A method for back-calculating exhaust gas of sailing ships based on optimization theory

Abstract

The present invention discloses a method for back-calculating exhaust gas of a ship underway based on optimization theory. The FLUENT software is used to simulate the diffusion and dilution process of ship exhaust gas. On the basis of the Gaussian point source diffusion model, the Gaussian smoke puff model and the Gaussian line source diffusion model, an optimized Gaussian model based on the study of exhaust gas diffusion conditions is proposed, and a source strength back-calculation model based on optimization theory is established. The ship's dynamic and static information, climate and weather information and ship exhaust monitoring information are obtained through the ship's AIS system and other monitoring equipment, and the intelligent optimization back-calculation method based on the Gaussian model is used to obtain the exhaust emission source intensity of the ship passing through the bridge. The method of the present invention ensures the solution efficiency and solution accuracy of the ship exhaust emission concentration inverted by the remote monitoring equipment, greatly improves the law enforcement ability and law enforcement efficiency of the maritime department to closely supervise the ship exhaust gas, strengthens the supervision and control scope of ships with excessive exhaust emissions, and has extremely strong engineering practical significance.

Description

Technical Field

[0001] The invention relates to an optimization theory-based exhaust back-calculation method for a sailing ship, and belongs to the technical field of ship exhaust remote sensing monitoring. Background Art

[0002] In order to control air pollution caused by ship exhaust in my country's inland river emission control areas, the Ministry of Transport has recently limited the sulfur content of marine fuel in inland river emission control areas to 0.1% m / m.

[0003] The use of inferior marine fuel, especially the use of fuel with excessive sulfur content, is an important reason for excessive exhaust emissions from ships and serious air pollution. There are great obstacles in the law enforcement process of the maritime department in the supervision and control of marine fuel. At present, the main method used is to extract oil samples from ships in the control area and send them for inspection to strengthen the supervision and inspection of marine fuel, which has many limitations such as long cycle, small scope, and labor consumption.

[0004] In order to improve the law enforcement capabilities and level of the maritime department, local governments have gradually introduced scientific and technological exhaust gas supervision methods, such as spectral analysis technology, sniffing method and differential absorption lidar technology, etc., providing equipment and technical support for the establishment of a remote monitoring platform.

[0005] In order to better integrate the ship exhaust remote monitoring equipment with the remote monitoring platform, use the monitoring equipment to monitor the exhaust concentration and more effectively and accurately calculate the ship exhaust emission concentration, so as to effectively judge and capture the ships with excessive emissions, there are currently a large number of engineering and technical problems that need to be explored and solved. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a method for back-calculating exhaust gas of a ship at sea based on optimization theory, to perform remote sensing monitoring of ship exhaust gas, and to improve the efficiency and accuracy of back-calculating the exhaust gas emission concentration of the ship by remote monitoring equipment.

[0007] The present invention adopts the following technical solutions to solve the above technical problems:

[0008] A method for back-calculating exhaust gas of a sailing ship based on optimization theory comprises the following steps:

[0009] Step 1: Establish a ship exhaust emission model based on a bridge through FLUENT simulation software, and use the ship exhaust emission model to analyze the influencing factors of ship exhaust diffusion;

[0010] Step 2, based on the influencing factors of ship exhaust diffusion and combined with the Gaussian smoke puff model, an optimized Gaussian exhaust diffusion model is established;

[0011] Step 3: According to the optimized Gaussian exhaust diffusion model, an exhaust back-calculation model of the ship at sea based on optimization theory is established, and the exhaust back-calculation model of the ship at sea is solved to solve the exhaust source strength of the ship from the monitored exhaust concentration of the ship.

[0012] As a preferred embodiment of the present invention, the specific process of step 1 is as follows:

[0013] Step 11, combining the bridge structure parameters, establish a ship operation flow field calculation model, where the model is 1232m long, 1600m wide, 120m high, the bridge is 32m high, the total length of the ship is 66.70m, the total width is 12.45m, the draft is 2.60m, and the chimney is 0.8m high;

[0014] Step 12: Determine the model calculation conditions, specifically:

[0015] (1) For ship exhaust emissions, the turbulence equation is selected, the k-εepsilon model is used, the component is selected as sulfur dioxide, the component transport model is turned on, and the transport of sulfur dioxide is simulated;

[0016] (2) The pressure outlet boundary condition is adopted, the direction of the external airflow is set as the velocity inlet condition, the velocity magnitude is 10 m / s, an area is set at the bow position on the ship surface as the sulfur dioxide release outlet, the exhaust gas emission rate is set to 1.1 kg / s, and the hull navigation speed is 3.75 m / s;

[0017] (3) Set the entire flow field as fluid, initialize globally, and select the second-order difference format;

[0018] (4) When the ship is underway, the exhaust diffusion and propagation is a non-steady-state process. The non-steady-state iteration is used and the calculation time step is set to 0.1s;

[0019] Step 13, 20 and 16 monitoring points are laid out in a chessboard pattern in the horizontal and vertical directions within the calculation domain respectively, and the distribution characteristics of ship exhaust in space are analyzed. The specific settings of the monitoring points are as follows: let the direction perpendicular to the bridge be the row and the direction parallel to the bridge be the column, then the monitoring points laid out in the horizontal direction are 4 rows and 5 columns, the monitoring points in the horizontal direction are on the same horizontal line as the emission source, in the direction parallel to the bridge, the interval between two adjacent monitoring points is 40m, in the direction perpendicular to the bridge, the interval between two adjacent monitoring points is 50m, and 4 of the monitoring points are set On the bridge, the remaining monitoring points are set at the same horizontal height and extend in the direction of the ship's travel; the monitoring points laid in the vertical direction are located in the longitudinal plane of the bridge, and the monitoring points laid in the vertical direction are 4 rows and 4 columns, of which 4 monitoring points are set 10m below the bridge deck, 4 monitoring points are set on the bridge deck, and the remaining monitoring points are set above the bridge deck, and the interval between two adjacent monitoring points in the direction parallel to the bridge deck is 40m, and in the direction perpendicular to the bridge deck, the interval between the monitoring points below the bridge deck and the monitoring points on the bridge deck is 10m, and the interval between the remaining monitoring points is 40m;

[0020] Step 14, the factors affecting the diffusion of ship exhaust gas include the ship exhaust gas emission time, wind speed, ship speed and the surrounding climate and geographical conditions of the ship exhaust gas diffusion.

[0021] As a preferred embodiment of the present invention, in step 2, a rectangular coordinate system is established with the exhaust chimney outlet of the ship as the coordinate origin, the downwind direction as the positive direction of the x-axis, the direction perpendicular to the x-axis in the horizontal plane as the y-axis, the left side of the positive direction of the x-axis as the positive direction of the y-axis, and the vertical upward direction as the positive direction of the z-axis;

[0022] Ship reaches t n At that moment, the concentration contribution value to the monitoring point on the bridge is the sum of the cumulative concentration contribution values ​​of the ship at each moment, that is:

[0023]

[0024] Among them, C(x,y,z,t n ) indicates that the ship sails to t n At time , the total concentration contribution of ships to the monitoring point on the bridge; c j (x,y,z,t n ) indicates that the ship sails to t j The exhaust gas emitted at time t diffuses to the downwind monitoring point (x, y, z) at t n The concentration at the time of j Indicates t j Ship exhaust emission intensity at the moment, in kg / s; t jAtmospheric diffusion coefficients on the x, y, and z axes at time instants are calculated using the Pasquill-Gifford model; u is the synthetic ambient wind speed in m / s; H is the height of the pollution source in m; x j ,y j 、z j t j The coordinate values ​​of the monitoring point in the x, y, and z axis directions at all times.

[0025] As a preferred solution of the present invention, in step 3, the error limit between the ship exhaust concentration monitoring value and the optimized Gaussian exhaust diffusion model calculation value is used as the objective function, the process of solving the ship exhaust source strength is converted into an optimization problem, and the source strength information is searched using an intelligent optimization algorithm, and the solution with the minimum objective function value is obtained as the source strength information;

[0026] m exhaust gas monitors are arranged in a chessboard pattern downwind of the ship exhaust diffusion to monitor the sulfur dioxide concentration of the downwind ships, with the symbol represents the monitoring concentration obtained by the i-th exhaust gas monitor, represents the calculated concentration value obtained at the position of the i-th exhaust gas monitor, and the objective function f(Q) is set to the sum of squares of the errors between the monitored concentration value and the calculated concentration value. Then the source strength information backcalculation problem is reduced to an unconstrained minimum optimization problem, and the objective function f(Q) is:

[0027]

[0028] Genetic algorithm or particle swarm algorithm is used to solve the above unconstrained minimum optimization problem, and the ship exhaust source strength Q is solved by the ship exhaust monitoring concentration.

[0029] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0030] 1. The present invention uses FLUENT software to model and simulate the exhaust diffusion process of ships in operation for monitoring ship exhaust emissions, and sets 36 vertical and horizontal monitoring points to analyze the spatiotemporal distribution characteristics of exhaust diffusion. Combined with the comparative analysis of the Gaussian diffusion model, the continuous diffusion model of ships in operation based on Gaussian puffs is optimized. Then, the intelligent optimization algorithm (genetic algorithm and particle swarm algorithm) is applied to the exhaust diffusion backcalculation to find the optimal solution.

[0031] 2. The present invention proposes a back-calculation idea and method for the source strength of ship exhaust emissions, and verifies the feasibility of the back-calculation method using simulation cases, ensuring the efficiency and accuracy of the solution of ship exhaust emission concentrations by remote monitoring equipment, greatly improving the law enforcement capabilities and efficiency of maritime departments in closely supervising ship exhaust, and strengthening the supervision and control scope of ships with excessive exhaust emissions, which has extremely strong practical engineering significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flow chart of the exhaust back-calculation method of a ship underway based on the optimization theory of the present invention;

[0033] Figure 2 is a distribution diagram of monitoring points of the method of the present invention, wherein (a) is the horizontal direction and (b) is the vertical direction;

[0034] Figure 3 This is an example diagram of the Gaussian model after optimization by the method of the present invention. DETAILED DESCRIPTION

[0035] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0036] like Figure 1 As shown, it is a flow chart of the exhaust back-calculation method of a ship underway based on optimization theory, and the specific steps are as follows:

[0037] Step 1: Use FLUENT simulation software to establish a ship exhaust emission model based on Nanjing Dashengguan Bridge, and analyze the spatial characteristics and influencing factors of ship exhaust emissions. The details are as follows:

[0038] Step 11: Establish the model: Combined with the structural parameters of Nanjing Dashengguan Yangtze River Bridge, establish the ship operation flow field calculation model. Some parameter setting values ​​are as follows: the model is 1232m long, 1600m wide, 120m high, the bridge is 32m high, the total length of the ship is 66.70m, the total width is 12.45m, the draft is 2.60m, and the ship height (chimney height) is 0.8m.

[0039] Step 12: Determine the calculation conditions and basic model:

[0040] (1) Setting up equations: FLUENT contains many physical models, such as compressible and incompressible flow, laminar and turbulent flow, steady and unsteady flow, heat transfer, chemical reaction, etc. Among them, the turbulence model includes Spalart-Allmaras model, k-ε model group, RSM group, LES group, k-ω model group, etc. The ship exhaust emission uses the turbulence equation, uses the classic k-εepsilon model, selects sulfur dioxide as the component, opens the component transport model, and simulates the transport of sulfur dioxide.

[0041] (2) Boundary condition setting: In the model calculation process, in order to facilitate numerical calculation, the commonly used pressure outlet boundary condition is adopted, the direction of the external airflow is set as the velocity inlet condition, the velocity magnitude is 10m / s, an area is set at the bow position of the ship surface as the sulfur dioxide release outlet, the exhaust gas emission rate is set to 1.1kg / s, and the hull navigation speed is 3.75m / s.

[0042] (3) Initialize the flow field: Set the entire flow field to fluid, initialize globally, and select the second-order difference format.

[0043] (4) Iterative solution: When the ship is underway, the diffusion and propagation of exhaust gas is a non-steady-state process. Therefore, the non-steady-state iteration is adopted and the calculation time step is set to 0.1 s.

[0044] Step 12: Setting of horizontal and vertical monitoring points: To analyze the distribution characteristics of exhaust gas in space, 20 and 16 checkpoints are laid out in a chessboard pattern in the horizontal and vertical directions within the calculation domain (e.g. Figure 2 (a) and (b) in the figure). The horizontal monitoring point is on the same horizontal line as the emission source (ship height). In the direction parallel to the bridge, the interval between two adjacent monitoring points is 40m. In the direction perpendicular to the bridge, the interval between two adjacent points is 50m. Points 1, 2, 3, and 4 are set on the bridge. The vertical monitoring points are located in the longitudinal plane of the bridge. Points 1, 2, 3, and 4 are located 10m below the bridge deck. Points 5, 6, 7, and 8 are located parallel to the bridge deck. In the direction perpendicular to the bridge deck, the interval between two adjacent points of points 5 to 16 is 40m. In the direction parallel to the bridge deck, the interval between two adjacent points of points 1 to 16 is 40m.

[0045] Step 13: Temporal and spatial distribution characteristics of ship exhaust:

[0046] (1) The exhaust emission time of a ship is an important factor affecting the exhaust diffusion: the longer the emission time, the more obvious the exhaust concentration at the spatial monitoring location is affected by the diffusion of the ship's exhaust smoke.

[0047] (2) The combined effect of wind speed and ship speed is also an important factor affecting ship exhaust.

[0048] (3) The surrounding climate and geographical conditions are important factors affecting the diffusion of ship exhaust gas.

[0049] Step 2: Based on the analysis of the spatial distribution of ship exhaust, combined with the existing atmospheric diffusion model under the Gaussian mode, an optimized Gaussian model based on the study of exhaust diffusion conditions is proposed. The details are as follows:

[0050] like Figure 3As shown in the figure, a rectangular coordinate system is established with the downwind direction as the positive direction of the x-axis, the horizontal direction perpendicular to the x-axis as the positive direction of the y-axis, and the vertical upward direction as the positive direction of the z-axis. The origin of the coordinate system is set at the exhaust chimney outlet of the ship, and a ship exhaust diffusion model based on the Gaussian puff model can be established. Assuming that the ship travels to t n At the moment, the ship is at (x n ,y n ,z n ), the concentration contribution value of the monitoring point on the bridge is the concentration of the ship at each time, i.e., t0, t1, t2, t3…t n At the moment, the cumulative concentration contribution is the sum. The exhaust concentration value of a ship at any point downwind can be expressed as:

[0051]

[0052] Among them, c j (x,y,z,t n ) indicates that the ship sails to t j The exhaust gas emitted at time t diffuses to a monitoring point (x, y, z) downwind at t n The concentration at the time of j Indicates t j Ship exhaust emission intensity at the moment, unit: kg / s; t j Atmospheric diffusion coefficient on the x, y, and z axes at time; u is the synthetic ambient wind speed, in m / s; H is the height of the pollution source, in m.

[0053] The ship is traveling towards the bridge, gradually approaching and then leaving the bridge. The monitoring point is constantly monitoring the exhaust gas concentration, and selecting the peak concentration to calculate Q to determine whether it exceeds the standard. The t0 moment refers to the moment when the monitoring point can detect the exhaust gas concentration from stable to changing.

[0054] The atmospheric diffusion coefficient can be calculated by the Pasquill-Gifford model. The atmospheric stability is divided into 6 levels according to wind speed and light conditions, as shown in Table 1.

[0055] Table 1 Atmospheric stability levels

[0056]

[0057]

[0058] In the table, A is extremely unstable, B is moderately unstable, C is slightly unstable, D is neutrally stable, E is slightly stable, and F is moderately stable.

[0059] In the Pasquill-Gifford model, the relationship between the atmospheric stability level and the atmospheric diffusion coefficient can be obtained from Table 2:

[0060] Table 2 Diffusion coefficient and atmospheric stability

[0061] Pasquill-Gifford stability level <![CDATA[σ y or x ]]> <![CDATA[σ z ]]> A <![CDATA[0.18x 0.92 ]]> <![CDATA[0.60x 0.75 ]]> B <![CDATA[0.14x 0.92 ]]> <![CDATA[0.53x 0.75 ]]> C <![CDATA[0.10x 0.92 ]]> <![CDATA[0.34x 0.75 ]]> D <![CDATA[0.06x 0.92 ]]> <![CDATA[0.15x 0.75 ]]> E <![CDATA[0.04x 0.92 ]]> <![CDATA[0.10x 0.75 ]]> F <![CDATA[0.02x 0.92 ]]> <![CDATA[0.05x 0.75 ]]>

[0062] From Table 2, we can get the atmospheric diffusion coefficient σ of the Gaussian puff model: x , σ y , σ z are all exponential functions of the downwind distance x, and σ x =σ y .

[0063] Step 3: According to the optimized Gaussian exhaust diffusion model, establish an exhaust back-calculation model for ships in motion based on optimization theory.

[0064] By establishing the quantitative relationship between the ship chimney leakage source, pollution diffusion and exhaust gas monitoring values, and taking the error limit between the ship exhaust gas concentration monitoring value and the diffusion model calculation value as the objective function, the process of reverse solving the source strength is transformed into a traditional optimization problem. The source strength information is searched using an intelligent optimization algorithm, and the solution with the smallest target value is the target source strength information.

[0065] Assume that m sniffing or spectral monitors are arranged in a chessboard pattern downwind of the ship's exhaust gas diffusion, and are used to monitor the sulfur dioxide concentration at a certain point downwind, represented by the symbol represents the monitoring concentration obtained by the i-th exhaust gas monitor, represents the calculated exhaust concentration value obtained at the position of the i-th exhaust gas monitor. The objective function f(Q) is set as the sum of squares of the errors between the monitored concentration and the calculated concentration. Therefore, the source strength information back-calculation problem can be reduced to the solution of the unconstrained multidimensional extreme value problem without an objective function. The objective function f(Q) is:

[0066]

[0067] The above unconstrained minimum optimization problem is solved by using intelligent optimization algorithms such as genetic algorithm or particle swarm algorithm, so as to solve the ship exhaust source strength from the ship exhaust monitoring concentration.

[0068] Example

[0069] When installing ship exhaust monitoring equipment, the bridge positions on both sides of the ship channel are generally selected, and two points on the same horizontal line are selected as the installation points of the exhaust monitoring equipment. Therefore, the monitoring points No. 6 and No. 7 in the same row where the monitoring concentration changes significantly over a continuous period of time are selected. At this time, the ship's exhaust concentration has a greater impact on the monitoring position. In the simulation experiment, the ship sailed at a speed of 13.5km / h (3.75m / s) from a position 87.5m upstream of the bridge along a straight line perpendicular to the longitudinal plane of the bridge at a uniform speed in the direction of the bridge. During the driving process, the real-time stable and uniform release rate of sulfur dioxide concentration was set to 1.1kg / s, and the wind speed was 10m / s. The origin of the coordinate axis is set at the chimney discharge port of the ship. The ship is traveling at a uniform speed, so the origin of the coordinate moves with the ship at a uniform speed. The ship's heading is set as the x-axis, which is the same direction as the natural wind direction. Among them, the No. 6 and No. 7 monitoring points are located at 20m symmetrically on both sides of the route, then y6=20, y7=-20, the horizontal height is the same as the height of the ship's exhaust emission source, z=0, L is the distance between the ship and the upstream of the bridge, when the ship travels ts, L=87.5-3.75t, L0 is the distance between the starting point of the ship and the bridge, that is, L0=87.5m, the No. 6 and No. 7 monitoring points are 50m away from the downstream of the bridge, so the downwind x coordinates of No. 6 and No. 7 are x 6、7 =50+L.

[0070] From the above analysis, we can see that the ship speed and the natural wind speed have the same direction, and the synthetic wind speed after synthesis is 6.25m / s. Therefore, according to the Pasquill-Gifford model, the atmospheric stability level can be set to C. At this time, σ y =σ x =0.10x 0.92 , σ z =0.34x 0.75 .

[0071] The monitoring data of monitoring points 6 and 7 are taken for source strength back calculation analysis.

[0072] Table 3 Monitoring concentrations and relative coordinates of some monitoring points

[0073]

[0074]

[0075] Substitute the above data into the above objective function minimum problem in units of 0.1s, calculate the calculated value of ship exhaust diffusion when the ship travels to 17.8s, and calculate the variance between the monitored value and the calculated value as the objective function. The smaller the objective function value, the smaller the error between the monitored value and the calculated value, that is, the best inverse calculation effect. Therefore, the optimization purpose is to reduce the objective function value through various optimization algorithms, find the best match between the exhaust monitoring concentration value and the calculated concentration value, and the best match between the concentration value and the calculated concentration value, to obtain the leakage source intensity (Q) and the leakage source location (L0 is the distance between the starting point of the ship and the bridge).

[0076] Use a genetic algorithm to find the optimal value:

[0077] 1. Coding: Real number coding is performed on the unknown ship exhaust source intensity Q and the ship-bridge distance L0 (ship position).

[0078] 2. Generate the initial population: First, set the population size to 20 (two unknown quantities - Q and L0 are set to 10 individuals each), and randomly generate the initial population using the initial population generation function (Constraint dependent).

[0079] 3. Fitness function: Substitute the individuals in the population into the objective function of this problem, and calculate the sum of squares of the error between the calculated concentration and the monitored concentration of ship exhaust. The objective function is the sum of squares of the error between the monitored concentration and the calculated concentration at each monitoring point, and the formula is:

[0080]

[0081] 4. Selection, crossover, and mutation operations: Combined with relevant selection, crossover, and mutation models, operations are performed on individuals in the group to continuously complete the replacement process of the old and new populations, realize the global search of the genetic algorithm, and achieve an iterative process towards the optimal source strength information solution.

[0082] 5. Set the maximum evolutionary number to 500 and the termination number to 150. For the new population, repeat step 4 until the iterative result of the genetic algorithm meets the termination condition. At this time, the individual with the highest fitness in the new generation is taken as the global optimal solution in this inverse calculation problem. Output the result and draw the iteration graph.

[0083] Results: The genetic algorithm stopped after 74 iterations, and the source sulfur dioxide emission intensity was 1.1066 kg / m 3 , the objective function value is 14.9349045, and the starting point of the ship’s voyage is 87.774m away from the bridge.

[0084] The above embodiments are only for illustrating the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for back-calculating exhaust gas of a sailing ship based on optimization theory, characterized in that: The steps include: Step 1: Establish a ship exhaust emission model based on a bridge through FLUENT simulation software, and use the ship exhaust emission model to analyze the influencing factors of ship exhaust diffusion; the specific process is as follows: Step 11, combining the bridge structure parameters, establish a ship operation flow field calculation model, where the model is 1232m long, 1600m wide, 120m high, the bridge is 32m high, the total length of the ship is 66.70m, the total width is 12.45m, the draft is 2.60m, and the chimney is 0.8m high; Step 12, determining the model calculation conditions; Step 13, 20 and 16 monitoring points are laid out in a chessboard pattern in the horizontal and vertical directions within the calculation domain, respectively, to analyze the distribution characteristics of ship exhaust in space; Step 14, the factors affecting the diffusion of ship exhaust gas include the emission time of ship exhaust gas, wind speed, ship speed and the surrounding climate and geographical conditions of the ship exhaust gas diffusion; Step 2, based on the influencing factors of ship exhaust diffusion and combined with the Gaussian smoke puff model, an optimized Gaussian exhaust diffusion model is established; In the step 2, a rectangular coordinate system is established with the exhaust chimney outlet of the ship as the coordinate origin, the downwind direction as the positive direction of the x-axis, the direction perpendicular to the x-axis in the horizontal plane as the y-axis, the left side of the positive direction of the x-axis as the positive direction of the y-axis, and the vertical upward direction as the positive direction of the z-axis; Ship reaches t n At that moment, the concentration contribution value to the monitoring point on the bridge is the sum of the cumulative concentration contribution values ​​of the ship at each moment, that is: Among them, C(x,y,z,t n ) indicates that the ship sails to t n At time , the total concentration contribution of ships to the monitoring point on the bridge; c j (x,y,z,t n ) indicates that the ship sails to t j The exhaust gas emitted at time t diffuses to the downwind monitoring point (x, y, z) at t n The concentration at the time of j Indicates t j Ship exhaust emission intensity at the moment, in kg / s; t j Atmospheric diffusion coefficients on the x, y, and z axes at time instants are calculated using the Pasquill-Gifford model; u is the synthetic ambient wind speed in m / s; H is the height of the pollution source in m; x j ,y j 、z j t j The coordinate values ​​of the monitoring point in the x, y, and z axis directions at all times; Step 3: According to the optimized Gaussian exhaust diffusion model, an exhaust back-calculation model of the ship at sea based on optimization theory is established, and the exhaust back-calculation model of the ship at sea is solved to solve the exhaust source strength of the ship from the monitored exhaust concentration of the ship.

2. The exhaust back-calculation method for a sailing ship based on optimization theory according to claim 1 is characterized in that: The specific process of step 12 is as follows: (1) For ship exhaust emissions, the turbulence equation is selected, the k-εepsilon model is used, the component is selected as sulfur dioxide, the component transport model is turned on, and the transport of sulfur dioxide is simulated; (2) The pressure outlet boundary condition is adopted, the direction of the external airflow is set as the velocity inlet condition, the velocity magnitude is 10 m / s, an area is set at the bow position on the ship surface as the sulfur dioxide release outlet, the exhaust gas emission rate is set to 1.1 kg / s, and the hull navigation speed is 3.75 m / s; (3) Set the entire flow field as fluid, initialize globally, and select the second-order difference format; (4) When the ship is underway, the diffusion and propagation of exhaust gas is a non-steady-state process. The non-steady-state iteration is adopted and the calculation time step is set to 0.1 s.

3. The exhaust back-calculation method for a sailing ship based on optimization theory according to claim 1 is characterized in that: The specific setting process of the monitoring point described in step 13 is as follows: Let the direction perpendicular to the bridge be the row and the direction parallel to the bridge be the column, then there are 4 rows and 5 columns of monitoring points laid in the horizontal direction, the monitoring points in the horizontal direction are on the same horizontal line as the emission source, in the direction parallel to the bridge, the interval between two adjacent monitoring points is 40m, in the direction perpendicular to the bridge, the interval between two adjacent monitoring points is 50m, and 4 of the monitoring points are set on the bridge, and the remaining monitoring points are set at the same horizontal height and extend in the direction of ship travel; the monitoring points laid in the vertical direction are located in the longitudinal plane of the bridge, and there are 4 rows and 4 columns of monitoring points laid in the vertical direction, among which 4 monitoring points are set 10m below the bridge deck, 4 monitoring points are set on the bridge deck, and the remaining monitoring points are set above the bridge deck, and in the direction parallel to the bridge deck, the interval between two adjacent monitoring points is 40m, in the direction perpendicular to the bridge deck, the monitoring points below the bridge deck are 10m away from the monitoring points on the bridge deck, and the remaining monitoring points are 40m away.

4. The exhaust back-calculation method for a sailing ship based on optimization theory according to claim 1 is characterized in that: In step 3, the error limit between the ship exhaust concentration monitoring value and the optimized Gaussian exhaust diffusion model calculation value is used as the objective function, the process of solving the ship exhaust source strength is converted into an optimization problem, and the source strength information is searched using an intelligent optimization algorithm, and the solution with the minimum objective function value is obtained, which is the source strength information; m exhaust gas monitors are arranged in a chessboard pattern downwind of the ship exhaust diffusion to monitor the sulfur dioxide concentration of the downwind ships, with the symbol represents the monitoring concentration obtained by the i-th exhaust gas monitor, represents the calculated concentration value obtained at the position of the i-th exhaust gas monitor, and the objective function f(Q) is set to the sum of squares of the errors between the monitored concentration value and the calculated concentration value. Then the source strength information backcalculation problem is reduced to an unconstrained minimum optimization problem, and the objective function f(Q) is: Genetic algorithm or particle swarm algorithm is used to solve the above unconstrained minimum optimization problem, so as to solve the ship exhaust source strength Q from the ship exhaust monitoring concentration.

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