field in heavy-load railway tunnels.

[0029] See Figure 1 The figure shows that the method includes steps S1, S2, S3, S4, and S5.

[0030] Step S1: Obtain parameter information of heavy-haul railway tunnels, and construct physical models and test parameters based on the parameter information. The parameter information includes tunnel length, actual wind speed, train running speed and dust leakage intensity.

[0031] In step S1, the construction of the physical model and experimental parameters based on parameter information includes:

[0032] Step S11: Set the simulated tunnel length according to the tunnel length and geometric similarity ratio, wherein the simulated tunnel length includes the simulated open section length and the simulated tunnel section length;

[0033] In this embodiment, the simulated tunnel length is:

[0034]

[0035] In the formula, l model l represents the simulated tunnel length. real The actual tunnel length of a heavy-haul railway tunnel is represented by Δd, where k represents the geometric similarity ratio. adjust This indicates the adjustment amount of the open-air section length, which is related to the position of the blower and the acceleration / deceleration distance of the train.

[0036] Step S12: Construct a physical model based on the simulated tunnel length;

[0037] In this embodiment, the physical model adopts Figure 2 The test apparatus shown is composed of, for example Figure 3 The roadbed steel frame shown is composed of multiple movable steel frames, divided into open-air sections and tunnel sections. The length of the tunnel route and the length of the route outside the tunnel can be freely controlled (the number of individual roadbed steel frames is determined by simulating the length of the open-air section and the length of the tunnel section).

[0038] The open-air section includes a track subgrade steel frame, a rail model, and adjustable support legs. The tunnel section includes a track subgrade steel frame, a rail model, adjustable support legs, high-transparency acrylic panels, and a light-blocking cloth. The adjustable support legs allow for height adjustment of the track subgrade steel frame. The track subgrade steel frame is positioned above the adjustable support legs. The rail model is laid on the surface of the track subgrade steel frame and secured with screws. High-transparency acrylic panels are installed on both sides of the track subgrade steel frame to simulate the tunnel chamber. A smooth, matte white light-blocking cloth covers the back of the tunnel chamber to reduce interference from external light on image measurements.

[0039] Simultaneously adopting, such as Figure 4The train model shown simulates train movement. The train model consists of an open-top model box, a power unit, and a dust generator. The open-top model box, made of aluminum alloy and plastic, is positioned above the rail model and coupled via wheelsets, simulating an open coal wagon. The power unit includes a permanent magnet synchronous motor and a traction rope. The traction rope is positioned on the lower side of the open-top model box in the direction of travel and connects to an externally mounted permanent magnet synchronous motor at the other end of the track. The motor's operation pulls the model box. The dust generator, fixed inside the open-top model box, includes a dust collection box, a sealing baffle, an electric pump, and dust nozzles. Opening the sealing baffle allows the electric pump to extract coal dust from the dust collection box, which is then sprayed out by the upward-facing dust nozzles to simulate the rising of coal dust adhering to the surfaces inside the wagon.

[0040] The air blowing device includes a blower and an air outlet pipe. The air outlet pipe is a flexible hose. The air blowing device can be moved freely to simulate the natural air blowing in at the entrance and exit of the tunnel section.

[0041] The measuring device includes an industrial camera, a computer, a dust concentration sensor, a wind speed sensor, a strip LED light source, and a speed sensor. The industrial camera is positioned on the front side of the tunnel housing and connects to the computer for communication, control, and power supply, used to monitor the movement trajectory of coal dust inside the tunnel and at its entrances and exits. The dust concentration sensor and wind speed sensor are evenly distributed on the top inner side of the tunnel housing, with their power supply lines extending along the opening at the contact surface between the acrylic plate and the roadbed steel frame, used to monitor the coal dust concentration and wind speed inside the tunnel. The strip LED light source is positioned on the top inner side of the tunnel housing, consistent with the tunnel length, with its power supply line extending along the opening at the contact surface between the acrylic plate and the roadbed steel frame. The speed sensor is positioned on a train model to acquire the train model's running speed, communicating via Bluetooth. Therefore, based on the parameter information of a heavy-haul railway tunnel, a physical model can be built, allowing free control of the tunnel route length and the external route length within the testing device.

[0042] Step S13: Set the blower parameters according to the actual wind speed and geometric similarity ratio, wherein the blower parameters include simulated air outlet speed and simulated air outlet angle;

[0043] In this embodiment, the simulated air outlet angle is a dimensionless parameter, and the simulated air outlet velocity is:

[0044]

[0045] In the formula, F model F represents the simulated airflow velocity. real This represents the actual wind speed, and k represents the geometric similarity ratio. Furthermore, the actual wind speed is the wind speed at the tunnel entrance.

[0046] Step S14: Set the simulated train speed according to the train running speed and geometric similarity ratio, and set the simulated dust spraying amount according to the dust leakage intensity and geometric similarity ratio;

[0047] In this embodiment, based on the Froude number similarity criterion, the train speed and the simulated train speed satisfy the following:

[0048]

[0049] In the formula, v model v represents the simulated train speed. real The value represents the actual train speed in the heavy-load railway tunnel, and k represents the geometric similarity ratio.

[0050] The simulated dust spray volume meets the following requirements:

[0051]

[0052] In the formula, q model q represents the simulated dust spray volume. real denoted by , where k represents the actual dust leakage intensity, and k represents the geometric similarity ratio.

[0053] Step S15: Use the blower parameters, simulated train speed, and simulated dust spraying amount as the test parameters for the physical model.

[0054] Step S2: Conduct a simulation experiment based on the physical model and experimental parameters to obtain test data during the experiment. The test data includes dust trajectory data, dust concentration data, and wind speed data.

[0055] In this embodiment, a simulation test is conducted using calculated test parameters. The blower is turned on to blow air pressure into the tunnel. The sealing baffle and dust nozzle of the dust generator are opened, and the coal dust in the dust storage box is sprayed upward. The permanent magnet synchronous engine is started to pull the train car body. After the train car body passes through the tunnel, the engine is turned off, the train car body decelerates and stops, and the test ends. Test data during the simulation test is collected by dust concentration sensor, wind speed sensor, speed sensor and industrial camera.

[0056] The collected dust concentration, wind speed, and train speed were recorded temporally and spatially. Simultaneously, image processing was performed on photographs taken inside the tunnel (including the entrance, tunnel section, and exit section), and a cross-correlation algorithm was used to calculate the dust trajectory field, obtaining dust trajectory data. Local wind speeds measured at spatially discrete points were compiled into wind speed data. Simultaneously, dust concentration data measured at spatially discrete points were compiled into dust concentration data.

[0057] Step S3: Construct a dust transport flow field prediction model for heavy-load railway tunnels based on test data, and obtain the dust concentration field and dust trajectory field of heavy-load railway tunnels through the dust transport flow field prediction model;

[0058] In this embodiment, a two-dimensional flow field model of dust transport trajectory is established based on fluid mechanics and particle kinematics. After optimization using test data, an accurate dust transport flow field prediction model is obtained.

[0059] In step S3, the step of constructing a dust transport flow field prediction model for heavy-haul railway tunnels based on test data includes:

[0060] Step S31: Construct a wind speed model and build the motion equation of the dust based on the wind speed model;

[0061] In this embodiment, it is assumed that the motion of dust can be described by the continuity equation and momentum equation in fluid dynamics. The dust is considered as small particles moving with the fluid, and its motion is affected by gravity, wind force, and spray intensity. The equation of motion for the dust can be expressed as:

[0062]

[0063] In the formula, r represents the displacement vector of the dust, t represents time, and v wind (x) represents the wind speed vector located at the horizontal coordinate x, v train V represents the train's speed vector. dust This represents the velocity vector of dust generated during the spraying process, including the random motion of dust caused by factors such as airflow, turbulence, and diffusion.

[0064] In this model, it is assumed that the blower is placed at the tunnel entrance, and the wind speed gradually decreases along the tunnel direction. Therefore, an exponential or linear decay model is used. The wind speed vector is determined by the blower's output air intensity and direction, and can be expressed as:

[0065]

[0066] In the formula, v wind (x) represents the wind speed vector located at the horizontal coordinate x, F represents the wind speed, α represents the attenuation factor, and θ represents the wind speed angle.

[0067] Therefore, the equation of motion for dust can be further expressed as:

[0068]

[0069] In the formula, t represents time, F represents wind speed, α represents the attenuation factor, θ represents the wind speed angle, and v represents the train speed along the x-axis. dust,x and v dust,yThese represent the dust velocities generated at the positions on the horizontal axis (x) and vertical axis (y) during the spraying process, respectively. dust,x and v dust,y In reality, it refers to the random movement or diffusion rate of dust at the microscopic scale.

[0070] In this embodiment, the opening can be taken as the origin, the tunnel as the x-direction, and the perpendicular tunnel plane as the y-direction.

[0071] Step S32: Construct the dust flux based on the diffusion coefficient, Dirac function, and dust leakage intensity; and construct the dust diffusion-convection equation based on the dust flux.

[0072] In this embodiment, the dust concentration distribution can be described by the diffusion-convection equation. It is assumed that the dust concentration in the tunnel section and the open-air section satisfies the following formula:

[0073]

[0074] In the formula, C represents the dust concentration at the x-axis and y-axis positions, t represents time, and J represents the dust flux. Let denot be the gradient operator, D be the diffusion coefficient, q be the dust leakage intensity, δ(·) be the Dirac function, and x be the gradient operator. source and y source The x and y coordinates represent the dust spray source point, respectively.

[0075] Step S33: Optimize the motion equation and diffusion-convection equation of dust using test data to obtain a dust transport flow field prediction model, which includes the optimal motion equation and the optimal diffusion-convection equation.

[0076] Step S33 includes:

[0077] Step S331: Perform inverse and forward simulations using dust trajectory data, and match the results using the minimum trajectory error method to obtain the model parameters of the motion equations;

[0078] In this embodiment, the simulated trajectory is matched with the observed trajectory through inverse simulation and forward simulation to obtain the model parameters of the motion equation. The matching method using the minimum trajectory error satisfies the following formula:

[0079]

[0080] In the formula, This means finding the minimum value with parameter θ as the variable. Indicates at t i At time t, the position vector of the i-th dust particle is calculated based on the parameter θ and the equation of motion. Indicates at t iAt time t, the actual position vector of the i-th dust particle obtained from the dust trajectory data, ‖·‖ represents the Euclidean distance, and the parameter θ includes model parameters such as attenuation factor, wind speed, wind speed angle, diffusion coefficient, and dust leakage intensity.

[0081] Step S332: Interpolate the dust concentration data and wind speed data to obtain the continuous field of dust concentration and the continuous field of wind speed;

[0082] Step S333: Optimize the wind speed model through the wind speed continuous field to obtain the optimal wind speed model;

[0083] Step S334: Update the motion equations using the optimal wind speed model and model parameters to obtain the optimal motion equations;

[0084] In this embodiment, the optimal equation of motion is expressed as:

[0085]

[0086] In the formula, r represents the displacement vector of the dust, and t represents time. This indicates the optimized wind speed. This represents the optimized attenuation factor. This indicates the optimized wind speed angle. This represents the optimized train velocity vector (velocity along the x-axis is...). The velocity along the y-axis is 0), and η(t) represents random noise with respect to time t.

[0087] Step S335: Optimize the diffusion coefficient and dust leakage intensity through the dust concentration continuous field, and update the diffusion-convection equation with the optimized diffusion coefficient and dust leakage intensity to obtain the optimal diffusion-convection equation.

[0088] In this embodiment, the optimal diffusion-convection equation is expressed as:

[0089]

[0090] In the formula, C represents the dust concentration at the x-axis and y-axis positions, and t represents time. Represents the gradient operator. This represents the optimized wind speed vector located at the x-axis and y-axis. This represents the optimized train speed vector, which is actually... The optimized diffusion coefficient is represented by S(x,y,t), which represents the dust input at time t at the spray point located at the horizontal coordinate x and the vertical coordinate y. It is determined by the dust spray location and the dust leakage intensity.

[0091] Step S4: Optimize the dust suppression spraying scheme based on the dust concentration field and dust trajectory field to obtain the optimal dust suppression spraying scheme for heavy-haul railway tunnels;

[0092] In step S4, the step of obtaining the optimal dust suppression spraying scheme is as follows:

[0093] Step S41: Discretize the heavy-haul railway tunnel into multiple regional units and set a concentration threshold;

[0094] In this embodiment, the dust suppressant spraying device inside the tunnel is discretized into N regional units, and a concentration threshold C is set. th .

[0095] Step S42: Construct a formula for calculating the first dust suppressant spraying amount based on the concentration threshold;

[0096] In this embodiment, the amount of the first dust suppressant sprayed is dynamically set according to the dust concentration of each area unit, specifically as follows:

[0097]

[0098] In the formula, Q ca k represents the amount of dust suppressant sprayed in the first dust suppressant application in the a-th area unit. c The dust suppressant efficiency coefficient represents the amount of spray required per unit of excess concentration (calibrated experimentally, for example, by testing a dust suppressant to reduce concentration by 1 mg / m³ in a laboratory setting). 3 (Required amount of dust to be sprayed), C a C represents the dust concentration in the a-th region unit. th β represents the concentration threshold, and β represents the nonlinear adjustment factor, which can be determined based on the concentration of the regional unit.

[0099] Step S43: Obtain the dust concentration of each area unit through the dust concentration field, and calculate the first dust suppressant spraying amount of each area unit based on the dust concentration of each area unit and the first dust suppressant spraying amount calculation formula;

[0100] Step S44: Construct the formula for calculating the amount of the second dust suppressant sprayed;

[0101] In this embodiment, the dust trajectory field (actually a velocity vector field) is used to identify dust accumulation areas (areas where dust accumulates). The spraying amount is increased in these areas to prevent dust accumulation. Therefore, a formula for calculating the second dust suppressant spraying amount is constructed as follows:

[0102]

[0103] In the formula, Q va k represents the amount of the second dust suppressant sprayed in the a-th area unit. vThis represents the dust suppressant efficiency coefficient related to the trajectory, and max(·) indicates taking the maximum value. Let v represent the gradient operator. a This represents the dust trajectory in the a-th region unit. This represents the dust accumulation intensity of the a-th regional unit; a higher value indicates a higher risk of dust accumulation.

[0104] Step S45: Obtain the dust aggregation intensity of each area unit based on the dust trajectory field, and calculate the second dust suppressant spraying amount for each area unit based on the dust aggregation intensity;

[0105] Step S46: Calculate the optimal dust suppressant spraying amount for each area unit using the first dust suppressant spraying amount and the second dust suppressant spraying amount;

[0106] In this embodiment, the optimal dust suppressant spraying amount is:

[0107] Q a =Q ca +Q va

[0108] In the formula, Q a Q represents the optimal dust suppressant spraying amount for the a-th regional unit. ca Q represents the amount of dust suppressant sprayed in the a-th area unit. va This indicates the amount of the second dust suppressant sprayed in the a-th area unit.

[0109] Step S47: Obtain the optimal dust suppression spraying scheme for heavy-haul railway tunnels based on the optimal dust suppressant spraying amount.

[0110] In this embodiment, after obtaining the optimal dust suppressant spraying amount for each regional unit, the optimal dust suppressant spraying scheme for heavy-haul railway tunnels is obtained.

[0111] Step S5: Apply the optimal dust suppression spraying scheme to the heavy-haul railway tunnel.

[0112] In this embodiment, by changing the tunnel length, actual wind speed, etc., a corresponding physical model can be constructed for testing, and then the corresponding optimal dust suppression spraying scheme can be solved. Therefore, through multiple tests, a classic working condition library can be obtained, and reasonable extrapolation can be performed to obtain the optimal dust suppression spraying scheme for heavy-load railway tunnels that are not in the classic working condition library.

[0113] Step S5 includes:

[0114] Step S51: Construct a classic working condition library based on the optimal dust suppression spraying schemes corresponding to various typical working conditions of heavy-haul railway tunnels. The classic working condition library includes parameter information, dust trajectory field, dust concentration field and optimal dust suppression spraying scheme.

[0115] In this embodiment, when establishing the classic operating condition library, parameter information is normalized.

[0116] Step S52: Obtain target parameter information for the target heavy-load railway tunnel;

[0117] Step S53: Calculate the distance between the target heavy-haul railway tunnel and typical working conditions based on the target parameter information;

[0118] In this embodiment, the distance is calculated using the following formula:

[0119] d j =ω l (l norm,new -l norm,j ) 2 +ω F (F norm,new -F norm,j ) 2 +ω θ (θ norm,new -θ norm,j ) 2 +ω v (v norm,new -v norm,j ) 2 +ω q (q norm,new -q norm,j ) 2

[0120]

[0121] In the formula, d j ω represents the squared distance between the target heavy-haul railway tunnel and the j-th typical working condition. l ω F ω θ ω v and ω q Both represent weight parameters, F norm,new and F norm,j Let l represent the lengths of the target heavy-haul railway tunnel and the tunnel in the j-th typical working condition, respectively. norm,new and l norm,j Let θ represent the actual wind speeds of the target heavy-haul railway tunnel and the j-th typical working condition, respectively. norm,new and θ norm,j V represents the wind direction of the target heavy-haul railway tunnel and the j-th typical working condition, respectively. norm,new and v norm,j Let q represent the train speeds in the target heavy-haul railway tunnel and the j-th typical working condition, respectively. norm,new and q norm,j Let represent the dust leakage intensity of the target heavy-haul railway tunnel and the j-th typical working condition, respectively. Represents the distance between the target heavy-load railway tunnel and the j-th typical working condition, where |·| indicates taking the absolute value.

[0122] Step S54: Extrapolate the optimal dust suppression spraying scheme based on distance to obtain the optimal dust suppression spraying scheme for the target heavy-haul railway tunnel;

[0123] Step S54 includes:

[0124] Step S541: Set the distance threshold;

[0125] Step S542: Select the typical working condition with the smallest distance as the target heavy-load railway tunnel similar working condition;

[0126] Step S543: Determine whether the similar working conditions are less than the distance threshold. If so, obtain the optimal dust suppression spraying scheme for the target heavy-load railway tunnel based on the similarity criterion. Otherwise, perform weighted interpolation on multiple adjacent classic working conditions of the target heavy-load railway tunnel to obtain the optimal dust suppression spraying scheme for the target heavy-load railway tunnel.

[0127] In this embodiment, if the similarity condition is less than the distance threshold, the location of the area unit is matched first, and then the spraying volume is calculated using a global scaling factor based on the main similarity criteria. Specifically:

[0128]

[0129] In the formula, Q b′,new Let α1 represent the optimal dust suppressant spraying amount for the b′-th regional unit of the target heavy-haul railway tunnel, and Q represent the first global scaling factor. cb Q represents the amount of dust suppressant sprayed in the b-th area unit under similar working conditions. vb q represents the amount of the second dust suppressant sprayed in the b-th area unit under similar working conditions. new and v new q represents the dust leakage intensity and train speed of the target heavy-haul railway tunnel, respectively. s and v s This indicates the intensity of dust leakage and the speed of train movement under similar operating conditions.

[0130] When the target heavy-haul railway tunnel does not match similar working conditions, the optimal dust suppression spraying scheme is obtained by weighted interpolation of multiple neighboring working conditions within the maximum distance threshold, as follows:

[0131]

[0132] In the formula, Q b′,new Let α2 represent the optimal dust suppressant spraying amount for the b′-th regional unit of the target heavy-haul railway tunnel, α2 represent the second global scaling factor, W represent the weight sum, M represent the number of adjacent working conditions, and ω represent the maximum dust suppressant spraying amount. mQ represents the weight of the m-th neighboring case. cb,m and Q vb,m These represent the first and second dust suppressant spray amounts for the m-th adjacent industrial condition and the b-th regional unit, respectively.

[0133] If there are no adjacent working conditions within the maximum distance threshold of the target heavy-load railway tunnel, it is necessary to conduct tests and optimization calculations again to obtain the corresponding optimal dust suppression spraying scheme and add it to the classic working condition library.

[0134] Step S55: Perform dust suppression spraying using the optimal dust suppression spraying scheme for the target heavy-haul railway tunnel.

[0135] In summary, this invention constructs a physical model of a heavy-haul railway tunnel and conducts simulation experiments to simulate tunnels of different lengths and train operation scenarios, meeting diverse testing needs. At the same time, through parameter mapping, it can simulate dust transport flow fields under various complex working conditions, covering different operating scenarios and collecting test data from multiple sources.

[0136] Then, a dust transport flow field prediction model is constructed using test data, realizing a closed-loop process from physical experiments and data acquisition to model optimization. Compared with simple numerical simulation in existing technologies, calibrating model parameters using measured test data improves the prediction accuracy of dust concentration field and dust trajectory field.

[0137] Simultaneously, the spraying scheme is optimized by predicting dust distribution characteristics, changing the high-cost traditional method of uniformly spraying dust suppressants and achieving dynamic control of both concentration and trajectory. Furthermore, by establishing a classic working condition library and designing adaptive extrapolation, the optimal dust suppression spraying scheme for complex working conditions not included in the classic working condition library can be quickly obtained.

[0138] Example 2:

[0139] This embodiment provides a dust suppression system based on the prediction of dust transport flow field in heavy-haul railway tunnels. The system includes:

[0140] The acquisition module is used to acquire parameter information of heavy-haul railway tunnels, and construct physical models and test parameters based on the parameter information. The parameter information includes tunnel length, actual wind speed, train running speed and dust leakage intensity.

[0141] The test module is used to conduct simulation tests based on physical models and test parameters, and to obtain test data during the test process. The test data includes dust trajectory data, dust concentration data, and wind speed data.

[0142] The construction and prediction module is used to construct a dust transport flow field prediction model for heavy-load railway tunnels based on test data, and obtain the dust concentration field and dust trajectory field of heavy-load railway tunnels through the dust transport flow field prediction model.

[0143] The optimization module is used to optimize the dust suppression spraying scheme based on the dust concentration field and dust trajectory field, so as to obtain the optimal dust suppression spraying scheme for heavy-haul railway tunnels.

[0144] The dust suppression module is used to suppress dust in heavy-haul railway tunnels using the optimal dust suppression spraying scheme.

[0145] The construction and prediction module includes:

[0146] The first building unit is used to build a wind speed model, and the motion equation of dust is built based on the wind speed model.

[0147] The second building block is used to construct the dust flux based on the diffusion coefficient, Dirac function and dust leakage intensity, and to construct the dust diffusion-convection equation based on the dust flux.

[0148] An optimization unit is used to optimize the motion equation and diffusion-convection equation of dust using test data to obtain a dust transport flow field prediction model, which includes the optimal motion equation and the optimal diffusion-convection equation.

[0149] The optimization module includes:

[0150] The first setting unit is used to discretize the heavy-haul railway tunnel into multiple regional units and set a concentration threshold.

[0151] The third building unit is used to construct a calculation formula for the first dust suppressant spraying amount based on the concentration threshold.

[0152] The first calculation unit is used to obtain the dust concentration of each area unit through the dust concentration field, and to calculate the first dust suppressant spraying amount of each area unit based on the dust concentration of each area unit and the first dust suppressant spraying amount calculation formula.

[0153] The fourth building unit is used to construct the calculation formula for the second dust suppressant spraying amount;

[0154] The second calculation unit is used to obtain the dust aggregation intensity of each area unit based on the dust trajectory field, and to calculate the second dust suppressant spraying amount of each area unit based on the dust aggregation intensity.

[0155] The third calculation unit is used to calculate the optimal dust suppressant spraying amount for each area unit based on the first dust suppressant spraying amount and the second dust suppressant spraying amount.

[0156] The second setting unit is used to obtain the optimal dust suppression spraying scheme for heavy-haul railway tunnels based on the optimal dust suppressant spraying amount.

[0157] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0158] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0159] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A dust suppression method based on the prediction of dust transport flow field in heavy-haul railway tunnels, characterized in that, include: Obtain parameter information of heavy-haul railway tunnels, and construct physical models and test parameters based on the parameter information. The parameter information includes tunnel length, actual wind speed, train running speed and dust leakage intensity. Simulation experiments are conducted based on physical models and experimental parameters to obtain test data during the experiment. The test data includes dust trajectory data, dust concentration data, and wind speed data. Based on the test data, a dust transport flow field prediction model for heavy-load railway tunnels was constructed, and the dust concentration field and dust trajectory field of heavy-load railway tunnels were obtained through the dust transport flow field prediction model. The optimal dust suppression spraying scheme for heavy-haul railway tunnels was obtained by optimizing the dust concentration field and dust trajectory field. Dust suppression spraying was carried out on heavy-haul railway tunnels using the optimal dust suppression spraying scheme. The steps for obtaining the optimal dust suppression spraying scheme are as follows: The heavy-haul railway tunnel is discretized into multiple regional units, and a concentration threshold is set. A formula for calculating the spraying amount of the first dust suppressant is constructed based on the concentration threshold. The dust concentration of each area unit is obtained by the dust concentration field, and the first dust suppressant spraying amount of each area unit is calculated based on the dust concentration of each area unit and the first dust suppressant spraying amount calculation formula. Construct a formula for calculating the amount of the second dust suppressant to be sprayed. The dust aggregation intensity of each area unit is obtained based on the dust trajectory field, and the second dust suppressant spraying amount of each area unit is calculated based on the dust aggregation intensity. The optimal dust suppressant spraying amount for each area unit is calculated using the first and second dust suppressant spraying amounts. The optimal dust suppression spraying scheme for heavy-haul railway tunnels is obtained based on the optimal dust suppressant spraying amount.

2. The dust suppression method based on dust transport flow field prediction in heavy-haul railway tunnels according to claim 1, characterized in that... The construction of the physical model and experimental parameters based on parameter information includes: The simulated tunnel length is set according to the tunnel length and geometric similarity ratio, and the simulated tunnel length includes the simulated open section length and the simulated tunnel section length; A physical model is constructed based on the simulated tunnel length; The blower parameters are set according to the actual wind speed and geometric similarity ratio, and the blower parameters include the simulated air outlet speed and the simulated air outlet angle. The simulated train speed is set based on the train running speed and geometric similarity ratio, and the simulated dust spraying amount is set based on the dust leakage intensity and geometric similarity ratio; The blower parameters, simulated train speed, and simulated dust spray volume were used as the test parameters for the physical model.

3. The dust suppression method based on dust transport flow field prediction in heavy-haul railway tunnels according to claim 1, characterized in that... The step of constructing a dust transport flow field prediction model for heavy-haul railway tunnels based on test data includes: Construct a wind speed model, and then construct the motion equations of the dust based on the wind speed model; The dust flux is constructed based on the diffusion coefficient, Dirac function, and dust leakage intensity, and the dust diffusion-convection equation is constructed based on the dust flux. By optimizing the motion equations and diffusion-convection equations of dust using test data, a dust transport flow field prediction model is obtained, which includes the optimal motion equations and the optimal diffusion-convection equations.

4. The dust suppression method based on dust transport flow field prediction in heavy-haul railway tunnels according to claim 3, characterized in that... The process of optimizing the motion equations and diffusion-convection equations of dust using test data to obtain a dust transport flow field prediction model includes: Inverse and forward simulations were performed using dust trajectory data, and the model parameters of the motion equations were obtained by matching the data using the method of minimizing trajectory error. Interpolate the dust concentration data and wind speed data to obtain the continuous field of dust concentration and the continuous field of wind speed. The optimal wind speed model is obtained by optimizing the wind speed model through a continuous wind speed field. The optimal motion equations are obtained by updating the motion equations using the optimal wind speed model and model parameters. The diffusion coefficient and dust leakage intensity are optimized by continuous field analysis of dust concentration, and the diffusion-convection equation is updated using the optimized diffusion coefficient and dust leakage intensity to obtain the optimal diffusion-convection equation.

5. The dust suppression method based on the prediction of dust transport flow field in heavy-haul railway tunnels according to claim 1, characterized in that... The method of applying the optimal dust suppression spraying scheme to heavy-haul railway tunnels includes: A classic working condition library is constructed based on the optimal dust suppression spraying schemes corresponding to various typical working conditions of heavy-haul railway tunnels. The classic working condition library includes parameter information, dust trajectory field, dust concentration field and optimal dust suppression spraying scheme. Obtain target parameter information for the target heavy-load railway tunnel; Calculate the distance between the target heavy-haul railway tunnel and typical working conditions based on target parameter information; The optimal dust suppression spraying scheme for the target heavy-haul railway tunnel is obtained by extrapolating the optimal dust suppression spraying scheme based on distance. Dust suppression spraying was carried out using the optimal dust suppression spraying scheme for the target heavy-haul railway tunnel.

6. The dust suppression method based on the prediction of dust transport flow field in heavy-haul railway tunnels according to claim 5, characterized in that... The extrapolation of the optimal dust suppression spraying scheme based on distance yields the optimal dust suppression spraying scheme for the target heavy-haul railway tunnel, including: Set a distance threshold; The typical working condition with the shortest distance is selected as the target heavy-haul railway tunnel similar working condition. If the distance threshold is less than that of similar working conditions, the optimal dust suppression spraying scheme for the target heavy-load railway tunnel is obtained based on the similarity criterion. Otherwise, the optimal dust suppression spraying scheme for the target heavy-load railway tunnel is obtained by weighted interpolation of multiple adjacent classic working conditions.

7. A dust suppression system based on the prediction of dust transport flow field in heavy-haul railway tunnels, characterized in that, include: The acquisition module is used to acquire parameter information of heavy-haul railway tunnels, and construct physical models and test parameters based on the parameter information. The parameter information includes tunnel length, actual wind speed, train running speed and dust leakage intensity. The test module is used to conduct simulation tests based on physical models and test parameters, and to obtain test data during the test process. The test data includes dust trajectory data, dust concentration data, and wind speed data. The construction and prediction module is used to construct a dust transport flow field prediction model for heavy-load railway tunnels based on test data, and obtain the dust concentration field and dust trajectory field of heavy-load railway tunnels through the dust transport flow field prediction model. The optimization module is used to optimize the dust suppression spraying scheme based on the dust concentration field and dust trajectory field, so as to obtain the optimal dust suppression spraying scheme for heavy-haul railway tunnels. The dust suppression module is used to suppress dust in heavy-haul railway tunnels using the optimal dust suppression spraying scheme. The optimization module includes: The first setting unit is used to discretize the heavy-haul railway tunnel into multiple regional units and set a concentration threshold. The third building unit is used to construct a calculation formula for the first dust suppressant spraying amount based on the concentration threshold. The first calculation unit is used to obtain the dust concentration of each area unit through the dust concentration field, and to calculate the first dust suppressant spraying amount of each area unit based on the dust concentration of each area unit and the first dust suppressant spraying amount calculation formula. The fourth building unit is used to construct the calculation formula for the second dust suppressant spraying amount; The second calculation unit is used to obtain the dust aggregation intensity of each area unit based on the dust trajectory field, and to calculate the second dust suppressant spraying amount of each area unit based on the dust aggregation intensity. The third calculation unit is used to calculate the optimal dust suppressant spraying amount for each area unit based on the first dust suppressant spraying amount and the second dust suppressant spraying amount. The second setting unit is used to obtain the optimal dust suppression spraying scheme for heavy-haul railway tunnels based on the optimal dust suppressant spraying amount.

8. The dust suppression system based on dust transport flow field prediction in heavy-haul railway tunnels according to claim 7, characterized in that, The construction and prediction module includes: The first building unit is used to build a wind speed model, and the motion equation of dust is built based on the wind speed model. The second building block is used to construct the dust flux based on the diffusion coefficient, Dirac function and dust leakage intensity, and to construct the dust diffusion-convection equation based on the dust flux. An optimization unit is used to optimize the motion equation and diffusion-convection equation of dust using test data to obtain a dust transport flow field prediction model, which includes the optimal motion equation and the optimal diffusion-convection equation.

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