A CFD simulation-based method for smoke screen deployment decision-making
By simulating smoke diffusion and wind flow using CFD simulation technology, the adaptability of traditional smoke deployment decision-making methods under complex weather conditions was solved, and accurate prediction and efficient calculation of smoke shielding effects were achieved.
Patent Information
- Application Number
- CN202111641277.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-12-29
- Publication Date
- 2025-10-28
- Estimated Expiration
- 2041-12-29
AI Technical Summary
Traditional smoke screen deployment decision-making methods are based on Gaussian diffusion models, which have poor adaptability and cannot effectively predict and formulate deployment plans for smoke screen deployment devices, especially under complex weather conditions.
A CFD simulation-based smoke screen deployment decision-making method is adopted. Through smoke screen simulation and deployment scheme selection, the diffusion process of smoke screen in the air is simulated by CFD simulation technology. Combined with wind flow and the diffusion movement of smoke particles, the smoke screen coverage area is calculated and the optimal deployment scheme is selected.
It improves the adaptability of smoke prediction and the accuracy of deployment schemes of smoke screen deployment devices, enables rapid and efficient calculation of smoke screen shielding effects, simplifies the calculation process, and is easy to implement on low- to medium-configuration computers.
Smart Images
Figure CN114462327B_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of fluid simulation technology, specifically relating to a smoke screen deployment decision-making method based on CFD simulation. Background Art
[0002] Electro-optical reconnaissance makes the battlefield transparent, and electro-optical guided weapons can strike selected targets with a high hit rate. Smoke screens are an important means of countering electro-optical reconnaissance and attacks in high-tech battlefield environments. Smoke screens can attenuate the visible light and infrared radiation of targets through the absorption and scattering effects of aerosol systems, effectively reducing the probability of target detection. They can also create false targets by using their own infrared radiation, interfering with the working circuits of enemy electro-optical guided weapons and preventing them from locking onto and tracking real targets.
[0003] Smoke screen deployment devices are general-purpose smoke-generating equipment derived from aircraft engines, which spray various types of military smoke agents at high temperatures. Meteorological conditions, especially wind speed and direction, and atmospheric vertical stability, have a significant impact on the smoke screen's concealment effect. Developing a deployment plan that optimizes smoke screen concealment for different weather conditions is crucial to maximizing the effectiveness of the smoke screen.
[0004] Traditional smoke screen deployment decision-making methods are based on atmospheric diffusion estimation. These methods use Gaussian diffusion models to predict the spatiotemporal distribution of smoke concentration and then formulate deployment plans based on the predictions. The Gaussian diffusion model approximates the smoke concentration as a normal distribution, with the standard deviation calculated using an empirical function. It studies the diffusion process in a fixed space and is suitable for conditions such as open, flat, and homogeneous ground, stable and passive diffusing substances, total reflection from the ground, a flat and stable mean flow field, and no significant temporal variation in mean wind speed and direction. Therefore, the Gaussian diffusion model is less suitable for high-temperature jet smoke from smoke screen deployment devices. Summary of the Invention
[0005] Given that the Gaussian diffusion model has poor performance in predicting smoke from smoke deployment devices, a CFD simulation-based smoke deployment decision-making method is proposed to address the problems of smoke prediction and deployment scheme decision-making for smoke deployment devices.
[0006] This CFD simulation-based smoke screen deployment decision-making method includes smoke screen simulation and deployment scheme selection. Smoke screen simulation is used to obtain the smoke concentration function c = C(x,y,z,α) for a single smoke screen deployment device, where c is the smoke mass concentration in kg / m³. 3x, y, and z are the spatial coordinates along the wind direction, perpendicular to the ground, and across the wind direction, respectively, in meters; α is the injection angle, which is the angle between the nozzle of the smoke screen deployment device and the wind direction, in degrees. The specific steps include: geometric model construction, mesh generation, calculation parameter setting, wind field model solution, discrete phase setting, discrete phase solution, simulation result correction, and result export.
[0007] The steps for screening deployment schemes include: combining the three-dimensional smoke concentration obtained from smoke simulation into a large number of alternative deployment schemes by copying, translating, and changing the spray angle; calculating the superimposed three-dimensional smoke concentration generated by multiple smoke release devices in each alternative deployment scheme; calculating the smoke coverage area of each alternative deployment scheme based on the Lambert-Beer law; and selecting the alternative deployment scheme with the largest smoke coverage area as the optimal deployment scheme.
[0008] Furthermore, the specific steps for smoke screen simulation are as follows:
[0009] Step 1: Establish a geometric model: The diffusion of smoke in space is a three-dimensional process. Based on the measured length, width and height of the smoke, the dimensions of the three-dimensional geometric model are set to x×y×z, with the following range: 0≤x≤160, 0≤y≤30, -100≤z≤100. The smoke release device is located at (10,1.5,0).
[0010] Step 2, Mesh Generation: The geometric region is meshed using ICEM software with a mesh size of 2m and a total of 173,712 nodes.
[0011] Step 3, Calculation parameter settings: Transient simulation is used with a time step of 0.5s and logarithmic wind speed profile is used to simulate the natural wind field;
[0012] Step 4: Solving the wind field model: The governing equations are solved using Fluent, and discretized using the finite volume method. The momentum equation, energy equation, turbulent kinetic energy equation, and turbulent dissipation rate equation are discretized using a second-order upwind scheme. Pressure is discretized using the body-force-weighted method. The SIMPLEC algorithm is selected to couple pressure and velocity. The convergence criterion for the energy equation is set to 10. -6 The convergence criterion for other equations is set to 10. -3 ;
[0013] Step 5, Discrete Phase Setup: Air-smoke particle bidirectional coupling is adopted, with a coupling step size of 10; the smoke particle injection type is point source; the initial particle velocity is 110 m / s; the particle diameter is 10 μm; and the smoke agent flow rate is 0.125 kg / s.
[0014] Step 6, Setting the spray angle α: Considering the actual use of the smoke screen deployment device, the spray angle α is set to a fixed value α between 0 and 90°. i ;
[0015] Step 7, Discrete Phase Solution: Add the discrete phase to the wind field model and continue to use Fluent to solve until the convergence requirement is met. Calculate the concentration of the discrete phase (smoke particles) at the current time.
[0016] Step 8: Simulation result correction: Based on the measured data, correct the simulation results. When the length is less than 90m, the correction factor p for the smoke screen simulation height is 0.8; when the length is greater than 90m, p = 1.2.
[0017] Step 9: Simulation Result Storage: Record the smoke concentration function c of a single smoke screen deployment device with a fixed injection angle. i =C(x,y,z,α=α) i ):
[0018] Step 10: Repeat steps 6 through 9 until a. i It can cover a good range of 0 to 90 degrees.
[0019] Furthermore, the specific steps in the deployment scheme selection process are as follows:
[0020] Step 1: Establish a mathematical model: The superimposed smoke concentration c generated by multiple smoke screen deployment devices 1...M :
[0021] c 1...M =c1+c2+...+c M
[0022] Where: c1, c2, ..., c M The smoke concentrations generated by deployment devices 1 through M are given by the following formulas:
[0023] c1 = C(x + Δx1, y, z + Δz1, α1)
[0024] c2=C(x+Δx2,y,z+Δz2,α2) ...
[0026] ...
[0028] c M-1 =C(x+Δx) M-1 ,y,z+Δz M-1 ,α M-1 )
[0029] c M =C(x+Δx) M,y,z+Δz M ,α M )
[0030] In the formula: Δx and Δz represent the movement of the positions of launching devices 1 to M, α1 to α M Indicates the spray angle of each dispensing device;
[0031] Step 2, Model Simplification: Considering the left-right sway of the wind direction, the M-stage deployment devices are symmetrically arranged on both sides in the downwind direction (x direction), and the spray angle is mirror-symmetrical. Therefore, we have:
[0032] c1 = C(x + Δx1, y, z + Δz1, α1)
[0033] c2=C(x+Δx2,y,z+Δz2,α2) ...
[0035] ...
[0037] c M-1 =C(x+Δx²,y,z-Δz²,α²)
[0038] c M =C(x+Δx1,y,z-Δz1,α1)
[0039] Superimposed smoke concentration c 1...M The function representing the spatial coordinates (x, y, z) and the positions of each smoke screen deployment device, as well as the spray angle, can be expressed as:
[0040]
[0041] Step 3: Discretization of the superimposed smoke concentration function: Within a 1m×1m×1m cube, select a point with integer spatial coordinates. Use the smoke concentration at that point to approximate the smoke concentration within the cube. The discretized superimposed smoke concentration function can be expressed as:
[0042]
[0043] In the formula: x′, y′, and z′ are all integers, 0m≤x′≤160m, 0m≤y′≤30m, and -100m≤z′≤100m;
[0044] Step 4: Calculation of the shading rate function: Based on Beer-Lambert's law, the discretized superimposed smoke density function is weighted and accumulated along the y-axis to obtain the shading rate function τ in the top-down direction:
[0045]
[0046] Step 5: Calculate the shielding area: Calculate the shielding rate of each point within the range of 0m≤x′≤160m and -100m≤z′≤100m, and count the number of points with a shielding rate ≥90%, which will be the smoke screen shielding area A.
[0047]
[0048] Step 6: Change the values of Δx, Δz, and α, and repeat steps 3 through 5 until the maximum value A is obtained. max ;
[0049] Step 7: Output the values of Δx, Δz, and α at this time.
[0050] Furthermore, the superimposed smoke concentration function of N smoke screen deployment devices is obtained by four methods: copying, translating, accumulating, and superimposing the smoke concentration function.
[0051] Furthermore, the number of smoke-emitting devices, N ≤ 5.
[0052] Furthermore, the smoke screen coverage area includes the visible light smoke screen coverage area and the infrared smoke screen coverage area. Attached Figure Description
[0053] Figure 1 Flowchart of CFD-based smoke screen simulation
[0054] Figure 2 Simulation results of CFD-based smoke screen simulation flowchart Detailed Implementation
[0055] The embodiments of the method of the present invention will be described in detail below with reference to the accompanying drawings.
[0056] CFD-based smoke screen simulation is fundamental to deployment scheme selection. Through smoke screen simulation, the three-dimensional smoke concentration of a smoke screen deployment device at different spray angles can be obtained. Smoke diffusion in the air is a two-way coupling process between solid smoke particles and gaseous wind fields, belonging to a typical discrete phase diffusion model. CFD-based smoke screen simulation technology must consider both wind flow and the diffusion motion of smoke particles during modeling.
[0057] The spatial distribution of fluid motion at a given moment is called the flow field. A wind field is a special type of flow field, defined as the spatiotemporal distribution of wind speed within a certain region. The transport and diffusion caused by the wind field are the main driving forces for the formation of an effective shielding area for a smoke screen. During its ascent, the smoke screen is affected by the oncoming air (including air resistance and energy exchange) and the diffusion effect of the updraft itself. Let the components of the flow field velocity u along the three coordinate axes be u0. x u y u zThe temperature field parameter is T, and the flow field pressure is p. The smoke diffusion process follows the laws of conservation of mass, energy, and momentum. Furthermore, the smoke flow is turbulent, and the Reynolds time-averaged equation is commonly used in engineering to describe this turbulent state. The basic idea is to represent the instantaneous fluctuations of the fluid in the time-averaged equation using a k-ε (k is the turbulent kinetic energy, ε is the dissipation rate) two-equation model. The relevant mathematical models are as follows:
[0058] mass conservation equation:
[0059]
[0060] In the formula: ρ is the air density (kg / m³) 3 );x i For the tensor representation of the turbulence model, i = x, y, z; u i (i = x, y, z) represents the components of the flow field velocity along the x, y, and z axes; source term S m The mass (kg) of the continuously flowing gas.
[0061] Energy conservation equation:
[0062]
[0063] In the formula: c p λ is the specific heat capacity of air at constant pressure (J / (kg·℃)); λ is the thermal conductivity of the gas (W / (m·℃)); source term S T The heat (J) of the continuously flowing gas.
[0064] Momentum conservation equation:
[0065]
[0066] In the formula: u j Let j represent the components of the flow field velocity in the x, y, and z axes, where j = x, y, z; x j Here, μ is the tensor representation of the turbulence model; μ is the laminar viscosity coefficient (Pa·s); μ t μ is the turbulent viscosity coefficient (Pa·s). t =ρC μ k 2 / ε,C μ =0.09.
[0067] k-equation:
[0068]
[0069] In the formula: σ k The value is 1.0; G k Turbulent kinetic energy derived from the average velocity gradient (kg / (s))3 ·m)),
[0070] ε equation:
[0071]
[0072] In the formula: C 1ε =1.44; C 2ε =1.9; σ ε =1.3.
[0073] The discrete-phase model is a two-phase flow numerical model following the Euler-Lagrange method. This model treats the fluid as a continuous phase and solves for the particle trajectory by integrating the differential equations of particle forces in Lagrange coordinates. In Cartesian coordinates, the force balance equations and trajectory equations (x-axis direction) of the smoke particles are described as follows:
[0074]
[0075]
[0076] In the formula: u1, u p These represent the fluid phase velocity and particle velocity (m / s) along the x-axis, respectively; F D (u1-u p ) represents the drag force per unit mass of the particle; ρ p Particle bulk density (kg / m³) 3 );g x (ρ p -ρ) / ρ p F is the difference between the weight and buoyancy per unit mass of the particle; x Other forces per unit mass (N).
[0077] A CFD simulation-based smoke screen deployment decision-making method can be divided into two parts: smoke screen simulation of smoke screen deployment device and deployment scheme selection.
[0078] The purpose of smoke screen simulation is to obtain the smoke concentration function c = C(x,y,z,α) of a single smoke screen deployment device, where c is the smoke mass concentration (kg / m³). 3 x, y, and z are the spatial coordinates along the wind direction, perpendicular to the ground, and across the wind direction, respectively, in meters (m); α is the spray angle, which is the angle between the nozzle of the smoke screen deployment device and the wind direction.
[0079] The specific steps of smoke screen simulation include:
[0080] Step 1: Establish the geometric model. The diffusion of smoke in space is a three-dimensional process, which generally occurs within the atmospheric boundary layer (0–200m above the ground). Based on the measured length, width, and height of the smoke screen, the dimensions of the three-dimensional geometric model are set to 160m × 200m × 30m. Value range: 0 ≤ x ≤ 160, 0 ≤ y ≤ 30, -100 ≤ z ≤ 100. The smoke screen deployment device is located at (10, 1.5, 0).
[0081] Step 2: Mesh Generation. To perform smoke screen simulation using CFD, the geometric model must first be meshed. ICEM software was used to generate a structured mesh for the geometric region, with each mesh size being 2m and a total of 173,712 nodes.
[0082] Step 3: Calculation parameter settings. Transient simulation is used with a time step of 0.5s. Logarithmic wind speed profiles are used to simulate natural wind fields.
[0083] Step 4: Solving the wind field model. Fluent is used to solve the governing equations, which are then discretized using the finite volume method. The momentum, energy, turbulent kinetic energy, and turbulent dissipation rate equations are discretized using a second-order upwind scheme; pressure is discretized using the body-force-weighted method; the SIMPLEC algorithm is selected to couple pressure and velocity; to ensure the convergence of the calculation results, the convergence criterion for the energy equation is set to 10. -6 (Standardized residuals), the convergence criterion for other equations is set to 10. -3 (Standardized residuals).
[0084] Step 5, Discrete Phase Setup. Air-smoke particle bidirectional coupling is used with a coupling step size of 10; the smoke particle injection type is point source; the initial particle velocity is 110 m / s; the particle diameter is 10 μm; and the smoke agent flow rate is 0.125 kg / s.
[0085] Step 6: Setting the spray angle α. Considering the actual use of the smoke screen deployment device, the spray angle α is set to a fixed value α between 0° and 90°. i .
[0086] Step 7: Discrete Phase Solution. Add the discrete phase to the wind field model and continue solving using Fluent until convergence is met. Calculate the current concentration of the discrete phase (smoke particles).
[0087] Step 8: Simulation Result Correction. Based on the measured data, correct the simulation results. When the length is less than 90m, the correction factor p for the smoke screen simulation height is 0.8; when the length is greater than 90m, p = 1.2.
[0088] Step 9: Simulation Result Storage. Record the smoke concentration function c of a single smoke screen deployment device with a fixed injection angle. i =C(x,y,z,α=α) i ).
[0089] Step 10: Repeat steps 6 through 9 until a. i It can effectively cover the 0–90° range. The smoke concentration function of a single smoke screen deployment device is approximately:
[0090]
[0091] Where: α1, α2, ..., α N The angles are discrete within the range of 0 to 90°. This completes the solution for the smoke concentration function c = C(x,y,z,α) of a single smoke screen deployment device.
[0092] The deployment scheme selection is based on the conclusions of smoke screen simulation. M (assuming M is an odd number) smoke screen deployment devices will simultaneously generate smoke. The deployment scheme selection process is as follows:
[0093] Step 1: Establish a mathematical model. The cumulative smoke concentration c produced by multiple smoke-emitting devices. 1...M There is a formula:
[0094] c 1...M =c1+c2+...+c M
[0095] Where: c1, c2, ..., c M The smoke concentrations generated by deployment devices 1 through M are given by the following formulas:
[0096] c1 = C(x + Δx1, y, z + Δz1, α1)
[0097] c2=C(x+Δx2,y,z+Δz2,α2) ...
[0099] ...
[0101] c M-1 =C(x+Δx) M-1 ,y,z+Δz M-1 ,α M-1 )
[0102] c M =C(x+Δx) M ,y,z+Δz M ,α M )
[0103] In the formula: Δx and Δz represent the movement of the positions of launching devices 1 to M, α1 to α M This indicates the spray angle of each dispensing device.
[0104] Step 2, Model Simplification. Considering the left-right sway of the wind direction, the M-stage deployment devices are symmetrically arranged on both sides in the downwind direction (x direction), with mirror-symmetrical spray angles. Therefore:
[0105] c1 = C(x + Δx1, y, z + Δz1, α1)
[0106] c2=C(x+Δx2,y,z+Δz2,α2) ...
[0108] ...
[0110] c M-1 =C(x+Δx²,y,z-Δz²,α²)
[0111] c M =C(x+Δx1,y,z-Δz1,α1)
[0112] Superimposed smoke concentration c 1...M The function representing the spatial coordinates (x, y, z) and the positions of each smoke screen deployment device, as well as the spray angle, can be expressed as:
[0113]
[0114] Step 3: Discretization of the superimposed smoke concentration function. Within a 1m × 1m × 1m cube, select a point with integer spatial coordinates. Use the smoke concentration at that point to approximate the smoke concentration within the cube. The discretized superimposed smoke concentration function can be expressed as:
[0115]
[0116] In the formula: x′, y′, and z′ are all integers, 0m≤x′≤160m, 0m≤y′≤30m, and -100m≤z′≤100m.
[0117] Step 4: Calculation of the shading rate function. Based on Beer-Lambert's law, the discretized superimposed smoke density function is weighted and accumulated along the y-axis to obtain the shading rate function τ in the top-down direction:
[0118]
[0119] Step 5: Calculate the shielding area. Calculate the shielding rate of each point within the range of 0m≤x′≤160m and -100m≤z′≤100m. Count the number of points with a shielding rate ≥90%, and use this as the smoke screen shielding area A.
[0120]
[0121] Step 6: Change the values of Δx, Δz, and α, and repeat steps 3 through 5 until the maximum value A is obtained. max .
[0122] Step 7: Output the values of Δx, Δz, and α at this time.
[0123] This completed the formulation of the smoke-generating device layout method.
[0124] The CFD simulation-based smoke screen deployment decision method is well adapted to smoke screen deployment devices and can quickly and efficiently calculate the smoke screen shielding effect and evaluate the smoke screen deployment scheme. The calculation and prediction process is simple and easier to implement on low- to medium-configuration computers.
Claims
1. A smoke screen deployment decision-making method based on CFD simulation, comprising smoke screen simulation and deployment scheme selection; wherein, Smoke simulation is used to obtain the smoke concentration function of a single smoke screen deployment device: c = C(x,y,z,α), where c is the smoke mass concentration in kg / m³. 3 x, y, and z are the spatial coordinates along the wind direction, perpendicular to the ground, and across the wind direction, respectively, in meters. α is the spray angle, which is the angle between the nozzle of the smoke screen deployment device and the wind direction, in degrees. Smoke screen simulation includes the following steps: geometric model construction, mesh generation, calculation parameter setting, wind field model solution, discrete phase setting, discrete phase solution, simulation result correction, and result export. The steps for screening deployment schemes include: combining the three-dimensional smoke concentration obtained from smoke simulation into a large number of alternative deployment schemes; calculating the superimposed three-dimensional smoke concentration generated by multiple smoke release devices in each alternative deployment scheme; calculating the smoke coverage area of each alternative deployment scheme; and selecting the alternative deployment scheme with the largest smoke coverage area as the optimal deployment scheme. The specific steps for smoke screen simulation are as follows: Step 1: Establish a geometric model: The diffusion of smoke in space is a three-dimensional process. Based on the measured length, width and height of the smoke, the dimensions of the three-dimensional geometric model are set to x×y×z, with the following range: 0≤x≤160, 0≤y≤30, -100≤z≤100. The smoke release device is located at (10,1.5,0). Step 2, Mesh Generation: The geometric region is meshed using ICEM software with a mesh size of 2m and a total of 173,712 nodes. Step 3, Calculation parameter settings: Transient simulation is used with a time step of 0.5s and logarithmic wind speed profile is used to simulate the natural wind field; Step 4: Solving the wind field model: The governing equations are solved using Fluent, and discretized using the finite volume method. The momentum equation, energy equation, turbulent kinetic energy equation, and turbulent dissipation rate equation are discretized using a second-order upwind scheme. Pressure is discretized using the body-force-weighted method. The SIMPLEC algorithm is selected to couple pressure and velocity. The convergence criterion for the energy equation is set to 10. -6 The convergence criterion for other equations is set to 10. -3 ; Step 5, Discrete Phase Setup: Air-smoke particle bidirectional coupling is adopted, with a coupling step size of 10; the smoke particle injection type is point source; the initial particle velocity is 110 m / s; the particle diameter is 10 μm; and the smoke agent flow rate is 0.125 kg / s. Step 6, Setting the spray angle α: Considering the actual use of the smoke screen deployment device, the spray angle α is set to a fixed value α between 0 and 90°. i ; Step 7, Discrete Phase Solution: Add the discrete phase to the wind field model and continue to use Fluent to solve until the convergence requirement is met. Calculate the discrete phase concentration at the current time. Step 8: Simulation result correction: Based on the measured data, correct the simulation results. When the length is less than 90m, the correction factor p for the smoke screen simulation height is 0.8; when the length is greater than 90m, p = 1.
2. Step 9: Simulation Result Storage: Record the smoke concentration function c of a single smoke screen deployment device with a fixed injection angle. i =C(x,y,z,α=α) i ): Step 10: Repeat steps 6 through 9 until a. i It can cover a range of 0 to 90 degrees.
2. The smoke screen deployment decision-making method according to claim 1, characterized in that: The specific steps in the deployment scheme selection process are as follows: Step 1: Establish a mathematical model: The superimposed smoke concentration c generated by multiple smoke screen deployment devices 1...M : c 1...M =c1+c2+...+c M Where: c1, c2, ..., c M The smoke concentrations generated by deployment devices 1 through M are given by the following formulas: c1 = C(x + Δx1, y, z + Δz1, α1) c2=C(x+Δx2,y,z+Δz2,α2) ... ... c M-1 =C(x+Δx M-1 ,y,z+Δz M-1 ,α M-1 ) c M =C(x+Δx M ,y,z+Δz M ,α M ) In the formula: Δx i and Δz i This represents the movement of the i-th launching device in the x and z directions, i = 1 to M, α1 to α2. M Indicates the spray angle of each dispensing device; Step 2, Model Simplification: Considering the left-right sway of the wind direction, the M-stage deployment devices are symmetrically arranged on both sides of the downwind direction, with mirror-symmetrical spray angles. Therefore, we have: c1 = C(x + Δx1, y, z + Δz1, α1) c2=C(x+Δx2,y,z+Δz2,α2) ... ... c M-1 =C(x+Δx2,y,z-Δz2,α2) c M =C(x+Δx1,y,z-Δz1,α1) Superimposed smoke concentration c 1...M The function representing the spatial coordinates (x, y, z) and the positions of each smoke screen deployment device, as well as the spray angle, can be expressed as: Step 3: Discretization of the superimposed smoke concentration function: Within a 1m×1m×1m cube, select a point with integer spatial coordinates. Use the smoke concentration at that point to approximate the smoke concentration within the cube. The discretized superimposed smoke concentration function can be expressed as: In the formula: x′, y′, and z′ are all integers, 0m≤x′≤160m, 0m≤y′≤30m, and -100m≤z′≤100m; Step 4: Calculation of the shading rate function: Based on Beer-Lambert's law, the discretized superimposed smoke density function is weighted and accumulated along the y-axis to obtain the shading rate function τ in the top-down direction: Step 5: Calculate the shielding area: Calculate the shielding rate of each point within the range of 0m≤x′≤160m and -100m≤z′≤100m, and count the number of points with a shielding rate ≥90%, which will be the smoke screen shielding area A. Step 6: Change the values of Δx, Δz, and α, and repeat steps 3 through 5 until the maximum value A is obtained. max ; Step 7: Output the values of Δx, Δz, and α at this time.
3. The smoke screen deployment decision method according to claim 1, characterized in that: The superimposed smoke concentration function of N smoke screen deployment devices is obtained by four methods: copying, translating, accumulating, and superimposing the smoke concentration function.
4. The smoke screen deployment decision-making method according to claim 3, characterized in that: The number of smoke screen deployment devices, N≤5.
5. The smoke screen deployment decision-making method according to claim 1, characterized in that: The smoke screen coverage area for each alternative deployment scheme is calculated based on the Lamb-Beer law.
6. The smoke screen deployment decision method according to claim 1, characterized in that: The smoke screen coverage area includes the visible light smoke screen coverage area and the infrared smoke screen coverage area.