An oil spill diffusion prediction method and device for realizing dispersant response

Abstract

The present invention discloses an oil spill diffusion prediction method and device for realizing dispersant response, which relates to the technical field of sea surface oil spill simulation, and aims to solve the problems that the oil spill diffusion simulation of the existing oil spill model is not fine enough and cannot support the dispersant response. The method includes: obtaining the simulation parameters of the oil spill area and the dispersant action parameters; determining the target entrainment probability of the oil spill 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 spill area by using an oil droplet diameter formula and a lognormal distribution probability density function according to the dispersant action parameters and the simulation parameters; determining the target position of each oil particle based on the initial position, the simulation parameters, the target entrainment probability, the target particle diameter and the oil spill model; determining the prediction result of the oil spill area diffusion based on the target position; the oil spill diffusion prediction method provided by the present invention can realize the prediction of oil spill diffusion under the response of the dispersant and improve the accuracy of the prediction result.

Description

Technical Field

[0001] The present invention relates to the technical field of simulation of oil spills on the sea surface, and particularly to a method and device for predicting oil spill diffusion that realizes the response of dispersants. Background Art

[0002] In marine oil spill accidents, dispersants can accelerate the dispersion of oil spills on the sea surface, forming tiny oil droplets distributed in the water body. Then, the natural degradation process is utilized to accelerate the removal of oil pollution. By using an oil spill model to simulate and predict oil spill events, the dispersion effect of oil pollution and the recovery of the environment can be understood, so as to adjust emergency response measures in a timely manner.

[0003] In the actual marine environment, breaking waves and turbulence on the sea will disperse the oil spill into the sea water. The floating oil on the sea surface is brought to a certain depth and presented in the form of a group of oil droplets, and each oil droplet has its own size; and over time, due to weathering, the viscosity of the oil spill continuously increases, and the effect of the dispersant may become 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 default that the mean value of the oil droplet size remains unchanged in the oil spill simulation, and at the same time, the situation of dispersant response is not considered in the simulation process. Therefore, the existing oil spill models are relatively simple and not fine enough in simulating the real oil spill process, and the accuracy of the predicted oil spill diffusion results is low. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and device for predicting oil spill diffusion that realizes the response of dispersants, which are used to simulate the process of oil spill entrainment by waves, the process of oil droplet breakage and aggregation in the sea, the process of viscosity increase due to weathering affecting the entrainment rate and oil droplet size, and the phenomenon of increased horizontal diffusivity when the proportion of oil spill entering the water is high, so as to improve the accuracy of the predicted oil spill diffusion results.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] In the first aspect, the present invention provides a method for predicting oil spill diffusion that realizes the response of dispersants, including:

[0007] Obtain the simulation parameters of the oil spill area and the dispersant action parameters; the simulation parameters at least include the initial positions of each oil particle in the oil spill area;

[0008] According to the dispersant action parameters and the simulation parameters, determine the target entrainment probability of the oil spill area through an entrainment algorithm;

[0009] According to the dispersant action parameters and the simulation parameters, use the oil droplet diameter formula and the lognormal distribution probability density function to determine the target particle diameter of each oil particle in the oil spill area;

[0010] Determine the target positions of each oil particle based on the initial position, simulation parameters, target entrainment probability, target particle diameter, and oil spill model;

[0011] Determine the prediction result of the spread of the oil spill area based on the target positions; the prediction result includes the concentration field formed by each oil particle and the target position of each oil particle.

[0012] Optionally, the dispersant action parameters include the first oil-water interfacial tension, the first oil dynamic viscosity, the second oil-water interfacial tension, and the second oil dynamic viscosity; the determining of the target entrainment probability of the oil spill area by the entrainment algorithm according to the dispersant action parameters and the simulation parameters includes:

[0013] According to the first oil dynamic viscosity, the first oil-water interfacial tension, and the simulation parameters, use the entrainment formula:

[0014] ;

[0015] Calculate the first entrainment probability of the oil particle being entrained per second;

[0016] Wherein, , , d o = 4 × [ IFT ( ρ w − ρ o ) × g ] 0 . 5 , is the first entrainment probability or the second entrainment probability, sea surface breaking wave coverage ratio, is the seawater density, is the gravitational acceleration, is the significant 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;

[0017] According to the second oil dynamic viscosity, the second oil-water interfacial tension, and the simulation parameters, use the entrainment formula to calculate the second entrainment probability of the oil spill particle being entrained per second;

[0018] Substitute the maximum value of the first entrainment probability and the second entrainment probability into the formula:

[0019] ;

[0020] Calculate the target entrainment probability of the oil particle in the oil spill area being entrained per time step;

[0021] Wherein, is the target entrainment probability, is the time step.

[0022] Optionally, determining the target particle diameter of each oil particle in the oil spill area according to the dispersant action parameter and the simulation parameter through the oil droplet size formula and the lognormal distribution probability density function includes:

[0023] According to the first oil dynamic viscosity and the first oil-water interfacial tension, using the volume mean diameter formula:

[0024] ;

[0025] Calculating the first oil droplet volume mean value of the oil spill area;

[0026] Wherein, is the first oil droplet volume mean value or the second oil droplet volume mean value, , , are constants;

[0027] According to the second oil dynamic viscosity and the second oil-water interfacial tension, using the volume mean diameter formula, calculating the second oil droplet volume mean value of the oil spill area;

[0028] Determining the minimum value of the first oil droplet volume mean value and the second oil droplet volume mean value as the target mean diameter;

[0029] According to the target mean diameter, using the lognormal distribution probability density function:

[0030] ;

[0031] Restoring the particle size of each oil particle in the oil spill area to obtain the target particle diameter of each oil particle in the oil spill area;

[0032] Wherein, is the natural logarithm standard deviation, is the target particle diameter, is the target mean diameter.

[0033] Optionally, the oil spill model includes a horizontal first component calculation function, a horizontal second component calculation function, and a vertical component calculation function; 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:

[0034] Divide 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 the oil particles located within the first preset depth range; the second oil particles are the oil particles located within the second preset depth range; the third oil particles are the oil particles located within the third preset depth range;

[0035] According to the target particle diameter and simulation parameters, use the vertical component calculation function and solve for the vertical component of displacement through the second-order accurate Euler method;

[0036] Based on the simulation parameters, target entrainment probability, vertical component of displacement, and the oil spill model, determine the offset displacement of the first oil particles;

[0037] Based on the simulation parameters, vertical component of displacement, and the oil spill model, determine the offset displacements of the second oil particles and the third oil particles;

[0038] Determine the target positions of the oil particles according to the initial position and the offset displacements of each oil particle.

[0039] Optionally, the step of "According to the target particle diameter and simulation parameters, use the vertical component calculation function and solve for the vertical component of displacement through the second-order accurate Euler method" includes:

[0040] Use the formula:

[0041] ;

[0042] Calculate the vertical component of velocity;

[0043] where, , is the vertical component of velocity; is the vertical component of the ocean current velocity, is the velocity due to buoyancy, is the vertical component of the turbulent velocity; is the acceleration due to gravity, is the oil density, is the seawater density, is the target particle diameter, is the critical particle size, is the kinematic viscosity of seawater;

[0044] Determine the vertical component of displacement by multiplying the vertical component of velocity by the time step.

[0045] Optionally, the step of "Based on the simulation parameters, target entrainment probability, vertical component of displacement, and the oil spill model, determine the offset displacement of the first oil particles" includes:

[0046] Determine 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;

[0047] Randomly assign an entrainment displacement to the entrained particles to obtain the entrainment displacement of the first oil particles; the entrainment displacement is a value within 0 - 1.5 times the breaking wave height, or the entrainment displacement conforms to a Gaussian distribution with a mean of 1.5 times the breaking wave height and a standard deviation of 0.35 times the breaking wave height; the entrainment displacement of the particles other than the entrained particles in the first oil particles is 0;

[0048] Determine the offset displacement of the first oil particles based on the entrainment displacement and the vertical component of the displacement.

[0049] Optionally, determining the offset displacement of the second oil particles based on the simulation parameters, the vertical component of the displacement, and the oil spill model includes:

[0050] Use the formula:

[0051] ;

[0052] Calculate the first component of the horizontal velocity;

[0053] where, is the first component of the 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 the wind speed at 10 m above the sea surface in the x-axis direction, is the component of the wind speed at 10 m above the sea surface in the y-axis direction, is the component of the oil particle drift velocity caused by the wave residual current generated by the nonlinear wave in the x-axis;

[0054] Confirm the first component of the horizontal displacement as the product of the first component of the horizontal velocity and the time step;

[0055] Use the formula:

[0056] ;

[0057] Calculate the second component of the horizontal velocity;

[0058] where, is the second component of the horizontal velocity; is the component of the oil particle drift velocity caused by the wave residual current generated by the nonlinear wave in the y-axis, is the component of the ocean current velocity in the y-axis direction;

[0059] Confirm the second component of the horizontal displacement as the product of the second component of the horizontal velocity and the time step;

[0060] The first horizontal displacement component, the second horizontal displacement component, and the vertical displacement component are combined to obtain the offset displacement of the second oil particle.

[0061] Optionally, the determining the target position of each oil particle according to the initial position and the offset displacement of each oil particle includes:

[0062] 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;

[0063] The migration position of each oil particle is used as the new initial position, and according to the new initial position, simulation parameters, target entrainment probability, target particle diameter, and oil spill model, the migration position of each oil particle at the next time step is continuously determined until the cumulative time reaches the preset time, and the target position of each oil particle is obtained.

[0064] Optionally, the determining the prediction result of the oil spill area diffusion based on the target position includes:

[0065] The residence time of each oil particle at a depth below 0.05 m of the sea surface is counted, and the residence time is substituted into the formula:

[0066] ;

[0067] The standard deviation corresponding to each oil particle is calculated;

[0068] Wherein, is the standard deviation of the i-th oil particle, is the residence time, is the diffusion coefficient;

[0069] According to the standard deviation, a target volume is assigned to each oil particle at the target position to form a concentration field, and the prediction result of the oil spill area diffusion is obtained; the lateral profile of the target volume is a circle with the standard deviation as the radius, and the longitudinal profile of the target volume is an ellipse with 2 times the standard deviation as the minor axis.

[0070] Compared with the prior art, an oil spill diffusion prediction method for realizing dispersant response provided by the present invention includes obtaining simulation parameters of the oil spill area and dispersant action parameters; according to the dispersant action parameters and the simulation parameters, determining the target entrainment probability of the oil spill area through an entrainment algorithm, which can simulate the situation where waves entrain particles on the sea surface into the sea; using the oil droplet size formula and the logarithmic normal distribution probability density function to obtain the target particle diameter of each oil particle in the oil spill area; based on the initial position, simulation parameters, target entrainment probability, target particle diameter, and oil spill model, determining the target position of each oil particle; and determining the prediction result of the oil spill area diffusion based on the target position. The present invention can simulate the oil spill diffusion after adding a dispersant, taking into account both the wave entrainment rate problem and the influence of wave entrainment on the diffusion depth of oil particles, as well as the change in the oil particle diameter caused by the breakup and aggregation of oil particles in the sea. Based on this, the target positions of each oil particle are more accurate, and thus the obtained oil spill diffusion prediction result is more accurate.

[0071] In a second aspect, the present invention further provides an oil spill diffusion prediction device for realizing dispersant response, including:

[0072] A parameter acquisition module for obtaining 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;

[0073] A target entrainment probability calculation module for determining the target entrainment probability of the oil spill area through an entrainment algorithm according to the dispersant action parameters and the simulation parameters;

[0074] A target particle diameter calculation module for determining the target particle diameter of each oil particle in the oil spill area by using the oil droplet size formula and the logarithmic normal distribution probability density function according to the dispersant action parameters and the simulation parameters;

[0075] 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;

[0076] A prediction result output module for determining 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.

[0077] Compared with the prior art, the beneficial effects of the oil spill diffusion prediction device for realizing dispersant response provided by the present invention are the same as those of the oil spill diffusion prediction method for dispersant response described in the above technical solution, and will not be elaborated here. Description of the Drawings

[0078] The accompanying drawings described herein are used to provide a further understanding of the present invention and form a part of the present invention. The schematic 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:

[0079] Figure 1 It is a flowchart of a method for predicting the spread of oil spills that realizes the response of a dispersant provided by the present invention;

[0080] Figure 2 It is a schematic diagram of model calculation after the use of a dispersant provided by the present invention;

[0081] Figure 3 It is a schematic diagram of the principle of weathering effect provided by the present invention;

[0082] Figure 4 It is a schematic diagram of the influence of dispersants and weathering on wave entrainment and oil droplet size provided by the present invention;

[0083] Figure 5 It is a schematic diagram of the principle of oil spill diffusion provided by the present invention;

[0084] Figure 6 It is a comparison chart of the prediction results, experimental results and results of the OSCAR model provided by the present invention;

[0085] Figure 7 It is a schematic structural diagram of a device for predicting the spread of oil spills that realizes the response of a dispersant provided by the present invention. Detailed implementation manners

[0086] In order to facilitate a clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish identical 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 do not limit their sequence. Those skilled in the art can understand that terms such as "first" and "second" do not limit the quantity and execution order, and terms such as "first" and "second" do not necessarily limit being different.

[0087] It should be noted that in the present invention, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "exemplary" or "for example" aims to present relevant concepts in a specific manner.

[0088] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the relationship between associated objects and indicates that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one (item)" or a similar expression refers to any combination of these items, including any combination of a single item or plural items. For example, at least one (item) of a, b, or c can represent: 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, and c can be single or multiple.

[0089] The simulation of the spread of spilled oil after the actual addition of a dispersant mainly has five aspects. First, the entrainment simulation of waves. Second, the simulation of the oil droplet size distribution spectrum. Third, the reduction of the oil-water interfacial tension by the dispersant. Fourth, the simulation of the process in which weathering causes the viscosity to continuously increase and affects the entrainment rate and the size of oil droplets. Fifth, the increase in horizontal diffusivity caused by the increase in the number of oil droplets entering the water after the addition of the dispersant. Existing oil spill models consider vertical turbulence but do not consider wave entrainment, and adopt a uniform distribution of 1 - 1000 μm for the oil droplet size. Therefore, the diffusion simulation of existing oil spill models is not fine enough to support the response of the dispersant, resulting in a low accuracy of the prediction results of the spread of spilled oil.

[0090] To solve the above problems, the present invention provides an oil spill diffusion prediction method and device that can achieve the response of the dispersant. Based on the original oil spill model, a wave entrainment algorithm and an oil droplet size algorithm are written, the horizontal diffusion setting is modified, and particle cloud processing is added to the visualization analysis module. The present invention is mainly applied to calculate the displacement of particles. First, a particle loop is performed to judge each particle to determine whether the particle is at the bottom of the sea or outside the calculated longitude and latitude boundary. If so, this particle is skipped. If not, wind and current data are inserted to calculate the displacement of the particle. After the displacement calculation of all particles is completed, the particle position state is updated to perform the displacement calculation of particles in the next time loop. Next, it will be described in conjunction with the accompanying drawings.

[0091] See Figure 1 , an oil spill diffusion prediction method that can achieve the response of the dispersant provided by the present invention includes the following steps:

[0092] Step S1: Obtain the simulation parameters of the oil spill area and the action parameters of the dispersant;

[0093] The simulation parameters include the initial positions of each oil particle in the oil spill area, environmental field data, and wind and current field data; the environmental field data includes: the coverage ratio of breaking waves on the sea surface, seawater density, significant wave height, oil density, sea current velocity, the drift velocity of oil particles brought by the wave residual current generated by nonlinear waves, turbulent velocity, kinematic viscosity of seawater, wave number, wave period, and breaking wave height; the wind and current field data includes the wind speed at 10 m above the sea surface, wind drift factor, and deflection angle. The dispersant action parameters include the first oil-water interfacial tension and the first oil dynamic viscosity before adding the dispersant, the second oil-water interfacial tension after adding the dispersant, and the second oil dynamic viscosity during the action of the dispersant.

[0094] Step S2: According to the dispersant action parameters and the simulation parameters, determine the target entrainment probability of the oil spill area through the entrainment algorithm;

[0095] Step S3: According to the dispersant action parameters and the simulation parameters, use the oil droplet diameter formula and the lognormal distribution probability density function to determine the target particle diameter of each oil particle in the oil spill area;

[0096] Step S4: Based on the initial position, simulation parameters, target entrainment probability, target particle diameter, and oil spill model, determine the target position of each oil particle;

[0097] The initial position and the target position of the oil particle 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 surface, and the z-axis direction is the seawater depth direction. The initial position is represented by (x1, y1, z1).

[0098] Step S5: Based on the target position, determine the prediction result of the spread of the oil spill area; the prediction result includes the concentration field formed by each oil particle and the target position of each oil particle.

[0099] The prediction result is displayed in a visual way.

[0100] As can be seen from the above method, the present invention determines the target entrainment probability of the oil spill area through the entrainment algorithm, which can simulate the situation where waves entrain particles on the sea surface into the sea. Using the oil droplet diameter formula and the lognormal distribution probability density function, the target particle diameter of each oil particle in the oil spill area can be obtained. The present invention can more accurately simulate the three-dimensional migration of the oil spill after adding the dispersant, taking into account both the wave entrainment rate problem and the influence of wave entrainment on the diffusion depth of oil particles, as well as the change in the diameter of oil particles caused by the breakage and aggregation of oil particles in the sea. Based on this, the target positions of each oil particle are more accurate, and thus the oil spill diffusion prediction result obtained is more accurate.

[0101] Based on the above method, the present invention provides some specific embodiments, which will be described in detail below.

[0102] As Figure 2 shown, the above step S2 can be implemented based on the following steps:

[0103] According to the first oil dynamic viscosity, the first oil-water interfacial tension, and the simulation parameters, the entrainment formula (1) is adopted:

[0104] (1)

[0105] Calculate the first entrainment probability of oil particles entrained per second;

[0106] Wherein, , , d o = 4 × [ IFT ( ρ w − ρ o ) × g ] 0 . 5 , , is the dispersant-oil ratio, is the first entrainment probability or the second entrainment probability, is the proportion of sea surface breaking waves covered, is the seawater density, is the acceleration of gravity, is the significant 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.

[0107] According to the second oil dynamic viscosity, the second oil-water interfacial tension, and the simulation parameters, the entrainment formula of formula (1) is adopted to calculate the second entrainment probability of the oil spill particles entrained per second;

[0108] In the actual process of applying to the model, it is impossible to set the time step of the model to 1 s, so it is necessary to convert the entrainment probability per second into the entrainment probability per time step. The specific steps are as follows:

[0109] Substitute the maximum value of the first entrainment probability and the second entrainment probability into formula (2):

[0110] (2)

[0111] Calculate the target entrainment probability of each oil particle in the oil spill area at each time step;

[0112] Wherein, is the target entrainment probability, is the time step, is the maximum value of the first entrainment probability and the second entrainment probability.

[0113] Considering that the dispersant may be ineffective, the maximum value of the first entrainment probability and the second entrainment probability is used as the target entrainment probability.

[0114] The existing oil droplet model ignores the breakup and aggregation processes of oil droplets in the sea. It assumes that the size of oil droplets in water remains unchanged. When a large amount of floating oil enters the water, the distribution spectrum predicted by the existing oil droplet model will result in a lower probability of oil droplets floating to the surface than the actual value.

[0115] In view of this, the breakup and aggregation processes of oil droplets in the sea should be considered. When the oil spill on the sea surface is carried below the sea surface, it will appear as a group of oil droplets, each with its own size. Therefore, step S3 is implemented as follows:

[0116] As Figure 2 shown, according to the first oil dynamic viscosity and the first oil-water interfacial tension, the volume mean diameter formula (3) is used:

[0117] (3)

[0118] Calculate the first oil droplet volume mean of the oil spill area;

[0119] Wherein, is the first oil droplet volume mean or the second oil droplet volume mean, , , are constants; is 1.791, is 0.460, is -0.518. number, The number calculation is the same as that in formula (1).

[0120] According to the second oil dynamic viscosity and the second oil-water interfacial tension, use the volume mean diameter formula of formula (3) to calculate the second oil droplet volume mean of the oil spill area;

[0121] Determine the minimum value of the first oil droplet volume mean and the second oil droplet volume mean as the target mean diameter;

[0122] Considering that the dispersant may be ineffective, the minimum value of the first oil droplet volume mean and the second oil droplet volume mean is taken as the target mean diameter.

[0123] The size distribution of the oil particles better conforms to the log-normal distribution. The entire distribution spectrum of the oil particle diameters is restored through the target mean diameter and the standard deviation, as follows:

[0124] According to the target mean diameter, the log-normal distribution probability density function is adopted, as shown in formula (4):

[0125] (4)

[0126] Restore the particle diameters of each oil particle in the oil spill area to obtain the target particle diameters of each oil particle in the oil spill area;

[0127] Among them, is the natural logarithm standard deviation, is the target particle diameter, is the target mean diameter.

[0128] The range of the above natural logarithm standard deviation is between 0.5 and 0.921. Exemplarily, the natural logarithm standard deviation takes values such as 0.5, 0.921, 0.38×ln(10), etc. After our optimization, the result predicted when the standard deviation takes 0.921 is the most accurate. The larger the standard deviation, the flatter and more dispersed the probability density function image will be, the probability of randomly distributing the particle size to the intermediate value will decrease, and the probability of randomly distributing to large or small particle sizes will increase. Therefore, in practical applications, it is necessary to determine the appropriate value of the logarithmic standard deviation according to the measured data.

[0129] Set the diameter of the oil droplet to be re-assigned at each time step in the mixing layer. The depth of the mixing layer adopts the suggestion of (Sweeney 1988) (0~zi), where zi = 1.5 times the breaking wave height. And in the upper half of the mixing layer, the volume mean diameter is still affected by the wind speed, and the volume mean diameter will be fixed in the lower half of the mixing layer; in other words, in the upper half of the mixing layer, the randomness of the oil droplet diameter is restored by the continuously changing volume mean diameter, and the lower half is restored by the fixed volume mean diameter, which will reflect that the influence depth of the wind speed is continuously changing, and the target particle diameter will be fixed below the mixing layer.

[0130] By restoring the particle diameters of each oil particle through the above method, the obtained distribution spectrum of the oil particle diameters is closer to the true value.

[0131] See Figure 3 and Figure 4, the effects of adding dispersants at different times are different. At the initial stage of oil spill, due to the relatively low viscosity of the oil, the dispersing effect is good after adding the dispersant. However, as time goes by, under the action of weathering, the viscosity of the oil increases, and the dispersing effect produced after adding the dispersant will decrease. When the viscosity of the oil reaches a certain level, adding the dispersant will lose its dispersing effect. Moreover, the effects of effective and ineffective dispersants on the entrainment process and the size of oil droplet diameters are different. As Figure 3 shown, when the dispersant is effective, the interfacial tension between oil and water decreases, and more oil will be entrained by the waves, and the oil will be dispersed into smaller oil droplets. As the viscosity of the oil increases, the dispersant gradually becomes ineffective, and the theoretically calculated amount of oil entrained by the waves will decrease, and the diameter of the oil droplets will also increase, which 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 and the diameter of the oil droplets increases after adding the dispersant, it indicates that the dispersant has become ineffective. When calculating the entrainment probability and the target particle diameter in the above steps, before the action of the dispersant, the viscosity used is the first oil dynamic viscosity and the first interfacial tension between oil and water; after the action of the dispersant, if the predicted average diameter of the oil particles is smaller with the reduced interfacial tension and increased viscosity, it indicates that the dispersant is effective, and if it is larger, it indicates that it is ineffective. The minimum value is used as the target average diameter for calculating the target particle diameter; for the entrainment probability, if the predicted entrainment probability is larger, it is effective, otherwise it is ineffective, and the maximum value is used for calculating the target entrainment probability. Considering the possible failure of the simulated dispersant, the calculated target entrainment probability and target particle diameter are more accurate.

[0132] The oil spill model in the above step S4 is shown in formula (5):

[0133] (5)

[0134] Among them, , , is the first component of the 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 particle, 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 the 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 velocity under the action of buoyancy, is the acceleration due to gravity, is the oil density, is the sea water density, is the target particle diameter, is the critical particle size, is the kinematic viscosity of seawater, with a value of 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 the turbulent velocity in the z-axis direction; is the component of the turbulent velocity in the x-axis direction, is the component of the turbulent velocity in the y-axis direction, is the component of the wind speed at 10 m above the sea surface in the x-axis direction, is the component of the wind speed at 10 m above the sea surface in the y-axis direction, is the component of the drift velocity of the oil particle caused by the wave residual current generated by the nonlinear wave in the x-axis, is the component of the drift velocity of the oil particle caused by the wave residual current generated by the nonlinear wave in the y-axis, is the wind drift factor, with a value of 0.03, is the deflection angle, with a value of 0.

[0135] Using the Euler method with second-order accuracy to solve the equation in formula (5), the velocity of the oil particle can be obtained. Multiplying the velocity of the oil particle by the time step can get the displacement of the oil particle in one time step. Adding the displacement to the initial position can obtain the migration position of the oil particle in the next time step. After looping through all particles, using the newly obtained migration position as the initial position to calculate the migration position of the oil particle in the next time step until the cumulative time reaches the output time, and then outputting the position of the oil particle. Exemplarily, the time step is set to 10 minutes, and the position of the oil particle 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 at the simulation time of 0. Then the result is output once every hour until the model terminates at the simulation time of 2 days.

[0136] The displacement is the displacement of each oil particle moving in one time step. The component of the displacement of each oil particle in the z-axis direction is the vertical displacement component, the component in the x-axis direction is the first horizontal displacement component, and the component in the y-axis direction is the second horizontal displacement component.

[0137] Based on the oil spill model in formula (5), the above step S4 can be specifically implemented through the following steps:

[0138] Step S41: Divide 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 the oil particles located within the first preset depth range; the second oil particles are the oil particles located within the second preset depth range; the third oil particles are the oil particles located within the third preset depth range;

[0139] The action depth of the dispersant and the horizontal drag by wind and water is within 1 m, and the action depth of the wave entrainment is within 0.05 m. Therefore, the first preset depth range is 0 - 0.05 m, the second preset depth range is 0.05 - 1 m, and the third preset depth is 1 m to the seabed. As Figure 5 shown, the depth of the mixed layer is 1.5 times the breaking wave height. Within the mixed layer, the wave entrainment affects the oil particles within 0.05 m; the oil particles within 1 m are affected by the horizontal drag by wind and water and the dispersant; the oil particles below 1 m will not be affected by the horizontal drag by wind and water.

[0140] Step S42: According to the target particle diameter and the simulation parameters, use the vertical component calculation function and solve for the vertical displacement component by the Euler method of second-order accuracy;

[0141] Specifically, step S42 includes: using the vertical component calculation function in formula (5):

[0142] ;

[0143] Calculate the vertical velocity component;

[0144] Determine the vertical displacement component by multiplying the vertical velocity component by the time step.

[0145] Step S43: Determine the offset displacement of the first oil particles based on the simulation parameters, the target entrainment probability, the vertical displacement component, and the oil spill model;

[0146] Specifically, step S43 includes the following steps:

[0147] Step S431: Use the horizontal first component calculation function in formula (5):

[0148] ;

[0149] Calculate the horizontal velocity first component;

[0150] Step S432: Confirm the horizontal displacement first component by multiplying the horizontal velocity first component by the time step;

[0151] Step S433: Use the horizontal second component calculation function in formula (5):

[0152] ;

[0153] The second component of the horizontal velocity is calculated;

[0154] Step S434: Confirm the second component of the horizontal displacement as the product of the second component of the horizontal velocity and the time step;

[0155] Step S435: Determine 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;

[0156] Step S436: Randomly assign an entrainment displacement to the entrained particles to obtain the entrainment displacement of the first oil particles; the entrainment displacement is a value within 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 other than the entrained particles in the first oil particles is 0; After our optimization, the result of using the entrainment displacement as a value within 0 - 1.5 times the breaking wave height is the most accurate.

[0157] Step S436: Determine the offset displacement of the first oil particles based on the entrainment 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 entrainment 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 particles; the new vertical component of the displacement of the first oil particles, 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 particles.

[0158] Step S43: Determine the offset displacement of the second oil particles and the offset displacement of the third oil particles based on the simulation parameters, the vertical component of the displacement, and the oil spill model;

[0159] The calculation steps for the offset displacement of the second oil particles are as follows:

[0160] The offset displacement of the second oil particles is synthesized by combining 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.

[0161] The calculation steps for the offset displacement of the third oil particles are as follows:

[0162] Since the third oil particles are located below 1 m and are not affected by the wind speed, so let = 0, at this time, the first component of the horizontal velocity of the third oil particles is obtained according to the first component of the horizontal velocity calculation function, and the product of the first component of the horizontal velocity of the third oil particles and the time step is the first component of the horizontal displacement of the third oil particles;

[0163] Let = 0, at this time, the second horizontal velocity component of the third oil particle is obtained according to the horizontal second component calculation function, and the second horizontal displacement component of the third oil particle is obtained by multiplying the second horizontal velocity component of the third oil particle by the time step;

[0164] The first horizontal displacement component, the second horizontal displacement component of the third oil particle and the vertical displacement component obtained in step S42 are synthesized to obtain the offset displacement of the third oil particle.

[0165] Step S44: Determine the target positions of the oil particles according to the initial positions and the offset displacements of the oil particles.

[0166] Specifically, the initial positions are accumulated with the offset displacements of the oil particles to obtain the migration positions of the oil particles after the first time step;

[0167] The migration positions of the oil particles are used as new initial positions, and according to the new initial positions, simulation parameters, target entrainment probability, target particle diameter and oil spill model, the migration positions of the oil particles at the next time step are continuously determined until the cumulative time reaches the preset time, so as to obtain the target positions of the oil particles.

[0168] During the cyclic iteration process, when there is no dispersant action, only the first entrainment probability and the first oil droplet volume mean are calculated. Each time the dispersant acts, the first entrainment probability and the second entrainment probability, the first oil droplet volume mean and the second oil droplet volume mean will be calculated. After judging that the dispersant is effective, only the second entrainment probability and the second oil droplet volume mean will be calculated in the subsequent time. If the dispersant is ineffective, then only the first entrainment probability and the first oil droplet volume mean will be calculated in the subsequent time.

[0169] As an optional method, the above step S5 can be specifically implemented through the following steps:

[0170] By statistically calculating the residence time of each oil particle at a depth below 0.05 m of the sea surface, substituting the residence time into the formula:

[0171] ;

[0172] The standard deviation corresponding to each oil particle is calculated;

[0173] Among them, is the standard deviation of the \(i\)-th oil particle, is the residence time, are the diffusion coefficients in each direction; the diffusion coefficients include the horizontal diffusion coefficient and the vertical diffusion coefficient. Within 0.05 m of the sea surface, 1 / 100 times of the horizontal diffusion coefficient is used, and the horizontal diffusion coefficient at the remaining depths adopts the normal value.

[0174] Assign a target volume to each oil particle at the target position according to the standard deviation to form a concentration field, and obtain the prediction result of the spread of the oil spill area; the lateral 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.

[0175] In the above method, with the standard deviation as the radius, the mass of each oil particle is dispersed horizontally within the circle, and the superposition of many such particle clouds forms a new concentration field. Vertically, with 2 times the standard deviation as the axis of the ellipse, the mass of the oil particles is dispersed within the ellipse. The circle encountering the boundary will be truncated, so that the mass is distributed within 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, predicting the oil spill diffusion by this method can greatly shorten the calculation time.

[0176] After model verification and optimization, the prediction result obtained by using the entrainment simulation method with uniform probability and taking the logarithmic standard deviation of 0.921 is the most accurate, and its relative error is 0.09347.

[0177] See Figure 6 , the present invention uses experimental data for verification. The method of the present invention can well show the universality law of the 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. As Figure 6 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-oil ratios using 4 kinds of oils and the results of underwater oil accounting for the total oil predicted by two models.

[0178] In the case of dividing each functional module according to the corresponding functions, Figure 7 shows a schematic structural diagram of an oil spill diffusion prediction device for realizing dispersant response provided by the present invention. As Figure 7 shown, the device includes:

[0179] A parameter acquisition module 701, configured to 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;

[0180] A target entrainment probability calculation module 702, configured 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;

[0181] The target particle diameter calculation module 703 is configured 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 size formula and the lognormal distribution probability density function;

[0182] The target position calculation module 704 is configured to determine the target position of each oil particle based on the initial position, the simulation parameter, the target entrainment probability, the target particle diameter, and the oil spill model;

[0183] The prediction result output module 705 is configured 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.

[0184] Optionally, the dispersant action parameter includes the first oil-water interfacial tension, the first oil dynamic viscosity, the second oil-water interfacial tension, and the second oil dynamic viscosity; the target entrainment probability calculation module 702 may include:

[0185] The first entrainment probability calculation unit is configured to calculate the first entrainment probability of the oil particle being entrained per second according to the first oil dynamic viscosity, the first oil-water interfacial tension, and the simulation parameter by using the entrainment formula:

[0186] ;

[0187] The calculated first entrainment probability of the oil particle being entrained per second;

[0188] wherein, , , d o = 4 × [ IFT ( ρ w − ρ o ) × g ] 0 . 5 , is the first entrainment probability or the second entrainment probability, the proportion of the sea surface breaking wave coverage, is the sea water density, is the acceleration of gravity, is the significant 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;

[0189] The second entrainment probability calculation unit is configured to calculate the second entrainment probability of the spilled oil particle being entrained per second according to the second oil dynamic viscosity, the second oil-water interfacial tension, and the simulation parameter by using the entrainment formula;

[0190] The target entrainment probability calculation unit is configured to substitute the maximum value of the first entrainment probability and the second entrainment probability into the formula:

[0191] ;

[0192] Calculate the target entrainment probability of each oil particle in the oil spill area being entrained at each time step;

[0193] Wherein, is the target entrainment probability, is the time step, is the maximum value of the first entrainment probability and the second entrainment probability.

[0194] Optionally, the target particle diameter calculation module 703 may include:

[0195] The first oil droplet volume mean calculation unit is used to calculate the first oil droplet volume mean in the oil spill area according to the first oil dynamic viscosity and the first oil-water interfacial tension by using the volume mean diameter formula:

[0196] ;

[0197] Calculate the first oil droplet volume mean in the oil spill area;

[0198] Wherein: is the first oil droplet volume mean or the second oil droplet volume mean, , , are constants;

[0199] The second oil droplet volume mean calculation unit is used to calculate the second oil droplet volume mean in 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;

[0200] The target mean diameter determination unit is used to determine the minimum value of the first oil droplet volume mean and the second oil droplet volume mean as the target mean diameter;

[0201] The target particle diameter calculation unit is used to restore the particle sizes of the oil particles in the oil spill area according to the target mean diameter by using the lognormal distribution probability density function:

[0202] ;

[0203] Restore the particle diameters of the oil particles in the oil spill area to obtain the target particle diameters of the oil particles in the oil spill area;

[0204] Wherein, is the natural logarithm standard deviation, is the target particle diameter, is the target mean diameter.

[0205] 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:

[0206] A particle division unit, configured to divide 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 the oil particles located within a first preset depth range; the second oil particles are the oil particles located within a second preset depth range; the third oil particles are the oil particles located within a third preset depth range;

[0207] A displacement vertical component calculation unit, configured to use the vertical component calculation function according to the target particle diameter and simulation parameters, and solve for the displacement vertical component by means of the second-order accurate Euler method;

[0208] An offset displacement calculation unit for the first oil particles, configured to determine the offset displacement of the first oil particles based on the simulation parameters, the target entrainment probability, the displacement vertical component, and the oil spill model;

[0209] An offset displacement calculation unit for the second oil particles and the third oil particles, configured to determine the offset displacement of the second oil particles and the offset displacement of the third oil particles based on the simulation parameters, the displacement vertical component, and the oil spill model;

[0210] A loop iteration unit, configured to determine the target positions of the oil particles according to the initial position and the offset displacements of the oil particles.

[0211] Optionally, the displacement vertical component calculation unit may specifically be configured to:

[0212] Use the formula:

[0213] ;

[0214] Calculate the vertical component of velocity;

[0215] where , is the vertical component of velocity; is the vertical component of the ocean current velocity, the buoyancy velocity, is the vertical component of the turbulent velocity; is the buoyancy velocity, is the gravitational acceleration, is the oil density, is the seawater density, is the target particle diameter, is the critical particle size, is the kinematic viscosity of seawater;

[0216] Determine the product of the vertical component of the velocity and the time step as the vertical component of the displacement.

[0217] Optionally, the offset displacement calculation unit of the first oil particle may specifically be used for:

[0218] Determine the entrained particles in the first oil particle 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;

[0219] Randomly assign entrainment displacements to the entrained particles to obtain the entrainment displacement of the first oil particle; the entrainment displacement is a value within 0 - 1.5 times the breaking wave height, or the entrainment displacement conforms to a Gaussian distribution with a mean of 1.5 times the breaking wave height and a standard deviation of 0.35 times the breaking wave height; the entrainment displacement of the particles other than the entrained particles in the first oil particle is 0; determine the offset displacement of the first oil particle based on the entrainment displacement and the vertical component of the displacement.

[0220] Optionally, the offset calculation unit of the second oil particle may be used for:

[0221] Determining the offset displacement of the second oil particle based on the simulation parameters, the vertical component of the displacement, and the oil spill model includes:

[0222] Using the formula:

[0223] ;

[0224] Calculate the first horizontal velocity component;

[0225] Wherein, is the first horizontal velocity component; 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 wind speed at 10 m above the sea surface in the x-axis direction, is the component of the wind speed at 10 m above the sea surface in the y-axis direction, is the component of the oil particle drift velocity caused by the wave residual current generated by the nonlinear wave in the x-axis;

[0226] Confirm the product of the first horizontal velocity component and the time step as the first horizontal displacement component;

[0227] Using the formula:

[0228] ;

[0229] Calculate the second horizontal velocity component;

[0230] Wherein, is the second horizontal velocity component; The component of the oil particle drift velocity caused by the wave residual current generated by the nonlinear wave on the y-axis, is the component of the sea current velocity in the y-axis direction;

[0231] Confirm the product of the second component of the horizontal velocity and the time step as the second component of the horizontal displacement;

[0232] Synthesize the first component of the horizontal displacement, the second component of the horizontal displacement, and the vertical displacement component to obtain the offset displacement of the second oil particle.

[0233] Optionally, the loop iteration unit can be used for:

[0234] Accumulate the initial position and the offset displacement of each oil particle to obtain the migration position of each oil particle after the first time step;

[0235] Take the migration position of each oil particle as the new initial position, and continue to determine the migration position of each oil particle at the next time step 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, to obtain the target position of each oil particle.

[0236] Optionally, the prediction result output module 705 can be used for:

[0237] Statistically calculate the residence time of each oil particle at a depth below 0.05 m of the sea surface, and substitute the residence time into the formula:

[0238] ;

[0239] Calculate the standard deviation corresponding to each oil particle;

[0240] where, is the standard deviation of the i-th oil particle, is the residence time, is the diffusion coefficient;

[0241] Assign a target volume to each oil particle at the target position according to the standard deviation to form a concentration field, and obtain the prediction result of the diffusion in the oil spill area; the lateral profile of the target volume is a circle with the standard deviation as the radius, and the longitudinal profile of the target volume is an ellipse with 2 times the standard deviation as the minor axis.

[0242] Embodiments of the present invention can perform the division of functional modules according to the above method examples. For example, each functional module can be divided corresponding 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 a software functional module. It should be noted that the division of modules in the embodiments of the present invention is illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0243] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part 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 instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device, or other programmable devices. The computer program or instructions can 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 instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center integrating one or more available media. The available medium can be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); it can also be a semiconductor medium, such as a solid state drive (SSD).

[0244] Although the present invention has been described in conjunction with various embodiments herein, however, in the process of implementing the claimed invention, those skilled in the art can 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 "a" or "one" does not exclude a plurality. A single processor or other unit can implement several functions recited in the claims. Certain measures are recited in mutually different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0245] Although the present invention has been described in connection with specific features and their embodiments, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the invention. Accordingly, the specification and drawings are merely exemplary illustrations of the invention defined by the appended claims and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

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; According to the dispersant action parameters and the simulation parameters, the target entrainment probability of the oil spill area is determined by an entrainment algorithm; 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; Determining the target entrainment probability of the oil spill area by an entrainment algorithm according to the dispersant action parameter and the simulation parameter 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, , , , 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; 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.

2. The method for predicting oil spill diffusion by realizing dispersant response according to claim 1, 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.

3. The method for predicting oil spill diffusion by realizing 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.

4. The method for predicting oil spill diffusion by implementing dispersant response according to claim 3, 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.

5. The method for predicting oil spill diffusion by implementing dispersant response according to claim 3, 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.

6. The method for predicting oil spill diffusion by implementing dispersant response according to claim 3, 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.

7. The method for predicting oil spill diffusion by implementing dispersant response according to claim 3, 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.

8. 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 The standard deviation of oil particles, 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.

9. 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 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 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 includes: 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, , , , 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; 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 spread of the oil spill area based on the target position; the prediction result includes the concentration field formed by each oil particle.

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