Oil spill diffusion prediction method and device for realizing dispersant response

Abstract

The invention discloses an oil spill diffusion prediction method and device for realizing dispersant response, relates to the technical field of sea surface oil spill simulation, and aims to solve the problems that oil spill diffusion simulation of an existing oil spill model is not fine enough and cannot support the dispersant response. The method comprises the following steps: acquiring simulation parameters of an oil spilling area and dispersant action parameters; determining a target entrainment probability of the oil spilling area through an entrainment algorithm according to the dispersant action parameters and the simulation parameters; determining the target particle diameter of each oil particle in the oil spilling region by adopting an oil drop particle size formula and a logarithmic normal distribution probability density function according to the dispersant action parameter and the simulation parameter; determining the target position of each oil particle based on the initial position, the simulation parameter, the target entrainment probability, the target particle diameter and an oil spill model; determining a prediction result of oil spill area diffusion based on the target position; according to the oil spill diffusion prediction method provided by the invention, prediction of oil spill diffusion under response of the dispersant can be realized, and the accuracy of a prediction result is improved.

Description

Technical Field

[0001] The invention relates to the technical field of sea surface oil spill simulation, and in particular to an oil spill diffusion prediction method and device for realizing dispersant response. Background Art

[0002] In marine oil spills, dispersants can speed up the dispersion of oil spills on the sea surface, forming tiny oil droplets distributed in the water. Then the natural degradation process is used to accelerate the removal of oil pollution. Using oil spill models to simulate and predict oil spills can help us understand the dispersion effect of oil pollution and the recovery of the environment, so as to adjust emergency response measures in a timely manner.

[0003] In the actual marine environment, breaking waves and turbulence at sea will disperse the oil spill into the seawater. The floating oil on the sea surface is carried to a certain depth and appears in the form of a group of oil droplets, each of which has its own size. As time goes by, the viscosity of the oil spill continues to increase due to weathering, and the dispersant effect may be ineffective. Since the process of entrainment by breaking waves is random, the existing oil spill models cannot simulate this process, and the existing oil spill models assume that the mean oil droplet size is unchanged in the oil spill simulation. At the same time, the dispersant response is not taken into account in the simulation process. Therefore, the existing oil spill models are relatively simple and not sophisticated in simulating the real oil spill process, and the predicted oil spill diffusion results are of low accuracy. Summary of the invention

[0004] The object of the present invention is to provide an oil spill diffusion prediction method and device that realizes dispersant response, which is used to simulate the process of oil spill being entrained by waves, the process of oil droplets breaking and aggregating in the sea, the process of weathering increasing viscosity and affecting the entrainment rate and oil droplet size, and the phenomenon of increased horizontal diffusivity when the proportion of oil spill entering water is high, so as to improve the accuracy of oil spill diffusion prediction results.

[0005] In order to achieve the above object, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for predicting oil spill diffusion by implementing dispersant response, comprising: Acquire simulation parameters of the oil spill area and dispersant action parameters; the simulation parameters at least include the initial positions of each oil particle in the oil spill area; Determining the target entrainment probability of the oil spill area by an entrainment algorithm according to the dispersant action parameter and the simulation parameter; According to the dispersant action parameters and the simulation parameters, the target particle diameter of each oil particle in the oil spill area is determined using an oil droplet diameter formula and a log-normal distribution probability density function; Determining the target position of each oil particle based on the initial position, simulation parameters, target entrainment probability, target particle diameter, and oil spill model; The prediction result of the spread of the oil spill area is determined based on the target position; the prediction result includes the concentration field formed by each oil particle and the target position of each oil particle.

[0006] Optionally, the dispersant action parameters include a first oil-water interfacial tension, a first oil dynamic viscosity, a second oil-water interfacial tension, and a second oil dynamic viscosity; and determining the target entrainment probability of the oil spill area by an entrainment algorithm according to the dispersant action parameters and the simulation parameters includes: According to the first oil dynamic viscosity, the first oil-water interfacial tension and simulation parameters, the entrainment formula is adopted: ; The first entrainment probability of oil particles being entrained per second is calculated; in, , , d o = 4 × [ IFT ( ρ w − ρ o ) × g ] 0 . 5 , is the first entrainment probability or the second entrainment probability, Sea surface wave coverage ratio, is the density of seawater, is the acceleration due to gravity, is the effective wave height, is the first oil-water interfacial tension or the second oil-water interfacial tension, is the first oil dynamic viscosity or the second oil dynamic viscosity, is the oil density; , , is a constant; According to the second oil dynamic viscosity, the second oil-water interfacial tension and simulation parameters, the second entrainment probability of the oil spill particles being entrained per second is calculated using the entrainment formula; Substitute the maximum value of the first entrainment probability and the second entrainment probability into the formula: ; Calculate the target entrainment probability of oil particles being entrained in the oil spill area at each time step; in, is the target entrainment probability, is the time step.

[0007] Optionally, the determining the target particle diameter of each oil particle in the oil spill area according to the dispersant action parameter and the simulation parameter by using the oil droplet diameter formula and the log-normal distribution probability density function comprises: According to the first oil dynamic viscosity and the first oil-water interfacial tension, the volume mean diameter formula is adopted: ; The mean volume of the first oil drop in the oil spill area is calculated; in, is the mean volume of the first oil droplet or the mean volume of the second oil droplet, , , is a constant; According to the second oil dynamic viscosity and the second oil-water interfacial tension, the second oil droplet volume mean of the oil spill area is calculated using the volume mean diameter formula; determining a minimum value between the first oil droplet volume mean and the second oil droplet volume mean as a target mean diameter; According to the target mean diameter, the lognormal distribution probability density function is used: ; Reducing the particle size of each oil particle in the oil spill area to obtain a target particle diameter of each oil particle in the oil spill area; in, is the natural logarithmic standard deviation, is the target particle diameter, is the target mean diameter.

[0008] Optionally, the oil spill model includes a horizontal first component calculation function, a horizontal second component calculation function and a vertical component calculation function; and determining the target position of each oil particle based on the initial position, simulation parameters, target entrainment probability, target particle diameter and the oil spill model includes: The oil particles in the oil spill area are divided into first oil particles, second oil particles and third oil particles according to the initial position; the first oil particles are oil particles located within a first preset depth range; the second oil particles are oil particles located within a second preset depth range; the third oil particles are oil particles located within a third preset depth range; According to the target particle diameter and simulation parameters, the vertical component calculation function is used, and the vertical component of displacement is obtained by solving the Euler method with second-order accuracy; determining the offset displacement of the first oil particle based on the simulation parameters, the target entrainment probability, the vertical component of the displacement, and the oil spill model; Determining the offset displacement of the second oil particle and the offset displacement of the third oil particle based on the simulation parameters, the vertical component of the displacement and the oil spill model; The target position of each oil particle is determined according to the initial position and the offset displacement of each oil particle.

[0009] Optionally, the vertical component calculation function is used according to the target particle diameter and the simulation parameters, and the vertical component of the displacement is obtained by solving the Euler method with second-order accuracy, including: Using the formula: ; The vertical component of velocity is calculated; in, , is the vertical component of velocity; is the vertical component of the current velocity, Buoyancy velocity, is the vertical component of turbulent velocity; is the acceleration due to gravity, is the oil density, is the density of seawater, is the target particle diameter, is the critical particle size, is the kinematic viscosity of seawater; The product of the vertical component of velocity and the time step is determined as the vertical component of displacement.

[0010] Optionally, determining the offset displacement of the first oil particle based on the simulation parameters, the target entrainment probability, the vertical component of the displacement, and the oil spill model includes: Determining entrained particles in the first oil particles according to the target entrainment probability; the number of the entrained particles is the product of the number of the first oil particles and the target entrainment probability; Randomly assigning an entrainment displacement to the entrained particles to obtain an entrainment displacement of the first oil particle; the entrainment displacement is a value between 0 and 1.5 times the breaking wave height, or the entrainment displacement conforms to a Gaussian distribution with 1.5 times the breaking wave height as the mean and 0.35 times the breaking wave height as the standard deviation; the entrainment displacement of particles in the first oil particle other than the entrained particle is 0; The deflection displacement of the first oil particle is determined based on the entrainment displacement and the vertical component of the displacement.

[0011] Optionally, determining the offset displacement of the second oil particle based on the simulation parameter, the vertical component of the displacement and the oil spill model includes: Using the formula: ; The first component of horizontal velocity is calculated; in, is the first component of horizontal velocity; is the component of the ocean current velocity in the x-axis direction, is the component of the ocean current velocity in the y-axis direction, is the component of wind speed at 10m above the sea surface in the x-axis direction, is the component of wind speed at 10m above the sea surface in the y-axis direction, It is the component of the oil particle drift velocity on the x-axis brought by the residual flow generated by the nonlinear wave; Confirming the product of the first component of the horizontal velocity and the time step as the first component of the horizontal displacement; Using the formula: ; The second component of horizontal velocity is calculated; in, is the second component of horizontal velocity; is the component of the oil particle drift velocity on the y-axis caused by the residual flow generated by the nonlinear wave, is the component of the current velocity in the y-axis direction; Confirming the product of the second component of the horizontal velocity and the time step as the second component of the horizontal displacement; The first component of the horizontal displacement, the second component of the horizontal displacement and the vertical component of the displacement are synthesized to obtain the offset displacement of the second oil particle.

[0012] Optionally, determining the target position of each oil particle according to the initial position and the offset displacement of each oil particle includes: The initial position and the offset displacement of each oil particle are accumulated to obtain the migration position of each oil particle after the first time step; The migration position of each oil particle is taken as the new initial position, and the migration position of each oil particle in the next time step is continued to be determined according to the new initial position, simulation parameters, target entrainment probability, target particle diameter and oil spill model, until the cumulative time reaches the preset time, and the target position of each oil particle is obtained.

[0013] Optionally, the prediction result of determining the spread of the oil spill area based on the target location includes: The residence time of each oil particle at a depth of 0.05 m below the sea surface is calculated, and the residence time is substituted into the formula: ; The standard deviation corresponding to each oil particle is calculated; in, for The standard deviation of the oil particle, is the residence time, is the diffusion coefficient; A target volume is assigned to each oil particle at the target position according to the standard deviation to form a concentration field, thereby obtaining a prediction result of the spread of the oil spill area; the transverse section of the target volume is a circle with the standard deviation as the radius, and the longitudinal section of the target volume is an ellipse with 2 times the standard deviation as the minor axis.

[0014] Compared with the prior art, the present invention provides a method for predicting the spread of oil spills by implementing dispersant response, including obtaining simulation parameters of the oil spill area and dispersant action parameters; according to the dispersant action parameters and simulation parameters, the target entrainment probability of the oil spill area is determined by an entrainment algorithm, so as to simulate the situation that the wave entrainment brings the particles on the sea surface into the sea; the target particle diameter of each oil particle in the oil spill area can be obtained by using the oil droplet diameter formula and the log-normal distribution probability density function; based on the initial position, simulation parameters, target entrainment probability, target particle diameter and oil spill model, the target position of each oil particle is determined; based on the target position, the prediction result of the spread of the oil spill area is determined. The present invention can simulate the spread of oil spills after the addition of dispersants, taking into account the wave entrainment rate problem and the influence of wave entrainment on the depth of the spread of oil particles, and taking into account the change in the diameter of oil particles caused by the breakage and aggregation of oil particles in the sea, so that the target position of each oil particle obtained based on this is more accurate, and thus the prediction result of the spread of oil spills is more accurate.

[0015] In a second aspect, the present invention further provides an oil spill diffusion prediction device for realizing dispersant response, comprising: A parameter acquisition module is used to acquire simulation parameters of the oil spill area and dispersant action parameters; the simulation parameters at least include the initial position of each oil particle in the oil spill area; A target entrainment probability calculation module, used to determine the target entrainment probability of the oil spill area through an entrainment algorithm according to the dispersant action parameters and the simulation parameters; a target particle diameter calculation module, for determining the target particle diameter of each oil particle in the oil spill area according to the dispersant action parameter and the simulation parameter, using the oil droplet diameter formula and the log-normal distribution probability density function; A target position calculation module, for determining the target position of each oil particle based on the initial position, simulation parameters, target entrainment probability, target particle diameter and oil spill model; The prediction result output module is used to determine the prediction result of the oil spill area diffusion based on the target position; the prediction result includes the concentration field formed by each oil particle and the target position of each oil particle.

[0016] Compared with the prior art, the beneficial effects of the dispersant-responsive oil spill diffusion prediction device provided by the present invention are the same as the beneficial effects of the dispersant-responsive oil spill diffusion prediction method described in the above technical solution, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A flow chart of an oil spill diffusion prediction method for realizing dispersant response provided by the present invention; Figure 2 A schematic diagram of the calculation model of the dispersant provided by the present invention after use; Figure 3 A schematic diagram of the weathering effect principle provided by the present invention; Figure 4 A schematic diagram showing the effect of the dispersant and weathering provided by the present invention on wave entrainment and oil droplet size; Figure 5 The oil spill diffusion principle diagram provided by the present invention; Figure 6 A comparison chart of the prediction results provided by the present invention, the experimental results and the OSCAR model results; Figure 7 A schematic diagram of the structure of an oil spill diffusion prediction device for realizing dispersant response provided by the present invention. DETAILED DESCRIPTION

[0018] In order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, words such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and effects. For example, the first threshold and the second threshold are only used to distinguish different thresholds, and their order is not limited. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0019] It should be noted that, in the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0020] In the present invention, "at least one" means one or more, and "plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, the combination of a and b, the combination of a and c, the combination of b and c, or the combination of a, b and c, where a, b, c can be single or multiple.

[0021] There are five main aspects of the simulation of oil spill diffusion after the actual addition of dispersants: the first is the simulation of wave entrainment, the second is the simulation of the oil droplet distribution spectrum, the third is the dispersant reducing the oil-water interfacial tension, the fourth is the simulation of the process of weathering causing the viscosity to increase continuously, affecting the entrainment rate and the size of the oil droplets, and the fifth is the increase in horizontal diffusivity caused by the increase in oil droplets entering the water after the addition of dispersants. The existing oil spill model takes into account vertical turbulence, but does not take into account wave entrainment, and adopts a uniform distribution of 1-1000μm in the oil droplet size. Therefore, the diffusion simulation of the existing oil spill model is not detailed enough to support the dispersant response, resulting in low accuracy of the oil spill diffusion prediction results.

[0022] In order to solve the above problems, the present invention provides an oil spill diffusion prediction method and device that realizes dispersant response. On the basis of the original oil spill model, a wave entrainment algorithm and an oil droplet size algorithm are written, and the horizontal diffusion setting is modified, and particle cloud processing is added to the visualization analysis module. The present invention is mainly used in calculating particle displacement. First, a particle cycle is performed to determine each particle to determine whether the particle is located at the bottom of the seabed or outside the calculated longitude and latitude boundaries. If so, the particle is skipped. If not, the wind flow data is inserted to calculate the particle displacement. After the displacement calculation of all particles is completed, the particle position state is updated, and the particle displacement calculation of the next time cycle is performed. Next, it is explained in conjunction with the accompanying drawings.

[0023] See also Figure 1 The present invention provides a method for predicting oil spill diffusion by implementing dispersant response, comprising the following steps: Step S1: obtaining simulation parameters of the oil spill area and dispersant action parameters; The simulation parameters include the initial position of each oil particle in the oil spill area, environmental field data and wind flow field data; environmental field data include: sea surface breaking wave coverage ratio, seawater density, effective wave height, oil density, current speed, oil particle drift speed caused by wave residual flow generated by nonlinear waves, turbulent speed, kinematic viscosity of seawater, wave number, wave period and breaking wave height; wind flow field data include 10m high wind speed on the sea surface, wind drift factor and deflection. Dispersant action parameters include the first oil-water interfacial tension and the first oil dynamic viscosity before adding dispersant, the second oil-water interfacial tension after adding dispersant, and the second oil dynamic viscosity when dispersant acts.

[0024] Step S2: determining the target entrainment probability of the oil spill area by an entrainment algorithm according to the dispersant action parameter and the simulation parameter; Step S3: according to the dispersant action parameters and the simulation parameters, the target particle diameter of each oil particle in the oil spill area is determined by using the oil droplet diameter formula and the log-normal distribution probability density function; Step S4: determining the target position of each oil particle based on the initial position, simulation parameters, target entrainment probability, target particle diameter and oil spill model; The initial position and target position of the oil particles are both three-dimensional coordinates. The coordinate system corresponding to the three-dimensional coordinates is the coordinate system composed of the x-axis, y-axis, and z-axis, where the plane composed of the x-axis and y-axis is parallel to the sea level, and the z-axis direction is the seawater depth direction. The initial position is represented by (x1, y1, z1).

[0025] Step S5: Determine the prediction result of the spread of the oil spill area based on the target position; the prediction result includes the concentration field formed by each oil particle and the target position of each oil particle.

[0026] The prediction results are displayed in a visual way.

[0027] It can be seen from the above method that the present invention determines the target entrainment probability of the oil spill area through the entrainment algorithm, and can realize the simulation of the situation that the wave entrainment brings the particles on the sea surface into the sea. The target particle diameter of each oil particle in the oil spill area can be obtained by using the oil droplet diameter formula and the log-normal distribution probability density function; the present invention can more accurately simulate the three-dimensional migration of the spilled oil after the dispersant is added, taking into account the wave entrainment rate problem and the influence of wave entrainment on the diffusion depth of the oil particles, and taking into account the change in the diameter of the oil particles caused by the breakage and aggregation of the oil particles in the sea. Based on this, the target position of each oil particle obtained is more accurate, so that the obtained oil spill diffusion prediction result is more accurate.

[0028] Based on the above method, the present invention provides some specific implementation methods, which are described in detail below.

[0029] like Figure 2 As shown, the above step S2 can be implemented based on the following steps: According to the first oil dynamic viscosity, the first oil-water interfacial tension and simulation parameters, the entrainment formula (1) is used: (1) The first entrainment probability of oil particles being entrained per second is calculated; in, , , d o = 4 × [ IFT ( ρ w − ρ o ) × g ] 0 . 5 , , is the dispersant-to-oil ratio, is the first entrainment probability or the second entrainment probability, Sea surface wave coverage ratio, is the density of seawater, is the acceleration due to gravity, is the effective wave height, is the first oil-water interfacial tension or the second oil-water interfacial tension, is the first oil dynamic viscosity or the second oil dynamic viscosity, is the oil density; , , is a constant, is 4.604×10 -10 , is 1.805, is -1.023.

[0030] According to the second oil dynamic viscosity, the second oil-water interfacial tension and simulation parameters, the entrainment formula of formula (1) is used to calculate the second entrainment probability of the oil spill particles being entrained per second; In the actual application of the model, it is impossible to set the time step of the model to 1s, so the entrainment probability per second needs to be converted into the entrainment probability per time step. The specific steps are as follows: Substitute the maximum value of the first entrainment probability and the second entrainment probability into formula (2): (2) Calculate the target entrainment probability of oil particles being entrained in the oil spill area at each time step; in, is the target entrainment probability, is the time step, is the maximum value of the first entrainment probability and the second entrainment probability.

[0031] Considering that the dispersant may be ineffective, the maximum value of the first entrainment probability and the second entrainment probability is taken as the target entrainment probability.

[0032] The existing oil droplet model ignores the breakup and aggregation process of oil droplets in the sea. It assumes that the size of oil droplets in water is constant. When a large amount of floating oil enters the water, the distribution spectrum predicted by the existing oil droplet model will cause the probability of oil droplets floating to be lower than the actual value.

[0033] In view of this, the breakup and aggregation process of oil droplets in the sea should be considered. When the spilled oil on the sea surface is brought below the sea surface, it will appear in the form of a group of oil droplets, each of which has its own size. Therefore, step S3 is implemented by the following steps: like Figure 2 As shown, according to the first oil dynamic viscosity and the first oil-water interfacial tension, the volume mean diameter formula (3) is adopted: (3) The mean volume of the first oil drop in the oil spill area is calculated; in, is the mean volume of the first oil droplet or the mean volume of the second oil droplet, , , is a constant; is 1.791, is 0.460, It is -0.518. number, The calculation of the number is the same as that in formula (1).

[0034] According to the second oil dynamic viscosity and the second oil-water interfacial tension, the volume mean diameter formula of formula (3) is used to calculate the second oil droplet volume mean in the oil spill area; determining a minimum value between the first oil droplet volume mean and the second oil droplet volume mean as a target mean diameter; Considering that the dispersant may be ineffective, the minimum value between the first oil droplet volume mean and the second oil droplet volume mean is taken as the target mean diameter.

[0035] The size distribution of oil particle diameter is more consistent with the log-normal distribution. The target mean diameter and standard deviation are used to restore the entire distribution spectrum of crude oil particle diameter, as follows: According to the target mean diameter, a log-normal distribution probability density function is used, as shown in formula (4): (4) Reducing the particle size of each oil particle in the oil spill area to obtain a target particle diameter of each oil particle in the oil spill area; in, is the natural logarithmic standard deviation, is the target particle diameter, is the target mean diameter.

[0036] The natural logarithmic standard deviation ranges from 0.5 to 0.921. For example, the natural logarithmic standard deviation is 0.5, 0.921, 0.38×ln(10), etc. After our optimization, the standard deviation of 0.921 predicts the most accurate result. The larger the standard deviation, the flatter and more dispersed the probability density function graph will be, the probability of the particle size being randomly assigned to the middle value will decrease, and the probability of being randomly assigned to a large or small particle size will increase. Therefore, in practical applications, it is necessary to determine the appropriate value of the logarithmic standard deviation based on the measured data.

[0037] The diameter of the oil droplets in the mixed layer is re-assigned at each time step, and the depth of the mixed layer adopts the suggestion of (0~zi) zi=1.5 times the breaking wave height (Sweeney 1988). In addition, in the upper half of the mixed layer, the volume mean diameter is still affected by the wind speed, and in the lower half of the mixed layer, the volume mean diameter will be fixed; in other words, in the upper half of the mixed layer, the randomness of the oil droplet diameter is restored by the constantly changing volume mean diameter, and in the lower half, it is restored by the fixed volume mean diameter, which will reflect that the depth of the influence of wind speed is constantly changing, and the target particle diameter will be fixed below the mixed layer.

[0038] By restoring the particle size of each oil particle using the above method, the distribution spectrum of the oil particle diameter obtained is closer to the true value.

[0039] See also Figure 3 and Figure 4 The effect of adding dispersants at different times is different. In the early stage of oil spill, the dispersion effect is good after adding dispersants because the oil viscosity is low. However, as time goes by, the oil viscosity increases under the action of weathering, and the dispersion effect produced by the addition of dispersants will decrease. When the oil viscosity reaches a certain level, the dispersion effect will be lost if dispersants are added. The effects of effective and ineffective dispersants on the entrainment process and the size of oil droplets are different. Figure 3As shown in the figure, when the dispersant is effective, the oil-water interfacial tension decreases, the oil entrained by the waves will increase, and the oil will be dispersed into smaller oil droplets. However, as the oil viscosity increases, the dispersant gradually becomes ineffective, the theoretically calculated amount of oil entrained by the waves will decrease, and the diameter of the oil droplets will also increase. This is counterintuitive. The dispersant cannot increase the diameter of the oil droplets and reduce the amount of entrained oil. When the amount of oil entrained by the waves decreases after the dispersant is added, the diameter of the oil droplets increases, indicating that the dispersant has failed. In the above steps, when calculating the entrainment probability and the target particle diameter, before the dispersant acts, the viscosity used is the first oil dynamic viscosity and the first oil-water interfacial tension; after the dispersant acts, if the oil particle mean diameter predicted by the reduced interfacial tension and the increased viscosity is smaller, it means that the dispersant is effective, if it is larger, it means that it is ineffective, and the minimum value is used as the target mean diameter to calculate the target particle diameter; for the entrainment probability, if the predicted entrainment probability is larger, it is effective, otherwise it is invalid, and the maximum value is used to calculate the target entrainment probability, taking into account the possibility that the simulated dispersant may fail, and the calculated target entrainment probability and target particle diameter are more accurate.

[0040] The oil spill model in the above step S4 is shown in formula (5): (5) in, , , is the first component of horizontal velocity, The corresponding calculation formula is the horizontal first component calculation function, which is used to calculate the component of the velocity in the x-axis direction. is the second component of the horizontal velocity of the oil particles, The corresponding calculation formula is the horizontal second component calculation function, which is used to calculate the component of the velocity in the y-axis direction. is the vertical component of velocity; The corresponding calculation formula is the vertical component calculation function, which is used to calculate the component of the velocity in the z-axis direction; is the buoyancy velocity, is the acceleration due to gravity, is the oil density, is the density of seawater, is the target particle diameter, is the critical particle size, is the kinematic viscosity of seawater, and its value is 1.311 mm 2 / s; is the component of the ocean current velocity in the x-axis direction, is the component of the ocean current velocity in the y-axis direction, is the component of the ocean current velocity in the z-axis direction, is the component of turbulent velocity in the z-axis direction; is the component of turbulent velocity in the x-axis direction, is the component of turbulent velocity in the y-axis direction, is the component of wind speed at 10m above the sea surface in the x-axis direction, is the component of wind speed at 10m above the sea surface in the y-axis direction, is the component of the oil particle drift velocity on the x-axis caused by the residual flow generated by the nonlinear wave, is the component of the oil particle drift velocity on the y-axis caused by the residual flow generated by the nonlinear wave, is the wind drift factor, with a value of 0.03. is the deflection angle, and its value is 0.

[0041] The second-order accuracy Euler method is used to solve the equation of formula (5) to obtain the velocity of the oil particles. The velocity of the oil particles is multiplied by the time step to obtain the offset displacement of the oil particles in one time step. The migration position of the oil particles in the next time step can be obtained by adding the offset displacement to the initial position. After looping all particles, the migration position of the oil particles in the next time step is calculated using the newly obtained migration position as the initial position until the accumulated time reaches the output time and the position of the oil particles is output. Exemplarily, the time step is set to 10 minutes, and the position of the oil particles is output once every hour. The total simulation time of the model is set to 2 days, the starting simulation time is 0, and the initial position is the position of each oil particle when the simulation time is 0. Then the result is output once every hour until the model terminates when the simulation time is 2 days.

[0042] The offset displacement is the displacement of each oil particle in one time step. The component of the offset displacement of each oil particle in the z-axis direction is the vertical component of the displacement, the component in the x-axis direction is the first component of the horizontal displacement, and the component in the y-axis direction is the second component of the horizontal displacement.

[0043] Based on the oil spill model of formula (5), the above step S4 can be specifically implemented by the following steps: Step S41: dividing the oil particles in the oil spill area into first oil particles, second oil particles and third oil particles according to the initial position; the first oil particles are oil particles located within a first preset depth range; the second oil particles are oil particles located within a second preset depth range; the third oil particles are oil particles located within a third preset depth range; The depth of action of dispersant and wind horizontal drag is within 1m, and the depth of action of wave entrainment is within 0.05m. Therefore, the first preset depth range is 0-0.05m, the second preset depth range is 0.05-1m, and the third preset depth is 1m-seabed. Figure 5As shown in the figure, the depth of the mixed layer is 1.5 times the breaking wave height. In the mixed layer, wave entrainment affects oil particles within 0.05 m; oil particles within 1 m are affected by horizontal wind drag and dispersants; oil particles below 1 m will not be affected by horizontal wind drag.

[0044] Step S42: according to the target particle diameter and simulation parameters, the vertical component calculation function is used, and the vertical component of displacement is obtained by solving the Euler method with second-order accuracy; Specifically, step S42 includes: using the vertical component calculation function in formula (5): ; The vertical component of velocity is calculated; The vertical component of displacement is determined as the product of the vertical component of velocity and the time step.

[0045] Step S43: determining the offset displacement of the first oil particle based on the simulation parameters, the target entrainment probability, the vertical component of the displacement and the oil spill model; Specifically, step S43 includes the following steps: Step S431: Use the horizontal first component calculation function in formula (5): ; The first component of horizontal velocity is calculated; Step S432: confirming the product of the first component of horizontal velocity and the time step as the first component of horizontal displacement; Step S433: Use the horizontal second component calculation function in formula (5): ; The second component of horizontal velocity is calculated; Step S434: confirming the product of the second component of the horizontal velocity and the time step as the second component of the horizontal displacement; Step S435: determining the entrained particles in the first oil particles according to the target entrainment probability; the number of the entrained particles is the product of the number of the first oil particles and the target entrainment probability; Step S436: randomly assigning an entrainment displacement to the entrained particles to obtain the entrainment displacement of the first oil particle; the entrainment displacement is a value of 0-1.5 times the breaking wave height, or the entrainment displacement conforms to a Gaussian distribution with 1.5 times the breaking wave height as the mean and 0.35 times the breaking wave height as the standard deviation; the entrainment displacement of the particles in the first oil particle except the entrained particles is 0; after our optimization, the result using the entrainment displacement as a value of 0-1.5 times the breaking wave height is the most accurate.

[0046] Step S436: Determine the offset displacement of the first oil particle based on the entrained displacement, the vertical component of the displacement, the first component of the horizontal displacement, and the second component of the horizontal displacement. Specifically, the sum of the entrained displacement and the vertical component of the displacement obtained in step S42 is determined as the new vertical component of the displacement of the first oil particle; the new vertical component of the displacement of the first oil particle, the first component of the horizontal displacement, and the second component of the horizontal displacement are synthesized to obtain the offset displacement of the first oil particle.

[0047] Step S43: determining the offset displacement of the second oil particle and the offset displacement of the third oil particle based on the simulation parameters, the vertical component of the displacement and the oil spill model; The calculation steps of the offset displacement of the second oil particle are: The vertical component of the displacement obtained in step S42, the first component of the horizontal displacement obtained in step S432, and the second component of the horizontal displacement obtained in step S434 are synthesized to obtain the offset displacement of the second oil particle.

[0048] The calculation steps of the offset displacement of the third oil particle are: Since the third oil particle is located below 1m and is not affected by wind speed, let =0, at this time, the first component of the horizontal velocity of the third oil particle is obtained according to the horizontal first component calculation function, and the first component of the horizontal displacement of the third oil particle is obtained by multiplying the first component of the horizontal velocity of the third oil particle by the time step; Let the second horizontal component be calculated in the function =0, at this time, the second component of the horizontal velocity of the third oil particle is obtained according to the horizontal second component calculation function, and the second component of the horizontal displacement of the third oil particle is obtained by multiplying the second component of the horizontal velocity of the third oil particle by the time step; The offset displacement of the third oil particle is obtained by synthesizing the first component of the horizontal displacement, the second component of the horizontal displacement, and the vertical component of the displacement obtained in step S42.

[0049] Step S44: determining the target position of each oil particle according to the initial position and the offset displacement of each oil particle.

[0050] Specifically, the initial position and the offset displacement of each oil particle are accumulated to obtain the migration position of each oil particle after the first time step; The migration position of each oil particle is taken as the new initial position, and the migration position of each oil particle in the next time step is continued to be determined according to the new initial position, simulation parameters, target entrainment probability, target particle diameter and oil spill model, until the cumulative time reaches the preset time, and the target position of each oil particle is obtained.

[0051] During the iterative cycle, when there is no dispersant, only the first entrainment probability and the first oil droplet volume average are calculated. Each time the dispersant acts, the first and second entrainment probabilities, the first oil droplet volume average and the second oil droplet volume average are calculated. After judging that the dispersant is effective, only the second entrainment probability and the second oil droplet volume average will be calculated in the following time. If the dispersant is ineffective, only the first entrainment probability and the first oil droplet volume average will be calculated in the following time.

[0052] As an optional method, the above step S5 can be specifically implemented by the following steps: By counting the residence time of each oil particle at a depth of 0.05 m below the sea surface, the residence time is substituted into the formula: ; The standard deviation corresponding to each oil particle is calculated; in, for The standard deviation of the oil particle, is the residence time, is the diffusion coefficient in all directions; the diffusion coefficient includes the horizontal diffusion coefficient and the vertical diffusion coefficient. Within 0.05m of the sea surface, the horizontal diffusion coefficient of 1 / 100 is used, and the horizontal diffusion coefficient of other depths adopts the normal value.

[0053] A target volume is assigned to each oil particle at the target position according to the standard deviation to form a concentration field, thereby obtaining a prediction result of the spread of the oil spill area; the transverse section of the target volume is a circle with the standard deviation as the radius, and the longitudinal section of the target volume is an ellipse with 2 times the standard deviation as the axis.

[0054] The above method uses the standard deviation as the radius to disperse the mass of each oil particle into a circle in the horizontal direction. Many such particle clouds are superimposed to form a new concentration field. In the vertical direction, the mass of the oil particles is dispersed into an ellipse with 2 times the standard deviation as the axis of the ellipse. The circle that encounters the boundary will be truncated, so that the mass is distributed in the truncated circle. Such particle cloud processing can increase the accuracy of the concentration field without changing the grid resolution and the number of particles. At the same time, the prediction of oil spill diffusion by this method can greatly shorten the calculation time.

[0055] After model verification and optimization, the prediction result obtained by using the uniform probability entrainment simulation method with a logarithmic standard deviation of 0.921 is the most accurate, with a relative error of 0.09347.

[0056] See also Figure 6, the present invention uses experimental data for verification. The method of the present invention can well show the universal law of oil spill model, and the model results are consistent with the results of (Li, Spaulding et al. 2017). Compared with the international advanced model OSCAR, the prediction results of the present invention are better and more reasonable. Figure 6 As shown, the experimental data LAB obtained by laboratory simulation is compared with the results NMEFC obtained by the method of the present invention and the simulation results of the international advanced model OSCAR. The relative error of the first three groups of the results NMEFC obtained by the method of the present invention is 0.09347. The figure shows the comparison of the results of laboratory experiments with different dispersant-to-oil ratios using 4 kinds of oils and the results predicted by the two models of underwater oil to total oil.

[0057] In the case of dividing each functional module into corresponding functional modules, Figure 7 FIG. 2 shows a schematic diagram of the structure of an oil spill diffusion prediction device for realizing dispersant response provided by the present invention. Figure 7 As shown, the device comprises: The parameter acquisition module 701 is used to acquire simulation parameters of the oil spill area and dispersant action parameters; the simulation parameters at least include the initial position of each oil particle in the oil spill area; A target entrainment probability calculation module 702 is used to determine the target entrainment probability of the oil spill area through an entrainment algorithm according to the dispersant action parameters and the simulation parameters; The target particle diameter calculation module 703 is used to determine the target particle diameter of each oil particle in the oil spill area according to the dispersant action parameter and the simulation parameter by using the oil droplet diameter formula and the log-normal distribution probability density function; A target position calculation module 704 is used to determine the target position of each oil particle based on the initial position, simulation parameters, target entrainment probability, target particle diameter and oil spill model; The prediction result output module 705 is used to determine the prediction result of the spread of the oil spill area based on the target position; the prediction result includes the concentration field formed by each oil particle and the target position of each oil particle.

[0058] Optionally, the dispersant action parameters include a first oil-water interfacial tension, a first oil dynamic viscosity, a second oil-water interfacial tension, and a second oil dynamic viscosity; the target entrainment probability calculation module 702 may include: The first entrainment probability calculation unit is used to use an entrainment formula according to the first oil dynamic viscosity, the first oil-water interfacial tension and simulation parameters: ; The first entrainment probability of oil particles being entrained per second is calculated; in, , , d o = 4 × [ IFT ( ρ w − ρ o ) × g ] 0 . 5 , is the first entrainment probability or the second entrainment probability, Sea surface wave coverage ratio, is the density of seawater, is the acceleration due to gravity, is the effective wave height, is the first oil-water interfacial tension or the second oil-water interfacial tension, is the first oil dynamic viscosity or the second oil dynamic viscosity, is the oil density; , , is a constant; A second entrainment probability calculation unit is used to calculate a second entrainment probability of the oil spill particles being entrained per second using the entrainment formula according to the second oil dynamic viscosity, the second oil-water interfacial tension and simulation parameters; The target entrainment probability calculation unit is used to substitute the maximum value of the first entrainment probability and the second entrainment probability into the formula: ; Calculate the target entrainment probability of oil particles being entrained in the oil spill area at each time step; in, is the target entrainment probability, is the time step, is the maximum value of the first entrainment probability and the second entrainment probability.

[0059] Optionally, the target particle diameter calculation module 703 may include: The first oil droplet volume mean value calculation unit is used to calculate the volume mean diameter according to the first oil dynamic viscosity and the first oil-water interfacial tension using the following formula: ; The mean volume of the first oil drop in the oil spill area is calculated; in: is the mean volume of the first oil droplet or the mean volume of the second oil droplet, , , is a constant; A second oil droplet volume mean value calculation unit, configured to calculate the second oil droplet volume mean value of the oil spill area according to the second oil dynamic viscosity and the second oil-water interfacial tension by using the volume mean diameter formula; a target mean diameter determination unit, configured to determine a minimum value between the first oil droplet volume mean and the second oil droplet volume mean as a target mean diameter; The target particle diameter calculation unit is used to use the log-normal distribution probability density function according to the target mean diameter: ; Reducing the particle size of each oil particle in the oil spill area to obtain a target particle diameter of each oil particle in the oil spill area; in, is the natural logarithmic standard deviation, is the target particle diameter, is the target mean diameter.

[0060] Optionally, the oil spill model includes a first component calculation function, a second component calculation function and a vertical component calculation function; the target position calculation module 704 may include: a particle classification unit, for classifying the oil particles in the oil spill area into first oil particles, second oil particles and third oil particles according to the initial position; the first oil particles are oil particles located within a first preset depth range; the second oil particles are oil particles located within a second preset depth range; the third oil particles are oil particles located within a third preset depth range; A displacement vertical component calculation unit, for obtaining the displacement vertical component by using the vertical component calculation function according to the target particle diameter and simulation parameters and solving the displacement vertical component by the second-order accuracy Euler method; A first oil particle displacement calculation unit, used to determine the first oil particle displacement based on the simulation parameters, target entrainment probability, displacement vertical component and oil spill model; A displacement calculation unit for the second oil particle and the third oil particle, used to determine the displacement of the second oil particle and the third oil particle based on the simulation parameters, the vertical component of the displacement and the oil spill model; The loop iteration unit is used to determine the target position of each oil particle according to the initial position and the offset displacement of each oil particle.

[0061] Optionally, the displacement vertical component calculation unit may be specifically used for: Using the formula: ; The vertical component of velocity is calculated; in, , is the vertical component of velocity; is the vertical component of the current velocity, Buoyancy velocity, is the vertical component of turbulent velocity; is the buoyancy velocity, is the acceleration due to gravity, is the oil density, is the density of seawater, is the target particle diameter, is the critical particle size, is the kinematic viscosity of seawater; The product of the vertical component of velocity and the time step is determined as the vertical component of displacement.

[0062] Optionally, the first oil particle offset displacement calculation unit may be specifically used for: Determining entrained particles in the first oil particles according to the target entrainment probability; the number of the entrained particles is the product of the number of the first oil particles and the target entrainment probability; An entrainment displacement is randomly assigned to the entrained particles to obtain the entrainment displacement of the first oil particle; the entrainment displacement is a value between 0 and 1.5 times the breaking wave height, or the entrainment displacement conforms to a Gaussian distribution with 1.5 times the breaking wave height as the mean and 0.35 times the breaking wave height as the standard deviation; the entrainment displacement of particles in the first oil particles except the entrained particles is 0; and the offset displacement of the first oil particle is determined based on the entrainment displacement and the vertical component of the displacement.

[0063] Optionally, the second oil particle offset calculation unit may be used to: Determining the displacement of the second oil particle based on the simulation parameters, the vertical component of the displacement and the oil spill model includes: Using the formula: ; The first component of horizontal velocity is calculated; in, is the first component of horizontal velocity; is the component of the ocean current velocity in the x-axis direction, is the component of the ocean current velocity in the y-axis direction, is the component of wind speed at 10m above the sea surface in the x-axis direction, is the component of wind speed at 10m above the sea surface in the y-axis direction, It is the component of the oil particle drift velocity on the x-axis brought by the residual flow generated by the nonlinear wave; Confirming the product of the first component of the horizontal velocity and the time step as the first component of the horizontal displacement; Using the formula: ; The second component of horizontal velocity is calculated; in, is the second component of horizontal velocity; is the component of the oil particle drift velocity on the y-axis caused by the residual flow generated by the nonlinear wave, is the component of the current velocity in the y-axis direction; Confirming the product of the second component of the horizontal velocity and the time step as the second component of the horizontal displacement; The first component of the horizontal displacement, the second component of the horizontal displacement and the vertical component of the displacement are synthesized to obtain the offset displacement of the second oil particle.

[0064] Optionally, the loop iteration unit may be used to: The initial position and the offset displacement of each oil particle are accumulated to obtain the migration position of each oil particle after the first time step; The migration position of each oil particle is taken as the new initial position, and the migration position of each oil particle in the next time step is continued to be determined according to the new initial position, simulation parameters, target entrainment probability, target particle diameter and oil spill model, until the cumulative time reaches the preset time, and the target position of each oil particle is obtained.

[0065] Optionally, the prediction result output module 705 may be used to: The residence time of each oil particle at a depth of 0.05 m below the sea surface is calculated, and the residence time is substituted into the formula: ; The standard deviation corresponding to each oil particle is calculated; in, for The standard deviation of the oil particle, is the residence time, is the diffusion coefficient; A target volume is assigned to each oil particle at the target position according to the standard deviation to form a concentration field, thereby obtaining a prediction result of the spread of the oil spill area; the transverse section of the target volume is a circle with the standard deviation as the radius, and the longitudinal section of the target volume is an ellipse with 2 times the standard deviation as the minor axis.

[0066] The embodiment of the present invention can divide the functional modules according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0067] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instruction is loaded and executed on a computer, the process or function described in the embodiment of the present invention is executed in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device or other programmable device. The computer program or instruction may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program or instruction may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium, such as a floppy disk, a hard disk, or a tape; it may also be an optical medium, such as a digital video disc (DVD); it may also be a semiconductor medium, such as a solid state drive (SSD).

[0068] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in a claim. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0069] Although the present invention has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present invention. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present invention as defined by the appended claims and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present invention. Obviously, those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, the present invention is intended to include such modifications and variations if they fall within the scope of the claims of the present invention and their equivalents.

Claims

1. A method for predicting oil spill diffusion by implementing dispersant response, characterized in that: include: Acquire simulation parameters of the oil spill area and dispersant action parameters; the simulation parameters at least include the initial positions of each oil particle in the oil spill area; Determining the target entrainment probability of the oil spill area by an entrainment algorithm according to the dispersant action parameter and the simulation parameter; According to the dispersant action parameters and the simulation parameters, the target particle diameter of each oil particle in the oil spill area is determined using an oil droplet diameter formula and a log-normal distribution probability density function; Determining the target position of each oil particle based on the initial position, simulation parameters, target entrainment probability, target particle diameter, and oil spill model; The prediction result of the spread of the oil spill area is determined based on the target position; the prediction result includes the concentration field formed by each oil particle and the target position of each oil particle.

2. The method for predicting oil spill diffusion by realizing dispersant response according to claim 1, characterized in that: The dispersant action parameters include a first oil-water interfacial tension, a first oil dynamic viscosity, a second oil-water interfacial tension, and a second oil dynamic viscosity; the target entrainment probability of the oil spill area is determined by an entrainment algorithm according to the dispersant action parameters and the simulation parameters, including: According to the first oil dynamic viscosity, the first oil-water interfacial tension and simulation parameters, the entrainment formula is adopted: ； The first entrainment probability of oil particles being entrained per second is calculated; in, , , , is the first entrainment probability or the second entrainment probability, Sea surface wave coverage ratio, is the density of seawater, is the acceleration due to gravity, is the effective wave height, is the first oil-water interfacial tension or the second oil-water interfacial tension, is the first oil dynamic viscosity or the second oil dynamic viscosity, is the oil density; , , is a constant; According to the second oil dynamic viscosity, the second oil-water interfacial tension and simulation parameters, the second entrainment probability of the oil spill particles being entrained per second is calculated using the entrainment formula; Substitute the maximum value of the first entrainment probability and the second entrainment probability into the formula: ； Calculate the target entrainment probability of oil particles being entrained in the oil spill area at each time step; in, is the target entrainment probability, is the time step, is the maximum value of the first entrainment probability and the second entrainment probability.

3. The method for predicting oil spill diffusion by realizing dispersant response according to claim 2, characterized in that: The method of determining the target particle diameter of each oil particle in the oil spill area according to the dispersant action parameter and the simulation parameter by using the oil droplet diameter formula and the log-normal distribution probability density function comprises: According to the first oil dynamic viscosity and the first oil-water interfacial tension, the volume mean diameter formula is adopted: ； The mean volume of the first oil drop in the oil spill area is calculated; in, is the mean volume of the first oil droplet or the mean volume of the second oil droplet, , , is a constant; According to the second oil dynamic viscosity and the second oil-water interfacial tension, the second oil droplet volume mean of the oil spill area is calculated using the volume mean diameter formula; determining a minimum value between the first oil droplet volume mean and the second oil droplet volume mean as a target mean diameter; According to the target mean diameter, the lognormal distribution probability density function is used: ； Reducing the particle size of each oil particle in the oil spill area to obtain a target particle diameter of each oil particle in the oil spill area; in, is the natural logarithmic standard deviation, is the target particle diameter, is the target mean diameter.

4. The method for predicting oil spill diffusion by implementing dispersant response according to claim 1, characterized in that: The oil spill model includes a horizontal first component calculation function, a horizontal second component calculation function and a vertical component calculation function; the determination of the target position of each oil particle based on the initial position, simulation parameters, target entrainment probability, target particle diameter and the oil spill model includes: The oil particles in the oil spill area are divided into first oil particles, second oil particles and third oil particles according to the initial position; the first oil particles are oil particles located within a first preset depth range; the second oil particles are oil particles located within a second preset depth range; the third oil particles are oil particles located within a third preset depth range; According to the target particle diameter and simulation parameters, the vertical component calculation function is used, and the vertical component of displacement is obtained by solving the Euler method with second-order accuracy; determining the offset displacement of the first oil particle based on the simulation parameters, the target entrainment probability, the vertical component of the displacement, and the oil spill model; Determining the offset displacement of the second oil particle and the offset displacement of the third oil particle based on the simulation parameters, the vertical component of the displacement and the oil spill model; The target position of each oil particle is determined according to the initial position and the offset displacement of each oil particle.

5. The method for predicting oil spill diffusion by implementing dispersant response according to claim 4, characterized in that: The method of using the vertical component calculation function according to the target particle diameter and the simulation parameters and solving the vertical component of the displacement by the second-order accuracy Euler method includes: Using the formula: ； The vertical component of velocity is calculated; in, , is the vertical component of velocity; is the vertical component of the current velocity, Buoyancy velocity, is the vertical component of turbulent velocity; is the acceleration due to gravity, is the oil density, is the density of seawater, is the target particle diameter, is the critical particle size, is the kinematic viscosity of seawater; The product of the vertical component of velocity and the time step is determined as the vertical component of displacement.

6. The method for predicting oil spill diffusion by implementing dispersant response according to claim 4, characterized in that: The determining of the offset displacement of the first oil particle based on the simulation parameters, the target entrainment probability, the vertical component of the displacement and the oil spill model comprises: Determining entrained particles in the first oil particles according to the target entrainment probability; the number of the entrained particles is the product of the number of the first oil particles and the target entrainment probability; Randomly assigning an entrainment displacement to the entrained particles to obtain an entrainment displacement of the first oil particle; the entrainment displacement is a value between 0 and 1.5 times the breaking wave height, or the entrainment displacement conforms to a Gaussian distribution with 1.5 times the breaking wave height as the mean and 0.35 times the breaking wave height as the standard deviation; the entrainment displacement of particles in the first oil particle other than the entrained particle is 0; The deflection displacement of the first oil particle is determined based on the entrainment displacement and the vertical component of the displacement.

7. The method for predicting oil spill diffusion by implementing dispersant response according to claim 4, characterized in that: Determining the displacement of the second oil particle based on the simulation parameters, the vertical component of the displacement and the oil spill model includes: Using the formula: ； The first component of horizontal velocity is calculated; in, is the first component of horizontal velocity; is the component of the ocean current velocity in the x-axis direction, is the component of the ocean current velocity in the y-axis direction, is the component of wind speed at 10m above the sea surface in the x-axis direction, is the component of wind speed at 10m above the sea surface in the y-axis direction, It is the component of the oil particle drift velocity on the x-axis brought by the residual flow generated by the nonlinear wave; Confirming the product of the first component of the horizontal velocity and the time step as the first component of the horizontal displacement; Using the formula: ； The second component of horizontal velocity is calculated; in, is the second component of horizontal velocity; is the component of the oil particle drift velocity on the y-axis caused by the residual flow generated by the nonlinear wave, is the component of the ocean current velocity in the y-axis direction; Confirming the product of the second component of the horizontal velocity and the time step as the second component of the horizontal displacement; The first component of the horizontal displacement, the second component of the horizontal displacement and the vertical component of the displacement are synthesized to obtain the offset displacement of the second oil particle.

8. The method for predicting oil spill diffusion by implementing dispersant response according to claim 4, characterized in that: Determining the target position of each oil particle according to the initial position and the offset displacement of each oil particle includes: The initial position and the offset displacement of each oil particle are accumulated to obtain the migration position of each oil particle after the first time step; The migration position of each oil particle is taken as the new initial position, and the migration position of each oil particle in the next time step is continued to be determined according to the new initial position, simulation parameters, target entrainment probability, target particle diameter and oil spill model, until the cumulative time reaches the preset time, and the target position of each oil particle is obtained.

9. The method for predicting oil spill diffusion by implementing dispersant response according to claim 1, characterized in that: The prediction result of determining the spread of the oil spill area based on the target location includes: The residence time of each oil particle at a depth of 0.05 m below the sea surface is calculated, and the residence time is substituted into the formula: ； The standard deviation corresponding to each oil particle is calculated; in, for The standard deviation of the oil particle, is the residence time, is the diffusion coefficient; A target volume is assigned to each oil particle at the target position according to the standard deviation to form a concentration field, thereby obtaining a prediction result of the spread of the oil spill area; the transverse section of the target volume is a circle with the standard deviation as the radius, and the longitudinal section of the target volume is an ellipse with 2 times the standard deviation as the minor axis.

10. An oil spill diffusion prediction device for realizing dispersant response, characterized in that: include: A parameter acquisition module is used to acquire simulation parameters of the oil spill area and dispersant action parameters; the simulation parameters at least include the initial position of each oil particle in the oil spill area; A target entrainment probability calculation module, used to determine the target entrainment probability of the oil spill area through an entrainment algorithm according to the dispersant action parameters and the simulation parameters; a target particle diameter calculation module, for determining the target particle diameter of each oil particle in the oil spill area according to the dispersant action parameter and the simulation parameter, using the oil droplet diameter formula and the log-normal distribution probability density function; A target position calculation module, for determining the target position of each oil particle based on the initial position, simulation parameters, target entrainment probability, target particle diameter and oil spill model; The prediction result output module is used to determine the prediction result of the oil spill area diffusion based on the target position; the prediction result includes the concentration field formed by each oil particle and the target position of each oil particle.

Images