LNG tank car unloading component prediction method, system, equipment and medium

Abstract

The invention relates to the technical field of natural gas component pre-processing, and discloses an LNG tank car unloading component prediction method, system, equipment and medium, an LNG tank car unloading component prediction model with a theoretical basis and combining a cargo cavity model and a dynamic numerical simulation model is constructed from the perspective of a mechanism model, and the LNG tank car unloading component prediction model is obtained through dynamic simulation. The unloading liquefied natural gas component data under various working conditions (influence factors comprise liquid level, temperature, pressure and the like) can be accurately and efficiently predicted, and more accurate and low-cost LNG energy settlement can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of natural gas component prediction, and in particular to a method, system, equipment and medium for predicting components of LNG tank truck unloading. Background Art

[0002] LNG (liquefied natural gas) tanker transportation generally adopts the mass measurement method, which uses the truck scale to weigh the mass of the tanker before and after filling, and calculates the mass difference as the mass of the LNG cargo, and uses the mass of the LNG cargo as the unit of trade settlement. However, natural gas is a mixed fossil fuel, and its components vary greatly depending on the origin. The mass measurement of LNG cargo cannot reflect the quality difference of LNG as a fuel, and cannot achieve fair trade.

[0003] In order to meet the needs of the rapid development of the natural gas industry, the country has vigorously promoted natural gas energy metering in recent years, but the relevant methods and standards have not clarified the specific methods of LNG energy metering. In response to the national natural gas energy metering reform requirements, many domestic LNG receiving stations have reformed the natural gas metering method. For example, an online chromatograph is added at the main pipe entering the tank truck filling pipeline. By real-time analysis of LNG components to calculate the density and unit mass calorific value, the energy corresponding to each truck of LNG cargo can be calculated. However, my country's LNG imports are huge, and the tank truck market of LNG receiving stations has become large-scale. The aforementioned energy settlement method that relies on online chromatographs is costly and inefficient, and the accuracy of the settlement results cannot be guaranteed.

[0004] Therefore, a method for predicting the unloading composition of LNG tankers is urgently needed to achieve more accurate and low-cost LNG energy settlement. Summary of the invention

[0005] The present invention provides a method, system, device and medium for predicting components of LNG tank truck unloading, which are used to solve the defects of the existing LNG energy settlement method that is overly dependent on an online chromatographic analyzer, resulting in high LNG energy settlement cost and poor accuracy.

[0006] The present invention provides a method for predicting components of LNG tank truck unloading, comprising:

[0007] Obtain liquefied natural gas composition data from LNG tankers at the point of origin;

[0008] The cargo cavity model of the LNG tanker is established, and the first disturbance condition is added to the cargo cavity model to obtain the first outlet boundary condition and model improvement strategy;

[0009] Based on the cargo cavity model of the LNG tank truck, a numerical simulation model of the LNG tank truck is established, and the numerical simulation model is used to perform numerical simulation according to the model improvement strategy to obtain the second outlet boundary condition;

[0010] Based on the numerical simulation model of the LNG tanker, a dynamic numerical simulation model of the process flow is established according to the first outlet boundary condition, and the second disturbance condition is added to the dynamic numerical simulation model of the process flow to obtain the equipment influencing boundary conditions and equipment optimization control strategy;

[0011] Based on the dynamic numerical simulation model of the process flow, according to the second outlet boundary condition and the equipment optimization control strategy, a dynamic numerical simulation model of the process operation is established, and a third disturbance condition is added to the dynamic numerical simulation model of the process operation to obtain the process influence boundary condition;

[0012] According to the liquefied natural gas composition data of the LNG tank truck at the departure place, the dynamic numerical simulation model of process operation is used to predict the changes of the liquefied natural gas composition during unloading, and the liquefied natural gas composition data of the unloading of the LNG tank truck is obtained.

[0013] In one embodiment, the liquefied natural gas components of the LNG tanker at the origin include any one of the following or any combination thereof: methane, ethane, propane, n-butane, isobutane, n-pentane, isopentane, nitrogen, and impurities (such as carbon dioxide, etc.).

[0014] In one embodiment, the disturbance condition includes any one of the following or any combination thereof: sudden flow change, temperature change, pressure fluctuation.

[0015] In one embodiment, the method of establishing a cargo cavity model of an LNG tanker and adding a first disturbance condition to the cargo cavity model to obtain a first outlet boundary condition and a model improvement strategy includes:

[0016] The first disturbance condition is added to the cargo cavity model to obtain the first outlet boundary condition, and the storage tank heat and heat transfer coefficient of the LNG tank truck are calculated;

[0017] According to the storage tank heat and heat transfer coefficient of the LNG tank truck, the model improvement strategy of the cargo cavity model is obtained.

[0018] In one embodiment, the adding of the first disturbance condition to the cargo cavity model to obtain the first outlet boundary condition, and calculating the storage tank heat and heat transfer coefficient of the LNG tank truck, comprises:

[0019] Add the first disturbance condition to the cargo cavity model to obtain the first outlet boundary condition;

[0020] According to the first outlet boundary condition, the heat transfer coefficient outside the cryogenic storage tank, the convection heat transfer coefficient inside the cryogenic storage tank, the apparent thermal conductivity of each layer of the tank wall, the thickness of each layer of the tank wall, the ambient temperature outside the tank, the average temperature of the cryogenic liquid in the tank, and the heat transfer surface area of ​​the cryogenic storage tank are obtained;

[0021] The heat transfer coefficient of the LNG tank truck is obtained based on the heat transfer coefficient outside the cryogenic storage tank, the convection heat transfer coefficient inside the cryogenic storage tank, the apparent thermal conductivity of each layer of the tank wall, and the thickness of each layer of the tank wall;

[0022] According to the heat transfer coefficient of the LNG tank truck, combined with the ambient temperature outside the tank, the average temperature of the cryogenic liquid in the tank, and the heat transfer surface area of ​​the cryogenic tank, the tank heat of the LNG tank truck is obtained.

[0023] In one embodiment, the model improvement strategy includes any one of the following or any combination thereof: increasing the thickness of the insulation layer, changing the heat transfer coefficient, and improving the insulation material of the storage tank.

[0024] In one embodiment, the expression for the heat transfer coefficient of the LNG tanker is:

[0025]

[0026] In the expression of the heat transfer coefficient of the LNG tanker, K represents the heat transfer coefficient of the LNG tanker, A represents the heat transfer surface area of ​​the cryogenic storage tank, m 2 ;h 1 Indicates the heat transfer coefficient outside the cryogenic tank, including the convection heat transfer coefficient h c and the radiation heat transfer coefficient h r ,W / (m 2 ·K);h 2 Indicates the convection heat transfer coefficient in the low-temperature storage tank, W / (m 2 ·K);λ i Indicates the apparent thermal conductivity of each layer of the tank wall W / (m 2 ·K);δ i Indicates the thickness of each layer of the tank wall, m.

[0027] In one embodiment, the expression for the storage tank heat of the LNG tanker is:

[0028]

[0029] In the expression of the storage tank heat of the LNG tank truck, Q represents the storage tank heat of the LNG tank truck; A represents the heat transfer surface area of ​​the low-temperature storage tank, m 2 ;h 1 Indicates the heat transfer coefficient outside the cryogenic tank, including the convection heat transfer coefficient h c and the radiation heat transfer coefficient h r ,W / (m 2 ·K);h 2 Indicates the convection heat transfer coefficient in the low-temperature storage tank, W / (m 2 ·K);λ i Indicates the apparent thermal conductivity of each layer of the tank wall W / (m2·K); δ i Indicates the thickness of each layer of the tank wall, m; t f1Indicates the ambient temperature outside the tank, K; t f2 Represents the average temperature of the cryogenic liquid in the tank, K.

[0030] In one embodiment, the cargo cavity model of the LNG tank truck is used to establish a numerical simulation model of the LNG tank truck, and the numerical simulation model is used to perform numerical simulation according to the model improvement strategy to obtain the second outlet boundary condition, including:

[0031] Conduct mesh quality inspection and independence verification on the cargo cavity model of the LNG tanker;

[0032] Determine the equations satisfied by the free surface boundary;

[0033] Based on the cargo cavity model of the LNG tanker after mesh quality detection and independence verification, combined with the equations satisfied by the free liquid surface boundary, the VOF method (Volume of Fluid Method) is used to establish a numerical simulation model of the LNG tanker.

[0034] Based on the numerical simulation model of the LNG tanker, a turbulence model is set;

[0035] Based on the numerical simulation model of the LNG tanker, a phase change model is set;

[0036] Based on the numerical simulation model of the LNG tanker, the physical property parameters of LNG are set;

[0037] The formats of various models on the numerical simulation model of the LNG tank truck are unified, and the numerical simulation model of the LNG tank truck is used to perform numerical simulation according to the model improvement strategy to obtain the second outlet boundary condition.

[0038] In one embodiment, the equation satisfied by the free liquid surface boundary is:

[0039]

[0040] In the equation satisfied by the free surface boundary, c represents the shape or position of the liquid surface, which in this equation represents the free surface, and the free surface refers to the interface where the liquid and the gas contact; t represents time; n represents the direction perpendicular to the free surface; ρ represents the density of the fluid, which is the ratio of the mass of a substance to its volume, and directly affects the inertial properties of the fluid; ν represents the velocity, which describes the velocity of the fluid micro-group; Represents the partial derivative operator of a variable, which is used to describe the rate of change of the variable over time or space; V n represents the normal velocity of the free liquid surface; V c represents the tangential velocity of the free liquid surface; P represents the fluid pressure of the free liquid surface; P 0 represents the atmospheric pressure at the free surface; that is, It represents the rate of change of velocity along the normal direction with time; It represents the rate of change of velocity along the tangent direction in the normal direction; It represents the rate of change of the velocity along the normal direction in the normal direction. Among them, the wall condition satisfies the non-slip condition: V n ' represents the normal velocity to the wall; V c ' represents the wall tangential velocity.

[0041] In one embodiment, the expression of the turbulence model includes the turbulent kinetic energy k equation and the dissipation rate ε equation, wherein the turbulent kinetic energy k equation is:

[0042]

[0043] The dissipation rate ε equation is:

[0044]

[0045] In the formula, C 1ε , C 2ε and C 3ε represents the empirical constant, C 1ε =1.44, C 2ε =1.92, C 3ε =0.09;σ k , σ ε represents the Prandtl number corresponding to the turbulent kinetic energy k and the dissipation rate ε, σ k =1.0,σ ε =1.3; ρ represents the fluid density, in kg / m 3 ; represents the body derivative, which represents the rate of change of turbulent kinetic energy at a point in the control volume with time; μ represents the dynamic viscosity coefficient, also referred to as the viscosity coefficient or viscosity, which is a physical quantity that describes the resistance to fluid flow. It represents the force required for relative movement of fluid layers with a unit velocity difference per unit area due to internal friction; μ t The turbulent viscosity coefficient, also known as eddy viscosity or eddy viscosity, is a parameter that characterizes the interaction between fluid particles in turbulent motion; σ k The Prandtl number represents the turbulent kinetic energy, which determines the diffusion degree of turbulent kinetic energy in different directions; G k represents the turbulent kinetic energy generation term due to the mean velocity gradient, which is usually related to the Reynolds stress; G b represents the term of turbulent kinetic energy generation due to buoyancy; ε represents the turbulent kinetic energy dissipation rate, which represents the rate at which turbulent kinetic energy is dissipated in small-scale vortices; Y M represents the contribution of pulsating expansion to the total dissipation rate in compressible turbulent flow; Represents the spatial coordinate x iThe partial derivative of Denotes the turbulent kinetic energy k versus the spatial coordinate x i The partial derivative of describes the distribution of turbulent kinetic energy in space, that is, the changing trend of turbulent kinetic energy at different positions; The dissipation rate ε varies with the spatial position x i The change of, that is, the changing trend of the dissipation rate at different positions; Represents the rate of change of dissipation rate over time.

[0046] In one embodiment, the phase change model is an evaporation-condensation model, preferably a Lee model.

[0047] In one embodiment, the physical property parameters of LNG include any one of the following or any combination thereof: density, specific heat, heat transfer coefficient, and dynamic viscosity.

[0048] In one embodiment, the format of each model on the numerical simulation model of the LNG tank truck is unified, specifically, the format of each model on the numerical simulation model of the LNG tank truck is unified into the QUICK format.

[0049] In one embodiment, the dynamic numerical simulation model of the process flow includes a continuity equation and a momentum transport equation between the devices, wherein the continuity equation is:

[0050]

[0051] The momentum transport equation includes:

[0052] x-momentum equation:

[0053] The y-momentum equation is:

[0054] The z-momentum equation:

[0055] Where p represents the fluid pressure; S u , S v and S w represents the generalized force of the fluid; ρ represents the fluid density; u represents the velocity component in the x direction; v represents the velocity component in the y direction; w represents the velocity component in the z direction; t represents time; div represents the divergence operator, which is used to calculate the divergence rate of the vector field; μ represents the dynamic viscosity coefficient, which represents the ability of the fluid to resist shear deformation; Gradient tensor representing velocity gradient; S u represents the external source term or volume force term in the x direction, representing the external force acting on the fluid; The mass representing the density multiplied by the velocity in the x direction changes with time, that is, the increase or decrease in the mass of the fluid passing through a certain point per unit time; represents the convection term, which describes the momentum transfer in the x direction due to fluid flow; div(μgradu) represents the diffusion term, which describes the momentum dissipation in the x direction due to the viscous effect; represents the pressure gradient term, which shows how changes in pressure in the x-direction affect the acceleration of the fluid; The mass representing the density is multiplied by the rate of change of velocity in the y direction over time; represents the convection term, describing the momentum transfer in the y direction due to fluid flow; div(μgradv) represents the diffusion term, describing the momentum dissipation in the y direction due to the viscous effect; represents the pressure gradient term, which shows how the change of pressure in the y direction affects the acceleration of the fluid; S v represents the external source term or volume force term in the y direction, representing the external force acting on the fluid; The mass representing the density is multiplied by the rate of change of velocity in the z direction over time; represents the convection term, which describes the momentum transfer in the z direction due to fluid flow; div(μgradw) represents the diffusion term, which describes the momentum dissipation in the z direction due to the viscous effect; represents the pressure gradient term, which shows how the change of pressure in the z direction affects the acceleration of the fluid; S w represents the external source term or body force term in the z direction, representing the external force acting on the fluid.

[0056] In one embodiment, the method of predicting the change of liquefied natural gas components during unloading of the LNG tanker based on the liquefied natural gas component data of the LNG tanker at the origin using a dynamic numerical simulation model of process operation to obtain the unloading liquefied natural gas component data of the LNG tanker includes:

[0057] According to the liquefied natural gas component data of the LNG tanker at the departure point, the dynamic numerical simulation model of the process operation is used to predict the changes in the liquefied natural gas components during unloading, and the prediction formula for the changes in each liquefied natural gas component with time, ambient temperature, and heat transfer coefficient is obtained, and then the liquefied natural gas component data of the unloading of the LNG tanker is obtained.

[0058] The present invention also provides a LNG tank truck unloading component prediction system, comprising:

[0059] The data acquisition module is used to: obtain the liquefied natural gas component data of the LNG tanker at the origin;

[0060] The first establishment module is used to: establish a cargo cavity model of the LNG tank truck, and add a first disturbance condition to the cargo cavity model to obtain a first outlet boundary condition and a model improvement strategy;

[0061] The second establishment module is used to: establish a numerical simulation model of the LNG tank truck based on the cargo cavity model of the LNG tank truck, and use the numerical simulation model to perform numerical simulation according to the model improvement strategy to obtain a second outlet boundary condition;

[0062] The third establishment module is used to: establish a dynamic numerical simulation model of the process flow based on the numerical simulation model of the LNG tanker and according to the first outlet boundary condition, and add the second disturbance condition to the dynamic numerical simulation model of the process flow to obtain the equipment influencing boundary condition and the equipment optimization control strategy;

[0063] The fourth establishment module is used to: establish a dynamic numerical simulation model of the process operation based on the dynamic numerical simulation model of the process flow, according to the second outlet boundary condition and the equipment optimization control strategy, and add a third disturbance condition to the dynamic numerical simulation model of the process operation to obtain the process influence boundary condition;

[0064] The prediction module is used to: predict the changes of liquefied natural gas components during unloading based on the liquefied natural gas component data of the LNG tanker at the origin, using the dynamic numerical simulation model of process operation, and obtain the unloading liquefied natural gas component data of the LNG tanker.

[0065] The present invention also provides an electronic device, comprising a processor and a memory storing a computer program, wherein the processor implements any of the above-mentioned methods for predicting the unloading components of an LNG tank truck when executing the computer program.

[0066] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, any of the above-mentioned methods for predicting the unloading components of an LNG tank truck is implemented.

[0067] The present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute any of the above-mentioned LNG tank truck unloading component prediction methods.

[0068] The present invention provides a method, system, equipment and medium for predicting the unloading components of an LNG tank truck. From the perspective of a mechanism model, a theoretically based LNG tank truck unloading component prediction model combining a cargo cavity model and a dynamic numerical simulation model is constructed. Through dynamic simulation, the unloading liquefied natural gas component data under various operating conditions (influencing factors include liquid level, temperature, pressure, etc.) can be accurately and efficiently predicted, which can achieve more accurate and low-cost LNG energy settlement, and help to discover potential risks in advance and take corresponding measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0070] Figure 1 A schematic flow chart of a method for predicting components during unloading of an LNG tank truck provided by the present invention.

[0071] Figure 2 This is an example diagram of the cargo compartment model of an LNG tanker.

[0072] Figure 3 This is a cross-sectional change cloud diagram of the numerical simulation model of the LNG tank truck.

[0073] Figure 4 This is a steady-state transportation model for LNG tank trucks.

[0074] Figure 5 It is a comparison diagram of the simulation effect and the experimental effect of this embodiment under different pressure conditions.

[0075] Figure 6 A structural schematic diagram of an LNG tank truck unloading component prediction system provided by the present invention.

[0076] Figure 7 This is a schematic structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0078] Combine the following Figure 1-Figure 7 The present invention describes the LNG tanker unloading component prediction method, system, device and medium. It should be noted that the LNG tanker unloading component prediction method provided by the present invention can be executed by any network side device / terminal side device that meets the technical requirements, such as an LNG tanker unloading component prediction device.

[0079] Figure 1Schematic diagram of the process of predicting the components of LNG tanker unloading provided by the present invention. Figure 1 The present invention provides a method for predicting components of LNG tank truck unloading, which may include:

[0080] Step S110, obtaining the liquefied natural gas component data of the LNG tank truck at the departure point. In this embodiment, the liquefied natural gas components of the LNG tank truck at the departure point include any one of the following items or any combination thereof: methane, ethane, propane, n-butane, isobutane, n-pentane, isopentane, nitrogen, and impurities (such as carbon dioxide, etc.).

[0081] In one embodiment, step S110 may obtain the liquefied natural gas composition data of the LNG tanker at the origin through a third-party testing agency, an LNG supplier, and an LNG receiving station or a liquefaction plant.

[0082] Step S120: Establish a cargo chamber model of the LNG tanker (essentially a physical model, see the example diagram Figure 2 ), and add the first disturbance condition to the cargo cavity model to obtain the first outlet boundary condition and model improvement strategy.

[0083] See also Figure 4 , a steady-state transportation model of LNG tanker can be established first, and then further research on changing working conditions can be carried out. The LNG guided from the LNG loading truck main pipe is loaded into the LNG tanker tank through the flow control valve (VLV-100), and the tank is then placed in the liquid level through the LNG tanker. The liquid level control valve (VLV-102) is adjusted to control the liquid level of the tank, and the pressure through the control valve (VLV-101) is used to control the pressure of the tank. When the liquid level and pressure data of the LNG tanker are consistent with the data of the actual LNG tanker, VLV-100, VLV-101 and VLV-102 will be closed synchronously to achieve the initial state of the LNG tanker tank. The control system includes a flow rate controller, a liquid level controller and a pressure controller, all of which are feedback controls. The energy stream Q-100 is used to simulate the heat exchange between the LNG tanker tank and the external environment. When the LNG tank truck starts working, the external heat is continuously transmitted, and the parameters such as the material temperature, pressure and liquid position in the LNG tank truck storage tank will gradually change.

[0084] The purpose of the disturbance experiment is to simulate various abnormal situations that may be encountered in actual operation in order to test the model's response and recovery capabilities to emergencies. Disturbance conditions refer to specific conditions imposed by humans, such as sudden changes in flow, temperature changes, pressure fluctuations, etc. After the model is established, through these disturbance experiments, the parameter changes at the outlet of the system under different conditions can be observed to obtain boundary conditions. The first disturbance condition will also affect the temperature difference and fluid flow state inside and outside the tank, thereby affecting the calculation of the heat transfer coefficient. For example, temperature changes will cause changes in the temperature difference inside and outside the tank, and pressure fluctuations will affect the flow rate of the fluid, thereby affecting the heat transfer coefficient. The heat outside the tank is transmitted through the tank wall, causing the LNG to absorb heat and partially evaporate.

[0085] In one embodiment, step S120 may include:

[0086] Step S1201, adding a first disturbance condition to the cargo cavity model to obtain a first outlet boundary condition, and calculating the storage tank heat and heat transfer coefficient of the LNG tank truck;

[0087] Step S1202: obtaining a model improvement strategy for the cargo cavity model according to the storage tank heat and heat transfer coefficient of the LNG tank truck, wherein the model improvement strategy may include any one of the following or any combination thereof: increasing the thickness of the insulation layer, changing the heat transfer coefficient, and improving the insulation material of the storage tank.

[0088] Wherein, step S1201 may include:

[0089] Add the first disturbance condition to the cargo cavity model to obtain the first outlet boundary condition;

[0090] According to the first outlet boundary condition, the heat transfer coefficient outside the cryogenic storage tank, the convection heat transfer coefficient inside the cryogenic storage tank, the apparent thermal conductivity of each layer of the tank wall, the thickness of each layer of the tank wall, the ambient temperature outside the tank, the average temperature of the cryogenic liquid in the tank, and the heat transfer surface area of ​​the cryogenic storage tank are obtained;

[0091] According to the heat transfer coefficient outside the cryogenic tank, the convection heat transfer coefficient inside the cryogenic tank, the apparent thermal conductivity of each layer of the tank wall, and the thickness of each layer of the tank wall, the heat transfer coefficient of the LNG tank truck is obtained. The expression of the heat transfer coefficient of the LNG tank truck is:

[0092]

[0093] In the expression of the heat transfer coefficient of the LNG tanker, K represents the heat transfer coefficient of the LNG tanker, A represents the heat transfer surface area of ​​the cryogenic storage tank, m 2 ;h 1 Indicates the heat transfer coefficient outside the cryogenic tank, including the convection heat transfer coefficient h c and the radiation heat transfer coefficient h r , W / (m·K); h 2Indicates the convection heat transfer coefficient in the low-temperature storage tank, W / (m 2 ·K);λ i Indicates the apparent thermal conductivity of each layer of the tank wall W / (m·K); δ i Indicates the thickness of each layer of the tank wall, m;

[0094] According to the heat transfer coefficient of the LNG tank truck, combined with the ambient temperature outside the tank, the average temperature of the cryogenic liquid in the tank, and the heat transfer surface area of ​​the cryogenic tank, the tank heat of the LNG tank truck is obtained. The expression of the tank heat of the LNG tank truck is:

[0095]

[0096] In the expression of the storage tank heat of the LNG tank truck, Q represents the storage tank heat of the LNG tank truck; A represents the heat transfer surface area of ​​the low-temperature storage tank, m 2 ;h 1 Indicates the heat transfer coefficient outside the cryogenic tank, including the convection heat transfer coefficient h c and the radiation heat transfer coefficient h r ,W / (m 2 ·K);h 2 Indicates the convection heat transfer coefficient in the low-temperature storage tank, W / (m 2 ·K);λ i Indicates the apparent thermal conductivity of each layer of the tank wall W / (m·K); δ i Indicates the thickness of each layer of the tank wall, m; t f1 Indicates the ambient temperature outside the tank, K; t f2 Represents the average temperature of the cryogenic liquid in the tank, K.

[0097] Among them, the convection heat transfer from the tank wall of a large cryogenic storage tank to the surrounding environment belongs to forced convection heat transfer. Therefore, the external convection heat transfer coefficient of the cryogenic storage tank h c Should be expressed as:

[0098]

[0099] in,

[0100] In the formula, λ 气 represents the thermal conductivity of air, W / (m·K); D represents the diameter of the tank, m; Re represents the Reynolds number, dimensionless; v 气 Indicates wind speed, calculated with reference to the average wind speed in the coldest month of the year, m / s; μ 气Represents the fluid viscosity of air; C represents the empirical constant or convective heat transfer coefficient, which depends on factors such as the properties of the fluid, the flow state and the geometric shape of the heat transfer surface. This constant is usually determined experimentally and is used to adjust the difference between theoretical calculations and actual measurement results; n represents the influence of the Reynolds number on the convective heat transfer coefficient. Under different flow conditions, the efficiency of convective heat transfer is different, so the value of n will vary according to actual conditions.

[0101] Convective heat transfer coefficient h in cryogenic tank 2 The calculation expression is as follows:

[0102]

[0103] Where Nu = C (Gr·Pr) n ,

[0104]

[0105] Where β represents the fluid expansion coefficient, 1 / K; ρ represents the fluid density, kg / m 3 ; l represents characteristic length, m; ΔT represents temperature difference, K; μ represents fluid viscosity, kg / (m·s); λ represents fluid thermal conductivity, W / (m·K); C p represents the specific heat capacity of the fluid at constant pressure, J / (kg·K); C, n represents constants. g represents the acceleration of gravity, Nu represents the Nusselt number, which is a dimensionless number used to quantify the intensity of convective heat transfer in fluid flow. It is defined as the ratio of the convective heat transfer coefficient to the thermal conductivity and the characteristic length. The larger the Nusselt number, the stronger the convective heat transfer. Pr represents the Prandtl number, which is also a dimensionless number. It is the ratio of the momentum diffusivity to the heat diffusivity. It is defined as the product of the dynamic viscosity and the thermal diffusivity divided by the thermal conductivity. The Prandtl number reflects the relationship between the velocity gradient and the temperature gradient inside the fluid and is an important parameter for describing the flow and heat transfer characteristics of the fluid. Gr represents the Glashof number, which is a dimensionless number used to evaluate the intensity of natural convection heat transfer. It is defined as the ratio of the buoyancy force to the inertial force. The larger the Glashof number, the more significant the natural convection heat transfer.

[0106] External radiation heat transfer coefficient of cryogenic storage tank h r The expression is:

[0107]

[0108] Where, t hi Indicates the average monthly air temperature of the coldest month, K; t w1i Represents the average temperature of the outer wall of the cryogenic tank, K. q s It represents the solar radiation heat per unit area, that is, solar irradiance, which reflects the energy density of sunlight when it hits the ground or the surface of an object.

[0109] In this embodiment, the simulation change strategy is to improve the safety of the system according to the simulation results. For example, if the simulation results show that the evaporation rate of LNG is too high, the model improvement strategy may include increasing the thickness of the insulation layer, changing the heat transfer coefficient or improving the insulation material of the storage tank.

[0110] Step S130: Based on the cargo cavity model of the LNG tanker, a numerical simulation model of the LNG tanker is established (essentially a fluid mechanics model, with a pressure cloud diagram of the cross-section change shown in FIG. Figure 3 ), and use the numerical simulation model to perform numerical simulation according to the model improvement strategy to obtain the second outlet boundary condition.

[0111] Because after implementing the model improvement strategy, the temperature and pressure at the outlet of the cargo chamber model of the LNG tank truck may change, and these changes constitute the second outlet boundary conditions.

[0112] In one embodiment, step S130 may include:

[0113] Step S1301: Perform mesh quality detection and independence verification on the cargo cavity model of the LNG tank truck.

[0114] When checking the mesh quality with a fluid mechanics model, the range of orthogonal quality values ​​is 0 to 1, where values ​​close to 0 correspond to low quality; the range of orthogonal skew values ​​is 0 to 1, where values ​​close to 1 correspond to low quality. The inspection results show that the minimum orthogonal quality of the mesh is 1.0, the maximum orthogonal skew is 4.62376e-11, and the maximum aspect ratio is 4.12311, indicating that the mesh quality is high. The density of the mesh will affect the results of the numerical calculation. The simulation results are meaningful only when the number of meshes increases but the change in the calculation results is small. However, the higher the number of meshes, the better. The more meshes there are, the more CPU usage the computer will have, and the longer the calculation time will be.

[0115] This embodiment performs independence tests for five grid numbers, namely 6760, 8500, 10525, 17040, and 26632. The initial transient mass flow rate is greatly affected by the grid size, but the calculation results converge to almost the same value after running for 18 hours.

[0116] Step S1302: determine the equation satisfied by the free liquid surface boundary. In this embodiment, the equation satisfied by the free liquid surface boundary is:

[0117]

[0118]

[0119] In the equation satisfied by the free surface boundary, c represents the shape or position of the liquid surface, which in this equation represents the free surface, and the free surface refers to the interface where the liquid and the gas contact; t represents time; n represents the direction perpendicular to the free surface; ρ represents the density of the fluid, which is the ratio of the mass of a substance to its volume, and directly affects the inertial properties of the fluid; ν represents the velocity, which describes the velocity of the fluid micro-group; Represents the partial derivative operator of a variable, which is used to describe the rate of change of the variable over time or space; V n represents the normal velocity of the free liquid surface; V c represents the tangential velocity of the free liquid surface; P represents the fluid pressure of the free liquid surface; P 0 represents the atmospheric pressure at the free surface; that is, It represents the rate of change of velocity along the normal direction with time; It represents the rate of change of velocity along the tangent direction in the normal direction; It represents the rate of change of the velocity along the normal direction in the normal direction. Among them, the wall condition satisfies the non-slip condition: V n ' represents the normal velocity to the wall; V c ' represents the wall tangential velocity.

[0120] Step S1303: Based on the cargo cavity model of the LNG tank truck after mesh quality detection and independence verification, and in combination with the equations satisfied by the free liquid surface boundary, a numerical simulation model of the LNG tank truck is established using the VOF method (Volume of Fluid Method).

[0121] The VOF method defines the moving interface space grid as a fluid volume function and constructs the development equation of the fluid volume function.

[0122] The VOF model achieves phase tracking between two phases by solving the continuity equation of each phase. The continuity equation of the qth phase fluid is as follows:

[0123]

[0124] The volume fraction equation is:

[0125]

[0126] In the formula, ρ q represents the density of the single-phase fluid of the qth phase, kg / m3; α q represents the volume fraction of the single-phase fluid of the qth phase, a dimensionless number; v q represents the velocity of the single-phase fluid of the qth phase, m / s; m pq Indicates the mass change from the pth phase to the qth phase, kg / s / m3; m pqRepresents the mass change from the qth phase to the pth phase, kg / s / m3.

[0127] In this embodiment, the Volume of Fluid model in the multiphase flow model is opened, and the Euler phase number is set to 2, wherein liquid methane is the first phase and gaseous methane is the second phase. The evaporation-condensation model for controlling the phase change is set to the Lee model.

[0128] The VOF method is used to track the free boundary surface. The fluid volume fraction F is the core content of the VOF method. When F = 1, it means that the unit is filled with fluid; when F = 0, it is an empty unit. The shape of the free interface can be obtained by using the gradient of F and the values ​​of each unit. F satisfies the control equation:

[0129]

[0130] Where: t represents time; u and v represent the speed in the x and y directions respectively.

[0131] Step S1304: Based on the numerical simulation model of the LNG tanker, a turbulence model is set.

[0132] The turbulence model is a set of closed equations used to describe the average turbulence quantity, which is established by combining the Reynolds mean motion equation and the pulsating motion equation and relies on a series of assumptions proposed by theory and experience.

[0133] In this embodiment, the expression of the turbulence model includes the turbulent kinetic energy k equation and the dissipation rate ε equation, wherein the turbulent kinetic energy k equation is:

[0134]

[0135] The dissipation rate ε equation is:

[0136]

[0137] In the formula, C 1ε , C 2ε and C 3ε represents the empirical constant, C 1ε =1.44, C 2ε =1.92, C 3ε =0.09;σ k , σ ε represents the Prandtl number corresponding to the turbulent kinetic energy k and the dissipation rate ε, σ k =1.0,σ ε =1.3; ρ represents the fluid density, in kg / m 3 ; represents the body derivative, which represents the rate of change of turbulent kinetic energy at a point in the control volume with time; μ represents the dynamic viscosity coefficient, also referred to as the viscosity coefficient or viscosity, which is a physical quantity that describes the resistance to fluid flow. It represents the force required for relative movement of fluid layers with a unit velocity difference per unit area due to internal friction; μ t The turbulent viscosity coefficient, also known as eddy viscosity or eddy viscosity, is a parameter that characterizes the interaction between fluid particles in turbulent motion; σ k The Prandtl number represents the turbulent kinetic energy, which determines the diffusion degree of turbulent kinetic energy in different directions; G k represents the turbulent kinetic energy generation term due to the mean velocity gradient, which is usually related to the Reynolds stress; G b represents the term of turbulent kinetic energy generation due to buoyancy; ε represents the turbulent kinetic energy dissipation rate, which represents the rate at which turbulent kinetic energy is dissipated in small-scale vortices; Y M represents the contribution of pulsating expansion to the total dissipation rate in compressible turbulent flow; Represents the spatial coordinate x i The partial derivative of represents the rate of change of dissipation rate with time; Denotes the turbulent kinetic energy k versus the spatial coordinate x i The partial derivative of describes the distribution of turbulent kinetic energy in space, that is, the changing trend of turbulent kinetic energy at different positions; The dissipation rate ε varies with the spatial position x i , that is, the changing trend of the dissipation rate at different positions.

[0138] This embodiment adopts the second-order upwind format. In this method, the second-order accuracy value of the unit surface is realized by solving the Taylor expansion at the unit center, and the value at the unit surface is calculated using the multidimensional linear reconstruction method. φ is calculated by the following equation f :

[0139]

[0140] When it comes to multidimensional linear reconstruction, it is usually necessary to improve the accuracy of the numerical solution, where φ f It represents the value of a physical quantity (such as velocity, pressure, etc.) on the surface of the unit, and φ is the value of the physical quantity inside the unit. is the gradient of the physical quantity, and ΔS is the distance vector from the inside of the unit to the surface of the unit. In the discretization process of the finite volume method or other numerical methods, in order to reduce numerical errors and improve the accuracy of the calculation, it is often necessary to make a more accurate estimate of the variable values ​​on the unit boundary. In this case, it is an effective method to use the known information and gradient information inside the unit for linear reconstruction. For example, when solving the Navier-Stokes equations, it is necessary to know the values ​​of the flow velocity and other related variables on each control volume interface. Through the above formula, the approximate values ​​of these interfaces can be calculated based on the values ​​of the unit center and its gradient, thereby obtaining a more accurate numerical solution.

[0141] During the evaporation of the liquid in the tank and the discharge of the evaporated gas, the total mass of the system remains unchanged.

[0142] Liquid mass conservation:

[0143]

[0144] Gas phase mass conservation:

[0145]

[0146] Conservation of mass of mixture:

[0147]

[0148] in, is the mass average velocity, ρm represents the average density,

[0149]

[0150] ρ m =α l ρ l +α v ρ v

[0151] In the formula, α represents the volume fraction of each phase, and the subscripts v and l represent the gas phase and liquid phase respectively; S m Represents a mass source term.

[0152] Step S1305: Based on the numerical simulation model of the LNG tanker, a phase change model is set. In this embodiment, the phase change model is an evaporation-condensation model, preferably a Lee model.

[0153] Cryogenic liquid evaporation is a phase change process in which a substance is transformed from a liquid to a gas. The Lee model used in this embodiment is a physically based evaporation-condensation model that can be used together with the VOF model. In the Lee model, positive mass transfer is defined as an evaporation process, and the mass transfer process between the gas-liquid phase (i.e., the evaporation-condensation process) is controlled by the vapor transfer equation:

[0154]

[0155] In the formula, ρ v Indicates gas density, kg / m 3 ; Indicates gas phase velocity, m / s; Indicates the mass transfer rate from gas phase to liquid phase during evaporation, kg / s / m 3 ; Indicates the mass transfer rate from liquid to gas during the condensation process, kg / s / m 3 ; a v It represents the effective diffusion coefficient or volume fraction of steam, which reflects the distribution of steam in the medium; represents the gradient operator, which is used to calculate the spatial rate of change of a scalar or to find the direction of maximum increase in a vector field; represents the partial derivative with respect to time t.

[0156] When T l >T sat , that is, when evaporating:

[0157]

[0158] When T l <T sat , that is, when condensing:

[0159]

[0160] The evaporation-condensation flux is given by the plane interface kinetic theory:

[0161]

[0162] Under saturated conditions, relate pressure to temperature:

[0163]

[0164] Therefore, as long as the saturation condition is close, we will get:

[0165]

[0166] Combining them gives:

[0167]

[0168] In the model, it is assumed that all bubbles have the same diameter, so the interfacial area density is given by the following formula:

[0169]

[0170] Where coef f is the relaxation factor, 1 / s; R is the universal gas constant, R = 8.314472 J·mol -1 ·K -1 ; β represents the regulation coefficient of the part of the steam molecules that enter the liquid surface and are adsorbed by the liquid surface, which is determined by the regulation coefficient and the physical properties of the steam. When it is close to the equilibrium state, β = 1.0; P * represents the steam partial pressure at the steam side interface, Pa; L represents the latent heat, J / kg; d b Indicates the bubble diameter, m; T sat represents the saturation temperature, K.

[0171] According to the above calculation formula, the relaxation factor coef f can be expressed as follows:

[0172]

[0173] The flow change caused by the inertial force is:

[0174]

[0175] ρ=ρ H =ρ f =βρ H +(1-β)ρ L ,

[0176] In the formula, v l Indicates the liquid flow rate, v g Indicates gas flow rate in m / s 2 ρ H represents the homogeneous density, ρ f Indicates true density in kg / m 3 ; β represents the volumetric gas content; ρ g represents the gas density; ρ l Indicates the density of the liquid in kg / m 3 .

[0177] The Lee model considers the impact of various factors on the evaporation of LNG tankers, such as LNG physical properties, meteorological conditions, and transportation environment. By simulating the evaporation rate of LNG under different transportation conditions, the impact of different parameters on the changes in LNG components is analyzed.

[0178] Step S1306: Based on the numerical simulation model of the LNG tanker, set the physical property parameters of LNG, wherein the physical property parameters of LNG include any one of the following or any combination thereof: density, specific heat, heat transfer coefficient, and dynamic viscosity.

[0179] In this embodiment, methane is used instead of LNG for simulation. The physical property parameters of LNG include the physical property parameters of liquid methane and the physical property parameters of gaseous methane. The physical property parameter settings of liquid methane include:

[0180] density:

[0181]

[0182] Specific Heat:

[0183]

[0184] Heat transfer coefficient:

[0185]

[0186] Dynamic viscosity:

[0187]

[0188] The physical property parameter settings of gaseous methane include:

[0189] density:

[0190]

[0191] Specific Heat:

[0192]

[0193] Heat transfer coefficient:

[0194]

[0195] Dynamic viscosity:

[0196]

[0197] Among them, the relationship between the phase transition temperature and pressure of liquid methane is:

[0198]

[0199] In the formula, represents the phase transition temperature of liquid methane, and P represents the pressure of liquid methane.

[0200] The fitting correlation coefficient R of the above empirical relationships 2 All are greater than 0.999.

[0201] Step S1307, unify the formats between the models on the numerical simulation model of the LNG tank truck into the QUICK format, and use the numerical simulation model of the LNG tank truck to perform numerical simulation according to the model improvement strategy to obtain the second outlet boundary condition. The simulated flow direction of this embodiment is consistent with the grid division direction, and the QUICK format has higher accuracy. Therefore, in this embodiment, the discretization methods of the momentum equation, volume fraction equation, turbulence dissipation equation, turbulent kinetic energy equation and energy equation in the simulation all adopt the QUICK format. In addition, because the influence of gravity is considered in the simulation process, the discretization method of the pressure equation in this embodiment adopts the Body Force Weighted format.

[0202] To ensure the simulation accuracy, the convergence criterion in the simulation is that the iterative residual values ​​of all control equations are less than 1×10-6. In order to speed up the calculation and ensure the stability of the calculation, this embodiment uses the SIMPLEC algorithm in the simulation, and sets the sub-relaxation factor of pressure to 0.4, the sub-relaxation factor of momentum to 0.6, and other default settings.

[0203] Step S140: Based on the numerical simulation model of the LNG tanker and according to the first outlet boundary condition, a dynamic numerical simulation model of the process flow is established, and a second disturbance condition is added to the dynamic numerical simulation model of the process flow to obtain the equipment influencing boundary condition and the equipment optimization control strategy.

[0204] Monitor the operation of important nodes and equipment to obtain the boundary conditions that affect the experimental device. For example, in the experiment of LNG tank trucks, if high temperature disturbance conditions are applied, expansion or stress changes of the tank material may be observed. And give the corresponding equipment optimization control strategy; the equipment optimization control strategy is a control scheme designed to improve the performance of the model based on simulation or experimental results. In the operation of LNG tank trucks, the equipment optimization control strategy may include automatically adjusting the operation of the cooling system according to the ambient temperature.

[0205] Dynamic thermal simulation of process flow and analysis of material flow, heat transfer and mass transfer in a specific process flow. The dynamic data simulation model covers the entire process flow, including all equipment and pipelines, as well as the interactions between them.

[0206] In one embodiment, the dynamic numerical simulation model of the process flow includes a continuity equation and a momentum transport equation between devices, wherein the continuity equation is:

[0207]

[0208] The momentum transport equation includes:

[0209] x-momentum equation:

[0210] The y-momentum equation is:

[0211] The z-momentum equation:

[0212] Where p represents the fluid pressure; S u , S v and S w represents the generalized force of the fluid; ρ represents the fluid density; u represents the velocity component in the x direction; v represents the velocity component in the y direction; w represents the velocity component in the z direction; t represents time; div represents the divergence operator, which is used to calculate the divergence rate of the vector field; μ represents the dynamic viscosity coefficient, which represents the ability of the fluid to resist shear deformation; Gradient tensor representing velocity gradient; S u represents the external source term or volume force term in the x direction, representing the external force acting on the fluid; The mass representing the density multiplied by the velocity in the x direction changes with time, that is, the increase or decrease in the mass of the fluid passing through a certain point per unit time; represents the convection term, which describes the momentum transfer in the x direction due to fluid flow; div(μgradu) represents the diffusion term, which describes the momentum dissipation in the x direction due to the viscous effect; represents the pressure gradient term, which shows how changes in pressure in the x-direction affect the acceleration of the fluid; The mass representing the density is multiplied by the rate of change of velocity in the y direction over time; represents the convection term, describing the momentum transfer in the y direction due to fluid flow; div(μgradv) represents the diffusion term, describing the momentum dissipation in the y direction due to the viscous effect; represents the pressure gradient term, which shows how the change of pressure in the y direction affects the acceleration of the fluid; S v represents the external source term or volume force term in the y direction, representing the external force acting on the fluid; The mass representing the density is multiplied by the rate of change of velocity in the z direction over time; represents the convection term, which describes the momentum transfer in the z direction due to fluid flow; div(μgradw) represents the diffusion term, which describes the momentum dissipation in the z direction due to the viscous effect; represents the pressure gradient term, which shows how the change of pressure in the z direction affects the acceleration of the fluid; S w represents the external source term or body force term in the z direction, representing the external force acting on the fluid.

[0213] Step S150, based on the dynamic numerical simulation model of the process flow, according to the second outlet boundary condition and the equipment optimization control strategy, a dynamic numerical simulation model of the process operation is established, and a third disturbance condition is added to the dynamic numerical simulation model of the process operation to obtain the process influencing boundary condition.

[0214] In one embodiment, step S150 can change different operating conditions based on the dynamic numerical simulation model of the process flow and the liquefied natural gas component data of the LNG tank truck at the origin, as well as the first outlet boundary condition and the second outlet boundary condition, to study the trend change of the overall energy in the LNG tank truck and several key factors affecting the energy change of the LNG tank truck.

[0215] In this step, the SRK equation can be used to predict the phase equilibrium of liquid and gas:

[0216]

[0217] In the formula, P represents pressure (Pascal, Pa), R represents the ideal gas constant, T represents absolute temperature (K), and v represents molar volume (cubic meters per mole). a and b represent parameters related to the substance, which can be calculated through critical properties: a = 0.42748·R 2 T c 2.5 / Pc.a=0.42748·R 2 T c 2.5 / Pc.b=0.08664·RTc / Pcb. Tc represents the critical temperature (K), and Pc represents the critical pressure (Pa).

[0218] For mixtures, aa and bb can be calculated using the mixing rules. For binary mixtures, the following rules can be used:

[0219]

[0220] Where xi and xj represent the mole fractions of the i-th and j-th components in the mixture, ai and aj represent the a parameters of the pure components, and kij represents the interaction parameter, which is used to describe the non-ideal mixing behavior between different components.

[0221] The present invention essentially constructs an LNG tank truck unloading component prediction model that combines a cargo cavity model and a dynamic numerical simulation model, and is used to predict the unloading liquefied natural gas components of the LNG tank truck from the perspective of a mechanism model.

[0222] Step S160: Based on the liquefied natural gas component data of the LNG tanker at the departure point, the dynamic numerical simulation model of the process operation is used to predict the changes in the liquefied natural gas components during unloading, so as to obtain the liquefied natural gas component data of the unloaded LNG tanker.

[0223] In one embodiment, step S160 can predict the changes in liquefied natural gas components during unloading based on the liquefied natural gas component data of the LNG tanker at the departure point using a dynamic numerical simulation model of process operation, obtain a prediction formula for the changes in each liquefied natural gas component with time, ambient temperature, and heat transfer coefficient, and then obtain the liquefied natural gas component data when unloading the LNG tanker.

[0224] In the simulation of this embodiment, pure methane is used instead of LNG, the required heat is smaller, the amount of BOG obtained by evaporation is larger, and the simulated gas phase space pressure is larger.

[0225] On the one hand, the BOG outlet pressure in the simulation is a certain value, while the opening and closing pressure of the safety valve at the BOG outlet of the actual LNG storage tank is within a certain range, the BOG discharge is large, and the actual measured gas phase pressure is relatively smaller. Therefore, it can be considered that the simulated value and the measured value of the LNG storage tank are basically consistent after the BOG mass flow rate is stabilized, that is, this model can accurately simulate the LNG evaporation characteristics in the storage tank.

[0226] By simulating and calculating the rate of BOG mass flow rate after stabilization and the temperature, pressure and liquid level after stabilization, the influence of the three factors on the evaporation characteristics is analyzed.

[0227] 1. Ambient (external) temperature: 141.905w; 128.5177w; 115.130w;

[0228] 2. Initial liquid level (filling rate): 50%; 60%; 70%; 80%;

[0229] 3. Pressure: -156℃50kpa; -155℃62kpa; -154℃74kpa.

[0230] As the ambient temperature outside the tank increases, the temperature stratification of the gas phase space and the gas-liquid coexistence area at the interface gradually widen with the increase of ambient temperature. This indicates that the increase in ambient temperature increases the heat entering the tank and the evaporation of LNG in the tank.

[0231] In this embodiment, the prediction formulas for the changes of various liquefied natural gas components with time, ambient temperature, and heat transfer coefficient include:

[0232] Prediction formula of temperature change with time, ambient temperature and heat transfer coefficient:

[0233]

[0234] Prediction formula of pressure change with time, ambient temperature and heat transfer coefficient:

[0235]

[0236] Prediction formula of liquid level changing with time, ambient temperature and heat transfer coefficient:

[0237]

[0238] Where t is the time variable, in days. T is the ambient temperature variable, in °C. U is the heat transfer coefficient variable, in W / (m 2 *K).

[0239] The predicted relationship between gaseous components and time, ambient temperature and heat transfer coefficient is:

[0240] Methane:

[0241]

[0242] Ethane:

[0243]

[0244] Propane:

[0245]

[0246] n-Butane:

[0247]

[0248] Isobutane:

[0249]

[0250] n-Pentane:

[0251]

[0252] Isopentane:

[0253]

[0254] Nitrogen:

[0255]

[0256] Where t is the time variable, in days. T is the ambient temperature variable, in °C. U is the heat transfer coefficient variable, in W / (m 2 *K).

[0257] Prediction formula of liquid components with time, ambient temperature and heat transfer coefficient:

[0258] Methane:

[0259]

[0260] Ethane:

[0261]

[0262] Propane:

[0263]

[0264] n-Butane:

[0265]

[0266] Isobutane:

[0267]

[0268] n-Pentane:

[0269]

[0270] Isopentane:

[0271]

[0272] Nitrogen:

[0273]

[0274] See Figure 5 , this embodiment compares and analyzes the simulation results with the field test data: by comparing the temperature and pressure simulation results with the two experimental models, the correctness of the established numerical model is verified. The absolute value of the relative error of the simulation results of the LNG tank truck unloading component prediction model with a theoretical basis combined with the cargo cavity model and the dynamic numerical simulation model constructed in this embodiment is less than 5%, which is highly consistent with the experimental results. It can be seen that the low-temperature liquid evaporation model established by the fluid mechanics model and the thermodynamic model can be used to simulate the real evaporation process.

[0275] The LNG tank truck unloading component prediction method provided by the present invention constructs a LNG tank truck unloading component prediction model with a theoretical basis that combines a cargo cavity model and a dynamic numerical simulation model from the perspective of a mechanism model. Through dynamic simulation, the unloading liquefied natural gas component data under various operating conditions (influencing factors include liquid level, temperature, pressure, etc.) can be accurately and efficiently predicted, which can achieve more accurate and low-cost LNG energy settlement, and help to discover potential risks in advance and take corresponding measures.

[0276] The LNG tank truck unloading component prediction system provided by the present invention is described below. The LNG tank truck unloading component prediction system described below and the LNG tank truck unloading component prediction method described above can be referenced to each other.

[0277] Reference Figure 6The present invention provides a LNG tank truck unloading component prediction system, which may include:

[0278] The data acquisition module is used to: obtain the liquefied natural gas component data of the LNG tanker at the origin;

[0279] The first establishment module is used to: establish a cargo cavity model of the LNG tank truck, and add a first disturbance condition to the cargo cavity model to obtain a first outlet boundary condition and a model improvement strategy;

[0280] The second establishment module is used to: establish a numerical simulation model of the LNG tank truck based on the cargo cavity model of the LNG tank truck, and use the numerical simulation model to perform numerical simulation according to the model improvement strategy to obtain a second outlet boundary condition;

[0281] The third establishment module is used to: establish a dynamic numerical simulation model of the process flow based on the numerical simulation model of the LNG tanker and according to the first outlet boundary condition, and add the second disturbance condition to the dynamic numerical simulation model of the process flow to obtain the equipment influencing boundary condition and the equipment optimization control strategy;

[0282] The fourth establishment module is used to: establish a dynamic numerical simulation model of the process operation based on the dynamic numerical simulation model of the process flow, according to the second outlet boundary condition and the equipment optimization control strategy, and add a third disturbance condition to the dynamic numerical simulation model of the process operation to obtain the process influence boundary condition;

[0283] The prediction module is used to: predict the changes of liquefied natural gas components during unloading based on the liquefied natural gas component data of the LNG tanker at the origin, using the dynamic numerical simulation model of process operation, and obtain the unloading liquefied natural gas component data of the LNG tanker.

[0284] Figure 7 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 7 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the LNG tank truck unloading component prediction method, which includes:

[0285] Obtain liquefied natural gas composition data from LNG tankers at the point of origin;

[0286] The cargo cavity model of the LNG tanker is established, and the first disturbance condition is added to the cargo cavity model to obtain the first outlet boundary condition and model improvement strategy;

[0287] Based on the cargo cavity model of the LNG tank truck, a numerical simulation model of the LNG tank truck is established, and the numerical simulation model is used to perform numerical simulation according to the model improvement strategy to obtain the second outlet boundary condition;

[0288] Based on the numerical simulation model of the LNG tanker, a dynamic numerical simulation model of the process flow is established according to the first outlet boundary condition, and the second disturbance condition is added to the dynamic numerical simulation model of the process flow to obtain the equipment influencing boundary conditions and equipment optimization control strategy;

[0289] Based on the dynamic numerical simulation model of the process flow, according to the second outlet boundary condition and the equipment optimization control strategy, a dynamic numerical simulation model of the process operation is established, and a third disturbance condition is added to the dynamic numerical simulation model of the process operation to obtain the process influence boundary condition;

[0290] According to the liquefied natural gas composition data of the LNG tank truck at the departure place, the dynamic numerical simulation model of process operation is used to predict the changes of the liquefied natural gas composition during unloading, and the liquefied natural gas composition data of the unloading of the LNG tank truck is obtained.

[0291] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0292] On the other hand, the present invention further provides a computer program product, the computer program product comprising a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer can execute the LNG tank truck unloading component prediction method provided by the above methods, the method comprising:

[0293] Obtain liquefied natural gas composition data from LNG tankers at the point of origin;

[0294] The cargo cavity model of the LNG tanker is established, and the first disturbance condition is added to the cargo cavity model to obtain the first outlet boundary condition and model improvement strategy;

[0295] Based on the cargo cavity model of the LNG tank truck, a numerical simulation model of the LNG tank truck is established, and the numerical simulation model is used to perform numerical simulation according to the model improvement strategy to obtain the second outlet boundary condition;

[0296] Based on the numerical simulation model of the LNG tanker, a dynamic numerical simulation model of the process flow is established according to the first outlet boundary condition, and the second disturbance condition is added to the dynamic numerical simulation model of the process flow to obtain the equipment influencing boundary conditions and equipment optimization control strategy;

[0297] Based on the dynamic numerical simulation model of the process flow, according to the second outlet boundary condition and the equipment optimization control strategy, a dynamic numerical simulation model of the process operation is established, and a third disturbance condition is added to the dynamic numerical simulation model of the process operation to obtain the process influence boundary condition;

[0298] According to the liquefied natural gas composition data of the LNG tank truck at the departure place, the dynamic numerical simulation model of process operation is used to predict the changes of the liquefied natural gas composition during unloading, and the liquefied natural gas composition data of the unloading of the LNG tank truck is obtained.

[0299] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the LNG tank truck unloading component prediction method provided by the above methods, the method comprising:

[0300] Obtain liquefied natural gas composition data from LNG tankers at the point of origin;

[0301] The cargo cavity model of the LNG tanker is established, and the first disturbance condition is added to the cargo cavity model to obtain the first outlet boundary condition and model improvement strategy;

[0302] Based on the cargo cavity model of the LNG tank truck, a numerical simulation model of the LNG tank truck is established, and the numerical simulation model is used to perform numerical simulation according to the model improvement strategy to obtain the second outlet boundary condition;

[0303] Based on the numerical simulation model of the LNG tanker, a dynamic numerical simulation model of the process flow is established according to the first outlet boundary condition, and the second disturbance condition is added to the dynamic numerical simulation model of the process flow to obtain the equipment influencing boundary conditions and equipment optimization control strategy;

[0304] Based on the dynamic numerical simulation model of the process flow, according to the second outlet boundary condition and the equipment optimization control strategy, a dynamic numerical simulation model of the process operation is established, and a third disturbance condition is added to the dynamic numerical simulation model of the process operation to obtain the process influence boundary condition;

[0305] According to the liquefied natural gas composition data of the LNG tank truck at the departure place, the dynamic numerical simulation model of process operation is used to predict the changes of the liquefied natural gas composition during unloading, and the liquefied natural gas composition data of the unloading of the LNG tank truck is obtained.

[0306] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0307] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0308] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting components of LNG tanker unloading, characterized in that: include: Obtain liquefied natural gas composition data from LNG tankers at the point of origin; The cargo cavity model of the LNG tanker is established, and the first disturbance condition is added to the cargo cavity model to obtain the first outlet boundary condition and model improvement strategy; Based on the cargo cavity model of the LNG tank truck, a numerical simulation model of the LNG tank truck is established, and the numerical simulation model is used to perform numerical simulation according to the model improvement strategy to obtain the second outlet boundary condition; Based on the numerical simulation model of the LNG tanker, a dynamic numerical simulation model of the process flow is established according to the first outlet boundary condition, and the second disturbance condition is added to the dynamic numerical simulation model of the process flow to obtain the equipment influencing boundary conditions and equipment optimization control strategy; Based on the dynamic numerical simulation model of the process flow, according to the second outlet boundary condition and the equipment optimization control strategy, a dynamic numerical simulation model of the process operation is established, and a third disturbance condition is added to the dynamic numerical simulation model of the process operation to obtain the process influence boundary condition; According to the liquefied natural gas composition data of the LNG tanker at the origin, the dynamic numerical simulation model of process operation is used to predict the changes of the liquefied natural gas composition during unloading, and the liquefied natural gas composition data of the unloading of the LNG tanker is obtained.

2. The LNG tanker unloading component prediction method according to claim 1 is characterized in that: The method of establishing a cargo cavity model of an LNG tanker and adding a first disturbance condition to the cargo cavity model to obtain a first outlet boundary condition and a model improvement strategy includes: The first disturbance condition is added to the cargo cavity model to obtain the first outlet boundary condition, and the storage tank heat and heat transfer coefficient of the LNG tank truck are calculated; According to the storage tank heat and heat transfer coefficient of the LNG tank truck, the model improvement strategy of the cargo cavity model is obtained.

3. The LNG tanker unloading component prediction method according to claim 2 is characterized in that: The first disturbance condition is added to the cargo cavity model to obtain the first outlet boundary condition, and the storage tank heat and heat transfer coefficient of the LNG tank truck are calculated, including: Add the first disturbance condition to the cargo cavity model to obtain the first outlet boundary condition; According to the first outlet boundary condition, the heat transfer coefficient outside the cryogenic storage tank, the convection heat transfer coefficient inside the cryogenic storage tank, the apparent thermal conductivity of each layer of the tank wall, the thickness of each layer of the tank wall, the ambient temperature outside the tank, the average temperature of the cryogenic liquid in the tank, and the heat transfer surface area of ​​the cryogenic storage tank are obtained; The heat transfer coefficient of the LNG tank truck is obtained based on the heat transfer coefficient outside the cryogenic storage tank, the convection heat transfer coefficient inside the cryogenic storage tank, the apparent thermal conductivity of each layer of the tank wall, and the thickness of each layer of the tank wall; According to the heat transfer coefficient of the LNG tank truck, combined with the ambient temperature outside the tank, the average temperature of the cryogenic liquid in the tank, and the heat transfer surface area of ​​the cryogenic tank, the tank heat of the LNG tank truck is obtained; Among them, the expression of the heat transfer coefficient of the LNG tanker is: In the expression of the heat transfer coefficient of the LNG tanker, K represents the heat transfer coefficient of the LNG tanker, A represents the heat transfer surface area of ​​the cryogenic tank; h1 represents the heat transfer coefficient outside the cryogenic tank, including the convection heat transfer coefficient h c and the radiation heat transfer coefficient h r ,; h2 represents the convective heat transfer coefficient in the cryogenic storage tank; λ i Indicates the apparent thermal conductivity of each layer of the tank wall; δ i Indicates the thickness of each layer of the tank wall; The expression of the storage tank heat of the LNG tank truck is: In the expression of the heat of the LNG tank truck, Q represents the heat of the LNG tank truck; A represents the heat transfer surface area of ​​the cryogenic tank; h1 represents the external heat transfer coefficient of the cryogenic tank, including the convection heat transfer coefficient h c and the radiation heat transfer coefficient h r ,; h2 represents the convective heat transfer coefficient in the cryogenic storage tank; λ i Indicates the apparent thermal conductivity of each layer of the tank wall; δ i Indicates the thickness of each layer of the tank wall; t f1 Indicates the ambient temperature outside the tank; t f2 Indicates the average temperature of the cryogenic liquid in the tank.

4. The method for predicting the unloading components of an LNG tanker according to claim 3, characterized in that: Based on the cargo cavity model of the LNG tank truck, a numerical simulation model of the LNG tank truck is established, and the numerical simulation model is used to perform numerical simulation according to the model improvement strategy to obtain the second outlet boundary condition, including: Conduct mesh quality inspection and independence verification on the cargo cavity model of the LNG tanker; Determine the equations satisfied by the free surface boundary; Based on the cargo cavity model of the LNG tanker after mesh quality inspection and independence verification, combined with the equations satisfied by the free liquid surface boundary, the VOF method is used to establish the numerical simulation model of the LNG tanker. Based on the numerical simulation model of the LNG tanker, a turbulence model is set; Based on the numerical simulation model of the LNG tanker, a phase change model is set; Based on the numerical simulation model of the LNG tanker, the physical property parameters of LNG are set; The formats of various models on the numerical simulation model of the LNG tank truck are unified, and the numerical simulation model of the LNG tank truck is used to perform numerical simulation according to the model improvement strategy to obtain the second outlet boundary condition.

5. The method for predicting the unloading components of an LNG tanker according to claim 4, characterized in that: The equation satisfied by the free liquid surface boundary is: In the equations satisfied by the free surface boundary, c represents the shape or position of the free surface; t represents time; n represents the direction perpendicular to the free surface; ρ represents the fluid density; ν represents the velocity; V n represents the normal velocity of the free liquid surface; V c represents the tangential velocity of the free liquid surface; P represents the fluid pressure of the free liquid surface; P0 represents the atmospheric pressure of the free liquid surface; where the wall condition satisfies the non-slip condition: V n ' represents the normal velocity to the wall; V c ' represents the wall tangential velocity; The expression of the turbulence model includes the turbulent kinetic energy k equation and the dissipation rate ε equation, where the turbulent kinetic energy k equation is: The dissipation rate ε equation is: In the formula, C 1ε , C 2ε and C 3ε represents the empirical constant; σ k , σ ε represents the Prandtl number corresponding to the turbulent kinetic energy k and the dissipation rate ε; ρ represents the fluid density; represents the body derivative; μ represents the dynamic viscosity coefficient; μ t represents the turbulent viscosity coefficient; σ k Prandtl number representing the turbulent kinetic energy k; G k represents the term of turbulent kinetic energy generation due to the mean velocity gradient; G b represents the term of turbulent kinetic energy generation due to buoyancy; ε represents the turbulent kinetic energy dissipation rate; σ ε Prandtl number, which represents the turbulent kinetic energy dissipation rate ε; Y M It represents the contribution of pulsating expansion to the total dissipation rate in compressible turbulent flow.

6. The method for predicting the unloading components of an LNG tanker according to claim 5, characterized in that: The dynamic numerical simulation model of the process flow includes the continuity equation and momentum transport equation between the equipment, where the continuity equation is: The momentum transport equation includes: x-momentum equation: The y-momentum equation is: The z-momentum equation: Where p represents the fluid pressure; S u , S v and S w represents the generalized force of the fluid; ρ represents the fluid density; u represents the velocity component in the x direction; v represents the velocity component in the y direction; w represents the velocity component in the z direction; t represents time; div represents the divergence operator, which is used to calculate the divergence rate of the vector field; μ represents the dynamic viscosity coefficient, which represents the ability of the fluid to resist shear deformation; ▽u represents the gradient tensor of the velocity gradient; S u represents the external source term or body force term in the x-direction, representing the external force acting on the fluid; The mass representing the density multiplied by the velocity in the x direction changes with time, that is, the increase or decrease in the mass of the fluid passing through a certain point per unit time; represents the convection term, describing the momentum transfer in the x direction due to fluid flow; div(μgradu) represents the diffusion term, describing the momentum dissipation in the x direction due to the viscous effect; represents the pressure gradient term, which shows how changes in pressure in the x-direction affect the acceleration of the fluid; The mass representing the density is multiplied by the rate of change of velocity in the y direction over time; represents the convection term, describing the momentum transfer in the y direction due to fluid flow; div(μgradv) represents the diffusion term, describing the momentum dissipation in the y direction due to the viscous effect; represents the pressure gradient term, which shows how the change of pressure in the y direction affects the acceleration of the fluid; S v represents the external source term or volume force term in the y direction, representing the external force acting on the fluid; The mass representing the density is multiplied by the rate of change of velocity in the z direction over time; represents the convection term, describing the momentum transfer in the z direction due to fluid flow; div(μgradw) represents the diffusion term, describing the momentum dissipation in the z direction due to the viscous effect; represents the pressure gradient term, which shows how the change of pressure in the z direction affects the acceleration of the fluid; S w represents the external source term or body force term in the z direction, representing the external force acting on the fluid.

7. The method for predicting the unloading components of an LNG tanker according to claim 6, characterized in that: The method of using a dynamic numerical simulation model of process operation to predict the change of liquefied natural gas components during unloading based on the liquefied natural gas component data of the LNG tanker at the origin to obtain the liquefied natural gas component data of the unloading LNG tanker includes: According to the liquefied natural gas component data of the LNG tanker at the departure point, the dynamic numerical simulation model of the process operation is used to predict the changes in the liquefied natural gas components during unloading, and the prediction formula for the changes in each liquefied natural gas component with time, ambient temperature, and heat transfer coefficient is obtained, and then the liquefied natural gas component data of the unloading of the LNG tanker is obtained.

8. A LNG tanker unloading component prediction system, characterized in that: include: The data acquisition module is used to: obtain the liquefied natural gas component data of the LNG tanker at the origin; The first establishment module is used to: establish a cargo cavity model of the LNG tank truck, and add a first disturbance condition to the cargo cavity model to obtain a first outlet boundary condition and a model improvement strategy; The second establishment module is used to: establish a numerical simulation model of the LNG tank truck based on the cargo cavity model of the LNG tank truck, and use the numerical simulation model to perform numerical simulation according to the model improvement strategy to obtain a second outlet boundary condition; The third establishment module is used to: establish a dynamic numerical simulation model of the process flow based on the numerical simulation model of the LNG tanker and according to the first outlet boundary condition, and add the second disturbance condition to the dynamic numerical simulation model of the process flow to obtain the equipment influencing boundary condition and the equipment optimization control strategy; The fourth establishment module is used to: establish a dynamic numerical simulation model of the process operation based on the dynamic numerical simulation model of the process flow, according to the second outlet boundary condition and the equipment optimization control strategy, and add a third disturbance condition to the dynamic numerical simulation model of the process operation to obtain the process influence boundary condition; The prediction module is used to: predict the changes of liquefied natural gas components during unloading based on the liquefied natural gas component data of the LNG tanker at the origin, using the dynamic numerical simulation model of process operation, and obtain the unloading liquefied natural gas component data of the LNG tanker.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for predicting the unloading components of an LNG tank truck as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the unloading composition of an LNG tank truck according to any one of claims 1 to 7 is implemented.

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