Prediction method for paint mist particle recovery trajectory of ship paint spraying robot

Abstract

The invention discloses a prediction method for a paint mist particle recovery track of a ship paint spraying robot, which comprises a spray nozzle paint mist spraying model and a paint mist recovery model, mainly calculates the motion track of particles rebounded and splashed back from a spraying surface according to a set spray nozzle numerical value, a set negative pressure numerical value and the like, and obtains the recovery rate of the particles under the negative pressure. Therefore, proper recovery negative pressure is judged.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of particle dynamics in gas-solid two-phase flow, and particularly relates to a method for predicting the trajectory of paint mist particle recovery of a ship paint spraying robot. BACKGROUND

[0002] In the field of modern industrial manufacturing, painting process plays a key role in improving the appearance quality of products, providing corrosion protection, and enhancing decorative effects. In particular, in the industries of shipbuilding, automobile manufacturing, and mechanical processing, paint spraying operation is an indispensable part of the production process. During paint spraying, paint is sprayed onto the workpiece surface through a high-pressure spray gun. Due to the fine size and high-speed spraying of paint particles, a certain proportion of fine mist particles, i.e. paint mist, is inevitably generated. These paint mists contain a large amount of volatile organic compounds (VOCs) and tiny solid particles, which pollute the environment and pose potential threats to human health. VOCs are one of the important components of atmospheric pollution, while tiny solid particles can enter the human body through the respiratory tract and induce various health problems.

[0003] In the process of painting the outer surface of a ship and recovering paint mist, determining the optimal negative pressure value to achieve efficient paint mist capture is a challenge. During the spraying process, paint is atomized from the nozzle and sprayed at high speed onto the target surface. Part of the paint particles rebound and splash after hitting the surface, and enter the airflow under the action of negative pressure with a certain initial velocity. These particles undergo complex motion under the combined action of various forces, and their trajectories are difficult to predict, making it extremely difficult to calculate the recovery efficiency of paint mist particles. Existing motion models often cannot provide accurate calculation results when dealing with such complex particle dynamic behavior, thereby limiting the optimization and efficiency improvement of paint mist recovery systems. SUMMARY

[0004] The purpose of the present application is to provide a method for predicting the trajectory of paint mist particle recovery of a ship paint spraying robot to solve the problems existing in the prior art.

[0005] The above technical purpose of the present application is achieved by the following technical solution: A method for predicting the trajectory of paint mist particle recovery of a ship paint spraying robot, comprising the following steps: Step one, obtain part of the known parameters, establish a nozzle spraying motion model, and the spraying model is based on the Euler-Lagrange method; Step two, the impact energy of the paint mist particles on the wall surface can be obtained from known parameters. When the sprayed paint mist particles meet the conditions, they will adhere to the wall surface or bounce. For the new particles that bounce and splash, the Weibull distribution law is satisfied, and the diameter, speed, direction, etc. of the particles will change. For the diameter of the particles, it can be obtained through the cumulative distribution function. According to the conservation of mass, etc., the number and proportion of the bounced and splashed particles can be obtained. The number of splashed paint mist particles in each paint mist particle package is equal to the total number of paint mist particles multiplied by the probability distribution function corresponding to the diameter of the paint mist particles. After obtaining the number of splashed particles, the splashing speed of the particles can be determined. While meeting the Mundo experimental data, the Weibull distribution function can be used as the probability distribution function of the normal speed of the splashed particles. After listing the energy balance equation of the paint mist particles and making corrections, the normal and tangential velocities of the paint mist particles can be obtained. Step three, for the recovery of the bounced paint mist particles, the model uses the Euler-Lagrange method to describe the motion of the gas phase and the paint mist particles using the continuous phase and the particle phase respectively. The Euler model is established for the gas density and gas velocity field of the gas continuous phase. For the gas continuous phase, the continuity equation, Navier-Stokes equation, and energy equation are used. For the paint mist particle phase, the particle motion equation is used to establish the model considering the thermal effect. Step four, for the motion simulation of the paint mist particles, the RK4 method is used to calculate the particle trajectory based on the force and initial velocity of the particles. Specifically, at time step n, the total force of the particles is calculated, and the velocity and position of the particles are further updated. The motion of the particles is simulated to predict the landing point of the paint mist particles. The number of particles extracted on the recovery boundary is detected. When the number of boundary particles meets the requirements, it is considered that the negative pressure at this time meets the recovery requirements. If the number of particles is insufficient, the negative pressure is modified, and the model is re-run from step four until the recovery particle ratio meets the expectation. The recovery negative pressure of the particles can be obtained.

[0006] In further embodiments, the nozzles and the recovery hood selected in steps one and two are different, and the known parameters in step two include but are not limited to paint density, wall height, nozzle structure diameter, recovery hood size, vacuum pump pumping speed, and other known parameters.

[0007] In further embodiments, step two includes: The impact energy is defined as: ; wherein, is the liquid density, is the velocity of the paint mist particles relative to the spraying surface, is the diameter of the paint mist particles, is the liquid surface tension, is the height of the liquid film on the spraying surface, is the height of the boundary layer of the wall surface. If the impact energy of the paint mist particles is lower than the critical impact energy, and the temperature of the spraying surface is higher than the boiling point temperature of the paint mist particles, the paint mist particles will rebound from the wall surface, and the rebound coefficient of the paint mist particles is: ; wherein, is the collision angle of the paint mist particles; The Weibull distribution function in step two is: ; wherein, d is the particle diameter, and D is the characteristic diameter; The diameter distribution of the splashed paint mist particles is: ; wherein, is the Weber number, is the particle diameter corresponding to the peak of the distribution function, is the critical impact energy, represents the particle diameter corresponding to the minimum peak in the diameter distribution function of the splashed particles.

[0008] In further embodiments, the step three comprises: for the gas phase, the continuity equation is: ; wherein, is the partial derivative symbol, is the mass density of the gas, is the time, is the divergence operator, is the velocity vector.

[0009] The Navier-Stokes equation is: ; wherein, is the heat exchange between the particles and the gas.

[0010] The energy equation considering the thermal effect is: ; wherein, is the specific heat capacity of the gas, and T is the temperature, is the thermal conductivity of the gas For the particle phase, the particle motion equation can be obtained from Newton's second law: ; wherein, is the particle mass, is the particle velocity, is the resistance, is the gravity, is the mass force, For the Brown force, the resistance expression is: ; wherein, is the resistance coefficient, is the projection area of the particle, is the relative velocity between the gas and the particle.

[0011] Considering the thermal effect, the temperature change of the paint mist particle is: ; wherein, is the particle temperature, is the heat exchange between the particle and the gas.

[0012] In a further embodiment, the step five comprises: The combined velocity of the normal velocity and the tangential velocity after the rebound of the splash is taken as the initial velocity, the RK4 method is used to calculate the particle trajectory, specifically, at the time step n, the combined force of the particle is calculated, and the velocity and position of the particle are further updated, that is, the motion trajectory of the particle can be simulated and calculated, and the landing position of the particle can be obtained.

[0013] The particle velocity is calculated: ; The particle position is calculated: ; wherein, is the velocity of the particle at the n step, is the position of the particle at the n step, is an intermediate calculation variable.

[0014] The proportion of the particle number of the recovery boundary in the total particle number, that is, the paint mist particle recovery rate, is calculated, and when the recovery rate is higher than the expected value, it is considered that the paint mist particles are successfully recovered under the negative pressure.

[0015] In summary, the present application has the following beneficial effects: 1. This prediction method combines multi-physics coupled particle motion equations with a dynamic negative pressure optimization mechanism to achieve efficient and precise control of the paint mist recovery system. In particle trajectory modeling, the model comprehensively considers multiple forces such as drag, gravity, Brownian force, negative pressure, and heat exchange effects. The RK4 (fourth-order Runge-Kutta) method is used to perform high-precision numerical solutions for particle trajectories, ensuring the reliability of motion prediction under complex force fields. Simultaneously, based on a real-time feedback mechanism for the recovery rate, the negative pressure efficiency is dynamically evaluated by calculating the proportion of captured particles at the boundary. If the expected efficiency is not met, the negative pressure parameters are automatically iteratively adjusted and the simulation is re-enhanced until the recovery rate requirement is met. This design deeply integrates high-precision trajectory simulation with parameter self-optimization, improving the accuracy of paint mist particle recovery trajectory prediction and achieving intelligent dynamic adaptation of negative pressure parameters, providing a closed-loop solution for efficiency optimization of the paint mist recovery system for marine painting robots. Attached Figure Description

[0016] Figure 1 This is an overall flowchart of the present invention. Detailed Implementation

[0017] The present invention will be further described in detail below with reference to the accompanying drawings.

[0018] Identical parts are indicated by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "upper," and "lower" used in the following description refer to the attached figures. Figure 1 In this specification, the terms "bottom surface" and "top surface," "inner" and "outer" refer to the direction toward or away from the geometry of a specific component. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this specification, "a plurality of" means two or more, unless otherwise explicitly and specifically defined by the direction of the center.

[0019] Example 1: like Figure 1 As shown, this invention provides a method for predicting the trajectory of paint mist particles collected by a ship painting robot, specifically including the following steps: Step 1: Obtain the basic information required for the model, including nozzle paint spraying speed, paint density, paint mist particle diameter, recovery hood diameter, air inlet height, etc.

[0020] Step 2: Calculate the impact energy E of the paint mist particles. Further, the impact energy is: ; Furthermore, in practical applications, among which, - liquid density, kg / m3 - velocity of the paint mist particles relative to the sprayed surface, i.e. where and represent the velocities of the paint mist particles and the sprayed surface, respectively, in m / s; - diameter of the paint mist particles, m - liquid surface tension, N / m; in practical applications, 0.036 N / m can be taken - height of the liquid film on the wall, m - height of the wall boundary layer, defined as: ; Further, the Reynolds number is: ; To determine whether the paint mist particles can rebound and splash, the condition for the rebound of the paint mist particles is that the wall temperature is higher than the boiling point of the liquid, and the impact energy of the paint mist particles is compared with the critical impact energy, which is defined as , and the particles that meet the above two conditions will rebound and splash on the sprayed surface.

[0021] Further, the rebound coefficient of the paint mist particles is: ; The diameter of the newly generated particles is sampled from the cumulative probability distribution function (CPDF), also known as F. The diameter of the paint mist particles produced by the splash is , and the probability of the occurrence of the paint mist particles with a diameter of ; The particle diameter corresponding to the peak of the distribution function can be obtained as follows: ; Further, the critical Weber number is set to 10, and the Weber number is defined using the relative velocity and the diameter of the paint mist particles as follows: ; The cumulative probability distribution function is used: ; The upper limit of the integral is 1, and the lower limit is 0, and the expression for the diameter of the paint mist particles produced by the splash of sequence i is obtained: ; The total number of particles produced by the splash of the paint mist is calculated, and this value is obtained using the law of conservation of mass. The mass of the splash particles is a quadratic function of the collision energy, and the mass fraction of the splash paint mist particles in the total mass of the paint mist particles is calculated as , which is expressed as: ; From the mass conservation relationship, the sum of the splash masses of all the paint mist particles is equal to the total mass of the splash, i.e.: ; where, is the total mass of the wrapping impact wall, and the total number of paint mist particles is: ; The number of splashing particles in each splashing particle is equal to the total number The specific probability density calculated by multiplying the probability distribution function corresponding to the diameter of the paint mist particle is shown in the formula: ; Further, and The expression is as follows: ; ; where, is the impact angle of the paint mist particle, where is the bounce angle of the paint mist particle generated by splashing, and the expression is: ; Further, the tangential velocity is: ; Further, the sum of the kinetic energy and surface energy of the final paint mist particle generated by splashing should be less than the sum of the energy of the paint mist particle before impact, is the critical energy of splashing, and the energy balance equation is: ; The normal velocity and tangential velocity are calculated, and the velocity is corrected to ensure energy saving, so the correction coefficient is K, and the correction expression of the normal velocity and tangential velocity of the rebound particle is: ; ; Further, the correction parameter expression is: ; Step three: when calculating the motion trajectory of the particle recovery, the velocity of the paint mist particle after rebounding splashing is taken as the initial velocity, and the force of the particle needs to be analyzed. From the perspective of mass conservation, the continuity equation of the gas phase is listed, where is the gas density, is the gas velocity field: ; Further, the momentum equation is listed, where p is the gas pressure, g is the acceleration of gravity, is the gas dynamic viscosity, is the force generated due to the presence of particles, and the equation is: ; Further, is the specific heat capacity of the gas, T is the temperature, is the thermal conductivity of the gas, and Φ is the heat exchange between the particles and the gas. Thus, the energy equation considering the thermal effect is: ; When calculating the trajectory of the particle, the resultant force of the particle needs to be calculated, and the equation for the particle is listed by Newton's second law: ; Further, is the mass of the particle, is the particle velocity, is the resistance, is the gravity, is the mass force, is the Brownian force, and the particle resistance equation is as follows: ; Where, is the resistance coefficient, is the projected area of the particle, is the relative velocity between the gas and the particle; Further, the mass force considers the buoyancy force received by the particle, and the specific equation is: ; Where, is the particle volume, is the fluid density, is the particle density; Further, the diffusion coefficient of the particle is calculated: ; The Brownian force received by the particle is: .

[0022] Where, is the Boltzmann constant, is the absolute temperature, is the dynamic viscosity of the fluid, r is the particle radius, and R is a three-dimensional vector whose components are random numbers of standard normal distribution, indicating randomness in three directions.

[0023] Further, the additional force caused by negative pressure is calculated: ; Where, is the external pressure at the position of the particle, is the internal pressure of the particle, and if the paint mist particle is solid or incompressible, it is considered consistent with atmospheric pressure or external pressure.

[0024] In addition, is the particle temperature, is the specific heat capacity, is the heat exchange rate between the particle and the gas, and the temperature change equation of the particle is: .

[0025] RK4 method is used to calculate the particle trajectory, specifically, at time step n, the particle's resultant force is calculated by step eleven, the particle's velocity and position are further updated, and the particle's position is calculated : .

[0026] Further, the particle's velocity is calculated : .

[0027] Further, the particle's position is calculated : .

[0028] Further, the particle's velocity is calculated : .

[0029] The particle's velocity is calculated .

[0030] The particle's position is calculated .

[0031] The above calculation is repeated until the required calculation time is reached, or the particle reaches a certain boundary (such as leaving the calculation domain or being captured).

[0032] The number of particles captured by the boundary is calculated, and the ratio of the number of particles recovered by the boundary to the total number of rebound particles is the paint mist particle recovery rate. If the recovery rate meets the requirements, the negative pressure is considered appropriate, otherwise, the negative pressure is changed and the calculation is continued from step three.

[0033] In the embodiments disclosed in the present application, the terms "mounting", "connecting", "connecting", "fixing" and the like should be understood broadly, for example, "connecting" can be fixed connection, or detachable connection, or integrally connected; "connected" can be directly connected, or indirectly connected through an intermediate medium. For those skilled in the art, the specific meanings of the above terms in the embodiments disclosed in the present application can be understood according to the specific circumstances.

[0034] The specific embodiments are only an explanation of the present application, and are not a limitation of the present application. Those skilled in the art can make modifications to the embodiments without creative contribution after reading the present specification, but as long as the modifications are within the scope of the claims of the present application, they are protected by the patent law.

Claims

1. A method for predicting the trajectory of paint mist particles collected by a marine painting robot, characterized in that, Includes the following steps: Step 1: Obtain some known parameters and establish a nozzle jet motion model; Step 2: Obtain the impact energy of paint mist particles on the wall. When the ejected paint mist particles meet the conditions, they will adhere to the wall or bounce off. For the new particles that bounce off, the Weibull distribution law is satisfied, and the diameter, velocity, and direction of the particles will all change. The diameter distribution of the splashed paint mist particles can be obtained through the cumulative distribution function. According to the law of conservation of mass, the number and proportion of the bounced particles can be obtained. The number of splashed paint mist particles in each paint mist particle pack is equal to the total number of paint mist particles multiplied by the probability distribution function corresponding to the diameter of the paint mist particles. After obtaining the number of splashed particles, the splash velocity of the particles can be determined. While satisfying the Mundo experimental data, the Weibull distribution function can be used as the probability distribution function of the normal velocity of the splashed particles. After listing the energy balance equation for the paint mist particles and making corrections, the normal and tangential velocities of the paint mist particles can be obtained. Step 3: Recover the rebounding paint mist particles; Step 4: Simulate the motion of the paint mist particles. Based on the force acting on the particles and their initial rebound velocity, calculate the particle trajectory using the RK4 method.

2. The method for predicting the trajectory of paint mist particles in a ship painting robot according to claim 1, characterized in that, Step two includes: defining the impact energy as: ； in, For the density of the liquid, The velocity of the paint mist particles relative to the sprayed surface. The diameter of the paint mist particles, For liquid surface tension, The height of the liquid film on the sprayed surface, The height of the wall boundary layer; If the impact energy of paint mist particles is lower than the critical impact energy, and the temperature of the sprayed surface is higher than the boiling point temperature of the paint mist particles, the paint mist particles will rebound from the wall surface. The paint mist particle rebound coefficient is: ； in, The collision angle of the paint mist particles; In step two, the Weibull distribution function is: ； Where d is the particle diameter and D is the characteristic diameter; The diameter distribution of the splashed paint mist particles is as follows: ； in, For Weber numbers, The particle diameter corresponding to the peak value of the distribution function. The critical impact energy. This represents the particle diameter corresponding to the minimum peak in the splash particle diameter distribution function.

3. The method for predicting the trajectory of paint mist particles in a ship painting robot according to claim 1, characterized in that, Step three includes: using the Euler-Lagrange method to describe the motion of the gas phase and paint mist particles using both continuous and particulate phases, and establishing an Euler model for the gas density and gas velocity field of the gas phase; for the continuous gas phase, using the continuity equation, Navier-Stokes equation, and energy equation; for the particulate phase of paint mist particles, using the particle motion equation, and establishing a model considering thermal effects.

4. The method for predicting the trajectory of paint mist particles in a ship painting robot according to claim 1, characterized in that, It also includes step five, which includes: Using the resultant velocity of the normal and tangential velocities after the splash rebound as the initial velocity, the particle trajectory is calculated using the RK4 method. At time step n, the net force of the particle is calculated, and the particle's velocity and position are further updated. This allows for the simulation and calculation of the particle's motion trajectory, thus obtaining the particle's landing position.

5. The method for predicting the trajectory of paint mist particles in a ship painting robot according to claim 2, characterized in that, Step two includes: Establish an expression for the diameter of paint mist particles generated by splashing: ； in, The diameter of the paint mist particles generated by splashing. These are random samples of the cumulative distribution function.

6. The method for predicting the trajectory of paint mist particles in a ship painting robot according to claim 5, characterized in that, Step two includes: The probability density formula for calculating splatter paint mist particles is established as follows: ； in, Let be the normal velocity component of the droplet. For reference speed, For shape parameters, For proportional parameters, The angle between the droplet and the wall surface is denoted as .

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