Multi-target optimization method for heat insulation structure of liquid hydrogen storage tank based on multi-node self-pressurization

Abstract

The invention relates to a multi-target optimization method for a heat insulation structure of a liquid hydrogen storage tank based on multi-node self-pressurization, and the method comprises the steps: constructing a digital model of the liquid hydrogen storage tank, carrying out the digital modeling of an inner tank of the liquid hydrogen storage tank based on a multi-node self-pressurization model, carrying out the digital modeling of an outer tank of the liquid hydrogen storage tank based on a quasi-two-dimensional model, the outer tank of the liquid hydrogen storage tank adopts a high-vacuum variable-density multi-layer heat insulation structure; by taking the position of a steam cooling screen of a high-vacuum variable-density multi-layer heat insulation structure, the number of interlayer heat insulation layers and the radius of an inner tank as optimization objects and taking the heat insulation performance, the manufacturing cost, the dormancy time and the excess temperature as optimization targets, establishing a multi-target optimization function and constraint conditions thereof based on the liquid hydrogen storage tank digital model; and solving the multi-objective optimization function to obtain an optimization result of the heat insulation structure of the liquid hydrogen storage tank. Compared with the prior art, the method has the advantages that the prediction precision of the real thermal behavior of the storage tank is high, and the optimal structure scheme considering the thermal performance and the economical efficiency can be obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of high-vacuum low-temperature thermal insulation technology, in particular to a multi-objective optimization method for a liquid hydrogen storage tank thermal insulation structure based on multi-node self-pressurization. BACKGROUND

[0002] Under the background of the increasing depletion of traditional fossil energy, global climate warming has become a major challenge to sustainable development. In order to cope with climate change and achieve the goal of carbon reduction, the large-scale use of hydrogen energy has gradually become a key way for countries to promote energy structure transformation. As a core link in the hydrogen energy industry chain, liquid hydrogen storage technology has attracted widespread attention due to its high efficiency, economy and safety. Due to the extremely low boiling point (about 20K) and large latent heat of vaporization of liquid hydrogen, liquid hydrogen storage tanks must be equipped with high-performance insulation systems to effectively reduce evaporation loss. Existing modeling of liquid hydrogen storage tanks is mostly based on three-zone models and one-dimensional models, with relatively poor modeling accuracy. Such methods have obvious limitations in describing the radial temperature gradient of the inner tank and the non-uniform heat transfer characteristics of the multi-layer insulation structure of the outer tank, resulting in insufficient calculation accuracy and difficulty in accurately reflecting the actual thermal behavior of the storage tank.

[0003] The existing Chinese patent application CN120493555A discloses a multi-objective optimization method for liquid hydrogen storage tank thermal insulation structure based on quasi-two-dimensional model and NSGA2 algorithm, which includes: digital modeling of variable density multi-layer insulation structure and vapor cooling screen of liquid hydrogen storage tank based on quasi-two-dimensional model; taking the position of vapor cooling screen and the number of layers of each layer of insulation layer as optimization objects, and taking the cost and thermal insulation performance of liquid hydrogen storage tank as optimization targets, a multi-objective optimization model is constructed; the optimal values of the position of vapor cooling screen and the number of layers of each layer of insulation layer are obtained by solving the multi-objective optimization model based on NSGA2 algorithm as the optimization results. However, the liquid hydrogen storage tank thermal insulation structure optimization method proposed by CN120493555A still has certain limitations. The quasi-two-dimensional heat transfer model used by it is mainly based on the steady-state or quasi-steady-state assumption, only the variable density multi-layer insulation structure and vapor cooling screen are digitally modeled, and the transient heat conduction characteristics and dynamic changes of temperature gradient of liquid hydrogen storage tank during long-term storage and self-pressurization are not fully considered. This simplified model cannot accurately reflect the evolution law of the temperature field of the inner and outer tanks of the liquid hydrogen storage tank over time under actual operating conditions, resulting in deviations between the optimization results and the real working conditions. In addition, this method does not establish the coupling heat transfer relationship between the inner and outer tanks of the liquid hydrogen storage tank, only the thermal analysis of the insulation layer and the vapor cooling screen is carried out at the structural level, and the lack of coupled modeling of the inner and outer tanks will lead to incomplete description of the thermal response characteristics, making it difficult to accurately evaluate the real thermal insulation performance of the storage tank system.

[0004] Therefore, it is necessary to model the liquid hydrogen storage tank more finely to realize high-precision prediction of the temperature distribution, heat flow transfer and evaporation loss process, and on this basis, carry out structural optimization design, so as to further improve the thermal performance and energy storage efficiency of the liquid hydrogen storage tank. SUMMARY

[0005] The purpose of the present application is to overcome the defects of the prior art and provide a multi-objective optimization method for the adiabatic structure of a liquid hydrogen storage tank based on multi-node self-pressurization, which has high prediction accuracy for the real thermal behavior of the storage tank and can obtain an optimal structure scheme that balances thermal performance and economic efficiency.

[0006] The purpose of the present application can be achieved by the following technical solutions: A multi-objective optimization method for the adiabatic structure of a liquid hydrogen storage tank based on multi-node self-pressurization, comprising the following steps: Constructing a digital model of the liquid hydrogen storage tank, wherein the inner tank of the liquid hydrogen storage tank is digitally modeled based on a multi-node self-pressurization model, and the outer tank of the liquid hydrogen storage tank is digitally modeled based on a quasi-two-dimensional model, and the outer tank of the liquid hydrogen storage tank adopts a high-vacuum variable-density multi-layer adiabatic structure; Taking the position of the vapor cooling screen of the high-vacuum variable-density multi-layer adiabatic structure, the number of layers of the interlayer insulation layer, and the radius of the inner tank as optimization objects, and taking the adiabatic performance, manufacturing cost, dormancy time and excess temperature as optimization targets, a multi-objective optimization function and its constraint conditions are established based on the digital model of the liquid hydrogen storage tank; The multi-objective optimization function is solved by using the NSGA-3 algorithm to obtain the optimization result of the adiabatic structure of the liquid hydrogen storage tank.

[0007] The above method introduces the coupling mechanism of the multi-node heat and mass transfer model of the inner tank and the quasi-two-dimensional variable-density multi-layer adiabatic model of the outer tank at the model level, and on this basis, takes the number of adiabatic layers, the position of the vapor cooling screen and the geometric parameters of the inner tank as optimization decision variables, constructs a multi-objective optimization system with the total cost of the storage tank, the adiabatic performance, the dormancy time and the excess temperature in the tank as optimization targets, and uses the NSGA-3 algorithm for global optimization to obtain an optimal solution that balances thermal performance and economic efficiency, and realizes the precise balance between thermal efficiency and cost of the adiabatic structure of the liquid hydrogen storage tank.

[0008] Further, the digital modeling of the inner tank of the liquid hydrogen storage tank based on the multi-node self-pressurization model specifically comprises: based on the conservation of mass and energy, the gas-phase multi-node heat and mass transfer process of the inner tank of the liquid hydrogen storage tank and the liquid-phase multi-node heat and mass transfer process of the inner tank of the liquid hydrogen storage tank are respectively expressed in a functional form.

[0009] Furthermore, the functional expressions describing the multi-node heat and mass transfer process in the liquid hydrogen storage tank include the mass conservation equation of the gas bulk layer in the multi-node self-pressurization model, the energy conservation equation of the gas bulk layer in the multi-node self-pressurization model, the mass conservation equation of the gas boundary layer in the multi-node self-pressurization model, the energy conservation equation of the gas boundary layer in the multi-node self-pressurization model, the mass conservation formula of the first control volume in contact between hydrogen and liquid hydrogen in the multi-node self-pressurization model, the energy conservation formula of the first control volume in contact between hydrogen and liquid hydrogen in the multi-node self-pressurization model, the mass conservation formula of the highest control volume of hydrogen in the multi-node self-pressurization model, and the energy conservation formula of the highest control volume of hydrogen in the multi-node self-pressurization model.

[0010] Furthermore, the functional expressions describing the multi-node heat and mass transfer process in the liquid phase of the liquid hydrogen storage tank include the mass conservation formula for the liquid bulk layer, the energy conservation equation for the liquid bulk layer, the mass conservation equation for the liquid boundary layer, the energy conservation equation for the liquid boundary layer, the mass conservation formula for the highest control volume of liquid hydrogen in contact with hydrogen, the energy conservation formula for the lowest control volume of liquid hydrogen, and the energy conservation formula for the lowest control volume of liquid hydrogen in contact with hydrogen.

[0011] Furthermore, the digital modeling of the outer tank of the liquid hydrogen storage tank based on the quasi-two-dimensional model specifically includes: performing digital transient modeling of the high vacuum variable density multilayer insulation structure and the steam cooling screen respectively.

[0012] Furthermore, the digital transient modeling expression for the high-vacuum variable-density multilayer insulation structure is as follows: in, The density of the variable density high-vacuum multilayer insulation structure for liquid hydrogen storage tanks. For the i-th layer liquid hydrogen storage tank, a variable density high vacuum multilayer insulation structure is formed. j The volume of the layer mesh, The isobaric specific heat capacity of the variable-density high-vacuum multilayer insulation structure of the liquid hydrogen storage tank. For the first i Liquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j Temperature of the layer mesh, For the first i Liquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j The area of ​​the layer mesh, For from the first i +1 layer liquid hydrogen storage tank, variable density high vacuum multi-layer insulation structure jheat flux into the i-th layer of the layer grid, is the heat flux into the i-th layer of the layer grid, i is the heat flux into the i-th layer of the layer grid, j is the heat flux into the i-th layer of the layer grid, i is the heat flux into the i-th layer of the layer grid.

[0013] Further, when a single steam cooled screen is provided, the digital transient modeling expression for the steam cooled screen is: wherein, is the density of the steam cooled screen of the liquid hydrogen tank, is the volume of the i-th layer grid of the steam cooled screen of the liquid hydrogen tank, j is the volume of the i-th layer grid of the steam cooled screen of the liquid hydrogen tank, is the isobaric specific heat capacity of the steam cooled screen of the liquid hydrogen tank, is the temperature of the i-th layer grid of the steam cooled screen of the liquid hydrogen tank, j is the heat into the steam cooled screen from the outside, is the heat into the steam cooled screen from the outside, is the heat into the i-th layer grid of the steam cooled screen from the i+1-th layer grid, j is the heat into the i-th layer grid of the steam cooled screen from the i+1-th layer grid, j is the heat into the i-th layer grid of the steam cooled screen from the i-1-th layer grid; is the heat into the i-th layer grid of the steam cooled screen from the i-1-th layer grid; j is the heat into the i-th layer grid of the steam cooled screen from the outside, is the heat into the i-th layer grid of the steam cooled screen from the i-1-th layer grid; j is the heat into the i-th layer grid of the steam cooled screen from the i-1-th layer grid; j is the heat into the i-th layer grid of the steam cooled screen from the i-1-th layer grid; Further, when a single steam cooled screen is provided, the digital transient modeling expression for the steam cooled screen is: wherein, and are the densities of the inner and outer steam cooled screens of the liquid hydrogen tank, respectively, and are the volumes of the i-th layer grid of the inner and outer steam cooled screens of the liquid hydrogen tank, respectively, j and are the isobaric specific heat capacities of the inner and outer steam cooled screens of the liquid hydrogen tank, respectively, is the temperature of the i-th layer grid of the inner steam cooled screen of the liquid hydrogen tank, is the temperature of the i-th layer grid of the outer steam cooled screen of the liquid hydrogen tank, j is the temperature of the i-th layer grid of the inner steam cooled screen of the liquid hydrogen tank, is the temperature of the i-th layer grid of the outer steam cooled screen of the liquid hydrogen tank, M is the heat into the inner steam cooled screen from the outside, j is the heat into the outer steam cooled screen from the outside, is the heat into the i-th layer grid of the inner steam cooled screen from the i+1-th layer grid, M is the heat into the i-th layer grid of the outer steam cooled screen from the i+1-th layer grid, j is the heat into the i-th layer grid of the inner steam cooled screen from the i-1-th layer grid, M is the heat into the i-th layer grid of the outer steam cooled screen from the i-1-th layer grid.j The heat of +1 layer of mesh, The heat flowing into the middle of the insulation structure from the external steam cooling screen. External steam cooling screen from the first M +1- j Inflow M +2- j Calories, For internal steam cooling screen from the first j -1 layer mesh flows into the first j The heat of the mesh layer, For internal steam cooling screen from the first j Layer mesh flows into the first j The heat of +1 layer of mesh, The total number of vertical grids in the quasi-two-dimensional model.

[0014] Furthermore, when a dual-vapor cooling screen is configured, the multi-objective optimization function is expressed as: in, To minimize heat leakage from liquid hydrogen storage tanks, To minimize the manufacturing cost of the insulation structure for liquid hydrogen storage tanks, To minimize excess temperature inside the liquid hydrogen storage tank, To minimize the negative value of the liquid hydrogen storage tank's dormancy time, For the first in the population i Each individual starts counting from the cold end. The number of insulation layers in the interlayer spacer layer, The thickness of a single-layer insulation layer, For the first n Average area of ​​the interlayer spacing The manufacturing cost per unit volume of insulation layer, The length of the internal steam cooling screen VCS1, The radius of the outer diameter of VCS1 The length of the external steam cooling screen VCS2, The radius of the outer diameter of VCS2 The thickness of the steam cooling screen. The manufacturing cost per unit volume of steam cooling screen. The height of the inner tank of the liquid hydrogen storage tank. Let be the radius of the inner tank of the liquid hydrogen storage tank. The thickness of the inner tank of the liquid hydrogen storage tank. Let Z be the manufacturing cost of the inner tank of the liquid hydrogen storage tank, and Z be the total number of gas-liquid two-phase layers in the inner tank of the liquid hydrogen storage tank. For the first z Temperature of the main body layer, The initial temperature of liquid hydrogen. This refers to the time from when the liquid hydrogen storage tank is opened to when it is first vented. N The number of layers in the variable density high vacuum multilayer insulation structure of the liquid hydrogen storage tank.

[0015] Furthermore, the constraints include: Constraints on the number of insulation layers in each layer: Total insulation layer quantity constraint: Steam cooling screen position constraints: Inner tank volume constraints: Inner tank radius constraint: in, This refers to the volume of the inner tank. For a constant volume, Where is the radius of the inner tank. , These are the minimum and maximum values ​​for the inner tank radius.

[0016] Furthermore, when using the NSGA-3 algorithm to solve the problem, genetic operations are performed on the constructed population to obtain a globally optimized solution set. These genetic operations include non-dominated sorting, reference point-based selection, crossover, mutation, and elite retention. Specifically, the selection based on the reference point includes: The objective functions in the multi-objective optimization function are normalized, and the coordinates of reference points are generated based on the normalized values. A reference line is obtained based on each reference point; Calculate the vertical distance between each individual and each reference line, and establish a membership relationship between each individual and the reference line with the smallest distance; The number of individuals belonging to each reference line is counted. Reference lines with the fewest belonging individuals are selected from the ones with the most individuals. The individual with the smallest distance among the selected reference lines enters the next generation, until the population size reaches the preset size.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention adopts a coupling method of an inner tank multi-node self-pressurization model and an outer tank quasi-two-dimensional model, which can simultaneously capture the radial and axial temperature gradient distribution, dynamically reflect the influence of gas-liquid interface phase change and steam cooling screen temperature change, and break through the limitations of traditional models in predicting the actual thermal behavior of storage tanks, thereby significantly improving the prediction accuracy of the actual thermal behavior of storage tanks and improving the reliability of the insulation structure optimization of liquid hydrogen storage tanks.

[0018] 2. This invention takes insulation performance, manufacturing cost, dormancy time and excess temperature as optimization objectives, and establishes a multi-objective optimization system under the synergistic effect of inner and outer tank parameters. It achieves a precise balance between thermal efficiency and cost in the insulation structure of liquid hydrogen storage tanks, and provides a systematic modeling and optimization framework for the optimized design of high-performance cryogenic storage tanks, obtaining the optimal structural scheme that takes into account both thermal performance and economy. The invention also introduces the NSGA-3 multi-objective evolutionary algorithm, and achieves an efficient and uniform Pareto front distribution through genetic operations such as non-dominated sorting, reference point selection, crossover, mutation and elite preservation.

[0019] 3. The coupled modeling and optimization framework proposed in this invention is not only applicable to the joint optimization of the inner and outer tanks of liquid hydrogen storage tanks, but can also be extended to the thermal design optimization of other cryogenic storage tanks such as liquid oxygen and liquid methane. It provides a systematic theoretical method and engineering application basis for the efficient design of complex cryogenic storage and transportation systems, with strong engineering adaptability and high scalability. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the digital model of the liquid hydrogen storage tank constructed in this embodiment of the invention, wherein 1 is the inner wall of the liquid hydrogen storage tank, 2 is the outer wall of the liquid hydrogen storage tank, 3 is the reflector, 4 is the insulation layer, 5 is the inlet pipe of the steam cooling screen, 6 is the outlet pipe of the steam cooling screen, and 7 is the first external steam cooling screen. j Layered mesh, 8th layer is the internal steam cooling screen j The grid consists of three layers: 9 is the main liquid hydrogen layer, 10 is the liquid hydrogen boundary layer, 11 is the main gaseous hydrogen layer, 12 is the gaseous hydrogen boundary layer, and 13 is the liquid hydrogen filling port. Figure 2 This is a flowchart of the optimization method in an embodiment of the present invention. Detailed Implementation

[0021] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0022] like Figure 2 As shown in the figure, the multi-objective optimization method for the insulation structure of a liquid hydrogen storage tank based on multi-node self-pressurization provided in this embodiment includes the following steps: Step S1: Perform digital modeling of the inner tank of the liquid hydrogen storage tank based on a multi-node self-pressurization model.

[0023] Step S1 specifically includes: Step S101: Describe the multi-node heat and mass transfer process of the tank gas phase inside the liquid hydrogen storage tank in functional form. The functional expression includes the mass conservation equation of the gas bulk layer in the multi-node self-pressurization model, the energy conservation equation of the gas bulk layer in the multi-node self-pressurization model, the mass conservation equation of the gas boundary layer in the multi-node self-pressurization model, the energy conservation equation of the gas boundary layer in the multi-node self-pressurization model, the mass conservation formula of the first control volume in contact between hydrogen and liquid hydrogen in the multi-node self-pressurization model, the energy conservation formula of the first control volume in contact between hydrogen and liquid hydrogen in the multi-node self-pressurization model, the mass conservation formula of the highest control volume of hydrogen in the multi-node self-pressurization model, and the energy conservation formula of the highest control volume of hydrogen in the multi-node self-pressurization model.

[0024] More specifically: The above are the mass conservation equations for the gas bulk layer in a multi-node self-pressurization model. Among them, For the first The rate of change of mass of the main layer in a gas control volume For the first The main body of the gas control unit flows into the first gas control layer. Mass flow rate of the main layer in a gas control volume For the first The main body of the gas control unit flows into the first gas control layer. Mass flow rate of the main layer in a gas control volume For the first i The mass flow rate from the host layer into the boundary layer in a gas control volume.

[0025] The above are the energy conservation equations for the gas bulk layer in a multi-node self-pressurization model. Among them, For the first The specific internal energy of the main layer in a gas control body For gas phase Volumetric power of the main layer in a gas control body For the first The enthalpy of the main layer in a gas control volume. For the first The enthalpy value of the main layer in a gas control volume.

[0026] in, The pressure of gaseous hydrogen. For the first The rate of change of the volume of the main layer in a gas control volume.

[0027] The above are the mass conservation equations for the gas boundary layer in a multi-node self-pressurization model. Among them, For the first The rate of change of the boundary layer in a gas control volume For the first The boundary layer flows into the first gas control volume. Mass flow rate of the boundary layer in a gas control volume For the first The boundary layer flows into the first gas control volume. Mass flow rate of the boundary layer in a gas control volume.

[0028] The above are the energy conservation equations for the gas boundary layer in a multi-node self-pressurization model. Among them, For the first Specific internal energy of the boundary layer in a gas control volume For gas phase Heat flow from the inner tank wall to the boundary layer in a gas control body For gas phase Boundary layer volumetric work power in a gas control volume For the first The enthalpy of the boundary layer in a gas control volume. For the first The enthalpy of the boundary layer in a gas control volume.

[0029] in, For the first The heat transfer coefficient between the boundary layer and the wall in a gas control volume. For the first The contact area between the boundary layer and the wall in a gas control volume. This refers to the wall temperature of the gas region inside the liquid hydrogen storage tank. For the first Gas boundary temperature in a gas control volume. in For the first The Reynolds number of the boundary layer in a gas control volume No. Prandtl number of the boundary layer in a gas control volume.

[0030] The above is the mass conservation formula for the first control volume in contact between hydrogen and liquid hydrogen in the multi-node self-pressurization model, where... No. Mass flow rate of the main layer in a gas control volume This represents the mass flow rate of hydrogen from the second bulk layer to the first bulk layer. This represents the mass flow rate of hydrogen from the first bulk layer to the boundary layer. This refers to the mass flow generated by the liquid-gas phase transition.

[0031] The above is the energy conservation formula for the first control volume in contact between hydrogen and liquid hydrogen in the multi-node self-pressurization model. interfacial heat flow between hydrogen gas and liquid hydrogen For gas phase Volumetric power of the main layer in a gas control body For the first The enthalpy of the main layer in a gas control volume. For the first The enthalpy of the boundary layer in a gas control volume. This is the enthalpy value required for the liquid-gas phase transition.

[0032] in, The inner radius of the liquid hydrogen storage tank. The heat transfer coefficient at the interface between hydrogen gas and liquid hydrogen is denoted as . This is the saturation temperature under that pressure. The temperature of the first bulk layer of hydrogen gas. The thermal conductivity of liquid hydrogen, is the Reynolds number at the interface between hydrogen gas and liquid hydrogen.

[0033] The above is the mass conservation formula for the highest control volume of hydrogen in the multi-node self-pressurization model. For the first The mass flow rate of the boundary layer into the highest gas control volume in each gas control volume. For the first The gas control body's main layer flows into the first Mass flow rate of the main gas control layer For from the first The mass flow rate of hydrogen gas flowing out of the liquid hydrogen inner tank from the main body of the gas control body.

[0034] The above is the energy conservation formula for the highest control volume of hydrogen in the multi-node self-pressurization model. This refers to the heat flow from the top layer of the liquid hydrogen storage tank into the inner tank. Representing the gas phase Volumetric power of the main layer in a gas control body For the first The enthalpy of the boundary layer in a gas control volume. For the first The enthalpy of the boundary layer in a gas control volume. The velocity of hydrogen gas discharged from the liquid hydrogen tank.

[0035] Step S102: Describe the multi-node heat and mass transfer process of the liquid phase inside the liquid hydrogen storage tank in functional form. The functional expression includes the mass conservation formula of the liquid bulk layer in the multi-node self-pressurization model, the energy conservation equation of the liquid bulk layer in the multi-node self-pressurization model, the mass conservation equation of the liquid boundary layer in the multi-node self-pressurization model, the energy conservation equation of the liquid boundary layer in the multi-node self-pressurization model, the mass conservation formula of the highest control volume of liquid hydrogen in the contact between liquid hydrogen and hydrogen in the multi-node self-pressurization model, the energy conservation formula of the highest control volume of liquid hydrogen in the contact between liquid hydrogen and hydrogen in the multi-node self-pressurization model, the mass conservation formula of the lowest control volume of liquid hydrogen in the multi-node self-pressurization model, and the energy conservation formula of the lowest control volume of liquid hydrogen in the multi-node self-pressurization model.

[0036] More specifically: The above is the mass conservation formula for the bulk liquid layer in a multi-node self-pressurization model, where... For the first The rate of change of mass of the main layer in a liquid control volume For the first The main body of the liquid control volume flows into the first... Mass flow rate of the bulk layer in a liquid control volume For the first The main body of the liquid control volume flows into the first... Mass flow rate of the bulk layer in a liquid control volume For the first The mass flow rate from the host layer into the boundary layer in a liquid control volume.

[0037] The above are the energy conservation equations for the bulk liquid layer in a multi-node self-pressurization model. Among them, For the first The specific internal energy of the bulk liquid layer For gas phase Volumetric power of the main layer in a liquid control volume For the first The enthalpy value of the main layer in a liquid control volume. For the first The enthalpy value of the main layer in a liquid control volume.

[0038] The above are the mass conservation equations for the liquid boundary layer in a multi-node self-pressurization model. Among them, For the first The rate of change of the boundary layer in a liquid control volume For the first The boundary layer flows into the first liquid control volume. Mass flow rate of the boundary layer in a liquid control volume For the first The boundary layer flows into the first liquid control volume. The mass flow rate of the boundary layer in a liquid control volume.

[0039] The above are the energy conservation equations for the gas bulk layer in a multi-node self-pressurization model. Among them, For the first Specific internal energy of the boundary layer in a liquid control volume For the first Heat flow from the inner tank wall to the boundary layer in a liquid control volume For the first Boundary layer volumetric work power in a liquid control volume For the first The enthalpy of the boundary layer in a liquid control volume For the first The enthalpy of the boundary layer in a liquid control volume.

[0040] The above is the mass conservation formula for the highest control volume of liquid hydrogen in the contact area between liquid hydrogen and hydrogen gas in the multi-node self-pressurization model. For the first The rate of change of the boundary layer in a liquid control volume For the first The mass flow rate of the boundary layer into the highest liquid control volume's main layer in a liquid control volume. For the first The liquid control body layer flows into the first Mass flow rate of the main body layer of the liquid control volume This refers to the mass flow generated by the gas-liquid phase change.

[0041] The above is the energy conservation formula for the highest control volume of liquid hydrogen in the contact area between liquid hydrogen and hydrogen gas in the multi-node self-pressurization model. interfacial heat flow between liquid hydrogen and hydrogen gas. For gas phase Volumetric power of the main layer in a liquid control volume For the first The enthalpy value of the main layer in a liquid control volume. For the first The enthalpy of the boundary layer in a liquid control volume This is the enthalpy value required for the gas-liquid phase transition.

[0042] The above is the mass conservation formula for the lowest control volume of liquid hydrogen in a multi-node self-pressurization model. For the first The mass flow rate of the boundary layer into the lowest gas control volume in each gas control volume. For the first The gas control volume liquid layer flows into the first Mass flow rate of the main body layer of the liquid control volume The mass flow rate of liquid hydrogen in the main body of the lowest layer of liquid control volume is determined by the external injection of liquid hydrogen.

[0043] The above is the energy conservation formula for the lowest control volume of liquid hydrogen in the multi-node self-pressurization model. This refers to the heat flow from the bottom of the liquid hydrogen storage tank into the inner tank. Representing the gas phase Volumetric power of the main layer in a liquid control volume For the first The enthalpy of the boundary layer in a liquid control volume For the first The enthalpy of the boundary layer in a liquid control volume The enthalpy of the liquid hydrogen in the lowest liquid control layer is determined by the amount of liquid hydrogen introduced from the outside. This refers to the rate at which liquid hydrogen is added from the outside.

[0044] Step S2: Perform digital modeling of the outer tank of the liquid hydrogen storage tank based on a quasi-two-dimensional model.

[0045] Step S2 specifically includes: Step S201: Perform digital transient modeling of the high-vacuum variable-density multilayer insulation structure: in, The density of the variable density high-vacuum multilayer insulation structure for liquid hydrogen storage tanks. For the i-th layer liquid hydrogen storage tank, a variable density high vacuum multilayer insulation structure is formed. j The volume of the layer mesh, The isobaric specific heat capacity of the variable-density high-vacuum multilayer insulation structure of the liquid hydrogen storage tank. For the first i Liquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j Temperature of the layer mesh, For the first i Liquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j The area of ​​the layer mesh, For from the first i +1 layer liquid hydrogen storage tank, variable density high vacuum multi-layer insulation structure j The heat flux density flowing into the i-th layer of the mesh. For from the first i Liquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j Inflow of the first layer of mesh i Heat flux density of layer -1.

[0046] in For the first i The radiative heat flux density of the j-th layer of the multilayer insulated structure for variable density high-vacuum liquid hydrogen storage tank. For the first i Liquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j Gas thermal conductivity and heat flux density of the layered grid For the first i Liquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j Solid thermal conductivity heat flux density of layered mesh.

[0047] The above are the formulas for calculating the heat flux density of radiative heat transfer. For the first i Liquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j The radiative heat transfer coefficient of the layered mesh, It is the Stefan-Boltzmann constant. For the first i Emissivity of +1 radiative layer For the first i Emissivity of the radiation layer.

[0048] The above is the formula for calculating the thermal conductivity heat flux density of a gas. No. iLiquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j The gas thermal conductivity of the layered grid, The residual gas pressure, For the fitness coefficient, The specific heat ratio of the residual gas. The gas constant is The residual gas molecular mass, This is the hot end temperature.

[0049] The above are the formulas for calculating the thermal conductivity and heat flux density of solids. No. i Liquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j The thermal conductivity of the solid in the layered mesh, It is an empirical constant. For relative density, Thermal conductivity, For the first i The width of the variable density high vacuum multilayer insulation structure of the liquid hydrogen storage tank.

[0050] Step S202: Perform digital transient modeling of the steam cooling screen.

[0051] The above describes the performance indicators and heat exchange process of a single steam cooling screen. The density of the vapor cooling screen for the liquid hydrogen storage tank. Liquid hydrogen storage tank steam cooling screen j The volume of the layer mesh, The isobaric specific heat capacity of the vapor cooling screen for the liquid hydrogen storage tank. Liquid hydrogen storage tank steam cooling screen j Temperature of the layer mesh, This refers to the heat flowing into the insulation structure from the steam cooling screen. For steam cooling screen from the first j Layer mesh flows into the first j The heat of +1 layer of mesh, For the first j The heat from the outside of the mesh to the steam cooling screen, For steam cooling screen from the first j -1 layer mesh flows into the first j The heat of the mesh layer, N The number of layers in the variable density high vacuum multilayer insulation structure of the liquid hydrogen storage tank. For the first N Liquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j Temperature of the reflective layer of the mesh The first layer of liquid hydrogen storage tank is a variable density high vacuum multi-layer insulation structure. j Temperature of the reflective layer of the mesh For the first i Liquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j Total thermal resistance of the layered mesh. The above describes the performance indicators and heat exchange process of the dual steam cooling screens. and These represent the densities of the internal and external steam cooling screens of the liquid hydrogen storage tank, respectively. and These are the internal and external steam cooling screens of the liquid hydrogen storage tank. j The volume of the layer mesh, and These are the isobaric specific heat capacities of the internal and external steam cooling screens of the liquid hydrogen storage tank, respectively. The first vapor cooling screen inside the liquid hydrogen storage tank j Temperature of the layer mesh, For the external steam cooling screen of the liquid hydrogen storage tank M - j Temperature of +1 layer mesh, External steam cooling screen from the first M - j Layer mesh flows into the first M - j The heat of +1 layer of mesh, The heat flowing into the middle of the insulation structure from the external steam cooling screen. External steam cooling screen from the first M +1- j Inflow M +2- j Calories, For internal steam cooling screen from the first j -1 layer mesh flows into the first j The heat of the mesh layer, For internal steam cooling screen from the first j Layer mesh flows into the first j The heat of +1 layer of mesh, The total number of vertical grids in the quasi-two-dimensional model.

[0052] VCS traffic With the flow rate of the inner tank Consistent.

[0053] The above is the formula for calculating the heat of VCS. The temperature is The enthalpy of hydrogen at that time.

[0054] The structure of the digital model of the liquid hydrogen storage tank constructed based on the above steps S1 and S2 is as follows: Figure 1 As shown, the liquid hydrogen storage tank comprises an inner wall 1, an outer wall 2, a reflector 3, a thermal insulation layer 4, a steam cooling screen inlet pipe 5, an outlet pipe 6, an internal steam cooling screen, an external steam cooling screen, a liquid hydrogen main body layer 9, a liquid hydrogen boundary layer 10, a gaseous hydrogen main body layer 11, a gaseous hydrogen boundary layer 12, and a liquid hydrogen filling port 13. The outer tank adopts a variable density vacuum multi-layer insulation structure, consisting of alternating reflectors and thermal insulation layers. The reflectors are used to reduce radiative heat transfer and are made of low-emissivity aluminized thin film material; the thermal insulation layers are used to suppress thermal conductivity and are made of low-thermal-conductivity nylon mesh material. A steam cooling screen is embedded in the thermal insulation layer, through which low-temperature hydrogen circulates and absorbs heat from the outer tank to achieve cold energy recovery. The steam cooling screen uses high thermal conductivity copper pipes and is discretized into multiple grid units along the axial direction for precise calculation of the temperature field and heat flux density distribution. The inner tank includes the main body layer and boundary layer of liquid hydrogen and gaseous hydrogen, and the gas-liquid two-phase heat and mass transfer process is characterized by multi-node modeling. This model achieves integrated coupled modeling of the inner and outer tanks, and can simultaneously reflect the radial and axial temperature gradients and heat flow changes of the storage tank. Compared with the traditional one-dimensional model, it significantly improves the accuracy and engineering reliability of predicting the thermal insulation performance of liquid hydrogen storage tanks.

[0055] Step S3: Based on the NSGA-3 algorithm, perform multi-objective optimization on the overall cost, thermal insulation performance, dormancy time and excess temperature of the liquid hydrogen storage tank.

[0056] Step S3 specifically includes: Step S301: Initialize the encoding and obtain the initial population by using the position of the steam cooling screen of the variable density high vacuum multilayer insulation structure, the number of insulation layers of each layer and the radius of the liquid hydrogen inner tank as independent variables.

[0057] In this embodiment, a dual steam cooling screen is provided, and the number of individuals in the population is [number missing]. , This indicates the number of the first insulating layer in the population, counting from the cold end. This indicates that the first individual in the population is the first individual from the cold end. The number of insulation layers in the interlayer spacer layer, This indicates the VCS1 position of the first individual in the population. This indicates the VCS2 position of the first individual in the population. This indicates the number of the first insulating layer in the population, starting from the cold end, for the i-th individual. This indicates that the i-th individual in the population starts from the cold end and counts... The number of insulation layers in the interlayer spacer layer, This represents the VCS1 position of the i-th individual in the population. This represents the VCS2 position of the i-th individual in the population. This represents the inner radius of the first individual. This represents the inner radius of the I-th individual.

[0058] Step S302: Define the multi-objective optimization function and its constraints.

[0059] The objective functions mentioned above are respectively To minimize heat leakage from liquid hydrogen storage tanks, To minimize the manufacturing cost of the insulation structure for liquid hydrogen storage tanks, To minimize excess temperature inside the liquid hydrogen storage tank, To minimize the negative value of the liquid hydrogen storage tank's dormancy time, Indicates the first in the population i Each individual starts counting from the cold end. The number of insulation layers in the interlayer spacer layer, Indicates the first in the population i Each individual starts counting from the cold end. j The number of insulation layers in the interlayer spacer layer, The thickness of a single-layer insulation layer, Let n be the average area of ​​the nth interlayer. The manufacturing cost per unit volume of insulation layer, The length of VCS1, The radius of the outer diameter of VCS1 The length of VCS2, The radius of the outer diameter of VCS2 The thickness of the steam cooling screen. The manufacturing cost per unit volume of steam cooling screen. The height of the inner tank of the liquid hydrogen storage tank. The diameter of the inner tank of the liquid hydrogen storage tank. For the number of VCS1 spurs, For the number of VCS2 units, Let be the radius of the inner tank of the liquid hydrogen storage tank. The thickness of the inner tank of the liquid hydrogen storage tank. Let Z be the manufacturing cost of the inner tank of the liquid hydrogen storage tank, and Z be the total number of gas-liquid two-phase layers in the inner tank of the liquid hydrogen storage tank. For the first z Temperature of the main body layer, The initial temperature of liquid hydrogen. This refers to the time from when the liquid hydrogen storage tank is opened to when it is first vented.

[0060] The mathematical expression for the constraints of the multi-objective optimization model is as follows: The above are the constraints, including the number of insulation layers in each partition layer, the total number of insulation layers, the location of the steam cooling screen, the volume of the inner tank, and the radius of the inner tank. Specifically, the number of insulation layers in each partition layer must be greater than or equal to 1 and less than or equal to 6, and the total number of insulation layers must be greater than or equal to 3. N Less than or equal to 6 N The positions of VCS1 and VCS2 are greater than 1 and less than 1. N , This indicates the VCS1 position of the first individual in the population. This indicates the VCS2 position of the first individual in the population. This indicates that the first individual in the population is the first individual from the cold end. The number of insulation layers in the interlayer spacer layer, This indicates that the volume of the hydrogen storage tank remains constant. The inner tank radius Greater than or equal to Less than or equal to .

[0061] Step S303: Genetic operations are performed based on the NSGA-3 algorithm and the objective function of the liquid hydrogen storage tank to obtain the global optimal solution set.

[0062] In this embodiment, genetic operations include non-dominated sorting, reference point-based selection, crossover, mutation, and elite retention.

[0063] Non-dominated sorting is a core screening method in multi-objective optimization. If a solution is not inferior to another solution in all optimization objectives and is strictly better in at least one objective dimension, then the former is determined to dominate the latter. A hierarchical structure of solution sets is formed by screening layer by layer. First, individuals not dominated by any other solution are extracted from the candidate solution set to form the first level. Then, solutions in this level are excluded and the screening process is repeated to generate subsequent levels in turn until all solutions are classified.

[0064] When selecting reference points, the objective functions of each optimization need to be normalized first. Generally, for multi-objective optimization problems, the range of values ​​for each objective can be linearly mapped to the interval [0,1] to eliminate the influence of different dimensions or orders of magnitude. Subsequently, the objective dimensions are equally divided at certain intervals within the normalized objective space, thereby generating a series of reference points. These reference points are used to guide the distribution of the solution set during the evolution process, ensuring that non-dominated solutions are evenly distributed in the objective space, thus guaranteeing the representativeness and diversity of the Pareto front.

[0065] in Let Q be the number of reference points and Q be the number of optimization targets. In this embodiment, Q is 4. P To optimize the number of target segments.

[0066] in Represented as the first n Coordinates of one reference point , , , Represented as normalized coordinates of each objective function, where all coordinates are between 0 and 1. The sum of the coordinates of all points at the same reference point is 1. Then, based on all reference points, connect each reference point to the origin to obtain a straight line connecting it to the origin. , that is, the reference line.

[0067] The above is for each individual With reference lines The formula for calculating the vertical distance d between individuals; With the If the distance to the reference line is the smallest, then the individual is determined to belong to the first reference line. A reference point.

[0068] During the selection phase, a niche preservation approach is used. First, the number of individuals currently assigned to each reference line is counted, indicating the occupancy status of that reference line. Reference lines with fewer occupied individuals represent insufficient population density in that direction within the solution set. The algorithm prioritizes selecting individuals from these under-occupied reference lines, and within the same reference line, selects the individual with the smallest distance to proceed to the next generation. Once all reference lines have associated individuals, selection continues from the group corresponding to the reference line with the fewest individuals, until the population size reaches a preset size, thus maintaining the uniformity and diversity of the Pareto front distribution.

[0069] After selecting offspring with both uniformity and diversity, operations such as crossover, mutation, and elite retention are performed. Through the synergistic effect of these operations, a dynamic balance is achieved between global search and local optimization. The crossover operator simulates the genetic recombination process, exchanging partial gene fragments between parent individuals to generate new individuals with both diversity and superior characteristics. The mutation operator randomly alters the genes of individuals with a low probability, thereby escaping local optima and improving the algorithm's global exploration capability. The elite retention strategy directly passes the best-performing individual in the current generation to the next generation, preventing excellent solutions from being eliminated during iteration. Together, these three elements form an evolutionary closed loop of "generation-exploration-retention," balancing the distribution and convergence stability of the solution set in complex multi-objective optimization until the optimization result is obtained after iteration.

[0070] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0071] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A multi-objective optimization method for the insulation structure of a liquid hydrogen storage tank based on multi-node self-pressurization, characterized in that, Includes the following steps: A digital model of a liquid hydrogen storage tank is constructed, wherein the inner tank of the liquid hydrogen storage tank is digitally modeled based on a multi-node self-pressurization model, and the outer tank of the liquid hydrogen storage tank is digitally modeled based on a quasi-two-dimensional model. The outer tank of the liquid hydrogen storage tank adopts a high-vacuum variable density multi-layer insulation structure. The optimization objects are the location of the steam cooling screen of the high vacuum variable density multi-layer insulation structure, the number of insulation layers and the inner tank radius. The optimization objectives are insulation performance, manufacturing cost, dormancy time and excess temperature. Based on the digital model of the liquid hydrogen storage tank, a multi-objective optimization function and its constraints are established. The optimization results of the insulation structure of the liquid hydrogen storage tank were obtained by solving the multi-objective optimization function using the NSGA-3 algorithm.

2. The multi-objective optimization method for the insulation structure of a liquid hydrogen storage tank based on multi-node self-pressurization as described in claim 1, characterized in that, The digital modeling of the inner tank of the liquid hydrogen storage tank based on the multi-node self-pressurization model specifically involves: based on the conservation of mass and energy, describing the gas phase multi-node heat and mass transfer process and the liquid phase multi-node heat and mass transfer process of the inner tank of the liquid hydrogen storage tank in functional form, respectively.

3. The multi-objective optimization method for the insulation structure of a liquid hydrogen storage tank based on multi-node self-pressurization as described in claim 2, characterized in that, The functional expressions describing the multi-node heat and mass transfer process in the liquid hydrogen storage tank include the mass conservation equations for the gas bulk layer, energy conservation equations for the gas bulk layer, gas boundary layer, and gas boundary layer in the multi-node self-pressurization model; the mass conservation formula for the first control volume in contact between hydrogen and liquid hydrogen in the multi-node self-pressurization model; the energy conservation formula for the first control volume in contact between hydrogen and liquid hydrogen in the multi-node self-pressurization model; the mass conservation formula for the highest control volume of hydrogen in the multi-node self-pressurization model; and the energy conservation formula for the highest control volume of hydrogen in the multi-node self-pressurization model.

4. The multi-objective optimization method for the insulation structure of a liquid hydrogen storage tank based on multi-node self-pressurization as described in claim 2, characterized in that, The functional expressions describing the multi-node heat and mass transfer process in the liquid hydrogen storage tank include the mass conservation formula for the liquid bulk layer, the energy conservation equation for the liquid bulk layer, the mass conservation equation for the liquid boundary layer, the energy conservation equation for the liquid boundary layer, the mass conservation formula for the highest control volume of liquid hydrogen in contact with hydrogen, the energy conservation formula for the lowest control volume of liquid hydrogen, and the energy conservation formula for the lowest control volume of liquid hydrogen in contact with hydrogen.

5. The multi-objective optimization method for the insulation structure of a liquid hydrogen storage tank based on multi-node self-pressurization as described in claim 1, characterized in that, The digital modeling of the outer tank of the liquid hydrogen storage tank based on the quasi-two-dimensional model specifically includes: digital transient modeling of the high vacuum variable density multilayer insulation structure and the steam cooling screen, respectively.

6. The multi-objective optimization method for the insulation structure of a liquid hydrogen storage tank based on multi-node self-pressurization as described in claim 5, characterized in that, The digital transient modeling expression for the high-vacuum variable-density multilayer insulation structure is as follows: in, The density of the variable density high-vacuum multilayer insulation structure for liquid hydrogen storage tanks. For the i-th layer liquid hydrogen storage tank, a variable density high vacuum multilayer insulation structure is formed. j The volume of the layer mesh, The isobaric specific heat capacity of the variable-density high-vacuum multilayer insulation structure of the liquid hydrogen storage tank. For the first i Liquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j Temperature of the layer mesh, For the first i Liquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j The area of ​​the layer mesh, For from the first i +1 layer liquid hydrogen storage tank, variable density high vacuum multi-layer insulation structure j The heat flux density flowing into the i-th layer of the mesh. For from the first i Liquid hydrogen storage tank with variable density high vacuum multilayer insulation structure j The inflow of the first layer of mesh i Heat flux density of layer -1.

7. The multi-objective optimization method for the insulation structure of a liquid hydrogen storage tank based on multi-node self-pressurization as described in claim 5, characterized in that, When a single steam cooling screen is configured, the digital transient modeling expression of the steam cooling screen is: in, The density of the vapor cooling screen for the liquid hydrogen storage tank. Liquid hydrogen storage tank steam cooling screen j The volume of the layer mesh, The isobaric specific heat capacity of the vapor cooling screen for the liquid hydrogen storage tank. Liquid hydrogen storage tank steam cooling screen j Temperature of the layer mesh, This refers to the heat flowing into the insulation structure from the steam cooling screen. For steam cooling screen from the first j Layer mesh flows into the first j The heat of +1 layer of mesh, For the first j The heat from the outside of the mesh to the steam cooling screen, For steam cooling screen from the first j -1 layer mesh flows into the first j The heat of the mesh layer; When setting up dual steam cooling screens, the digital transient modeling expression for the steam cooling screens is: in, and These represent the densities of the internal and external steam cooling screens of the liquid hydrogen storage tank, respectively. and These are the internal and external steam cooling screens of the liquid hydrogen storage tank. j The volume of the layer mesh, and These are the isobaric specific heat capacities of the internal and external steam cooling screens of the liquid hydrogen storage tank, respectively. The first vapor cooling screen inside the liquid hydrogen storage tank j Temperature of the layer mesh, For the external steam cooling screen of the liquid hydrogen storage tank M - j Temperature of +1 layer mesh, External steam cooling screen from the first M - j Layer mesh flows into the first M - j The heat of +1 layer of mesh, The heat flowing into the middle of the insulation structure from the external steam cooling screen. External steam cooling screen from the first M +1- j Inflow M +2- j Calories, For internal steam cooling screen from the first j -1 layer mesh flows into the first j The heat of the mesh layer, For internal steam cooling screen from the first j Layer mesh flows into the first j The heat of +1 layer of mesh, The total number of vertical grids in the quasi-two-dimensional model.

8. The multi-objective optimization method for the insulation structure of a liquid hydrogen storage tank based on multi-node self-pressurization as described in claim 1, characterized in that, When setting up dual steam cooling screens, the multi-objective optimization function is expressed as: in, To minimize heat leakage from liquid hydrogen storage tanks, To minimize the manufacturing cost of the insulation structure for liquid hydrogen storage tanks, To minimize excess temperature inside the liquid hydrogen storage tank, To minimize the negative value of the liquid hydrogen storage tank's dormancy time, For the first in the population i Each individual starts counting from the cold end. The number of insulation layers in the interlayer spacer layer, The thickness of a single-layer insulation layer, For the first n Average area of ​​the interlayer spacing The manufacturing cost per unit volume of insulation layer, The length of the internal steam cooling screen VCS1, The radius of the outer diameter of VCS1 The length of the external steam cooling screen VCS2, The radius of the outer diameter of VCS2 The thickness of the steam cooling screen. The manufacturing cost per unit volume of steam cooling screen. The height of the inner tank of the liquid hydrogen storage tank. Let be the radius of the inner tank of the liquid hydrogen storage tank. The thickness of the inner tank of the liquid hydrogen storage tank. Let Z be the manufacturing cost of the inner tank of the liquid hydrogen storage tank, and Z be the total number of gas-liquid two-phase layers in the inner tank of the liquid hydrogen storage tank. For the first z Temperature of the main body layer, The initial temperature of liquid hydrogen. This refers to the time from when the liquid hydrogen storage tank is opened to when it is first vented. N The number of layers in the variable density high vacuum multilayer insulation structure of the liquid hydrogen storage tank.

9. The multi-objective optimization method for the insulation structure of a liquid hydrogen storage tank based on multi-node self-pressurization as described in claim 8, characterized in that, The constraints include: Constraints on the number of insulation layers in each layer: Total insulation layer quantity constraint: Steam cooling screen position constraints: Inner tank volume constraints: Inner tank radius constraint: in, This refers to the volume of the inner tank. For a constant volume, Where is the radius of the inner tank. , These are the minimum and maximum values ​​for the inner tank radius.

10. The multi-objective optimization method for the insulation structure of a liquid hydrogen storage tank based on multi-node self-pressurization as described in claim 1, characterized in that, When using the NSGA-3 algorithm, genetic operations are performed on the constructed population to obtain the globally optimized solution set. These genetic operations include non-dominated sorting, selection based on reference points, crossover, mutation, and elite retention. Specifically, the selection based on the reference point includes: The objective functions in the multi-objective optimization function are normalized, and the coordinates of reference points are generated based on the normalized values. A reference line is obtained based on each reference point; Calculate the vertical distance between each individual and each reference line, and establish a membership relationship between each individual and the reference line with the smallest distance; The number of individuals belonging to each reference line is counted. Reference lines with the fewest belonging individuals are selected from the ones with the most individuals. The individual with the smallest distance among the selected reference lines enters the next generation, until the population size reaches the preset size.

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