Supercritical carbon dioxide parameter calculation method and device and storable medium

Abstract

The invention discloses a supercritical carbon dioxide parameter calculation method, a supercritical carbon dioxide parameter calculation device and a storable medium, and aims to solve the problem of low parameter calculation precision caused by the fact that the adopted method cannot give consideration to calculation precision and calculation cost when a supercritical carbon dioxide power system is used for calculating supercritical carbon dioxide parameters at present. According to the method, input nodes and input node information are constructed according to the thermodynamic physical quantity of supercritical carbon dioxide, reference node information with a large range is constructed according to the input node information, dependent variable reference values corresponding to the reference node information are solved through a supercritical carbon dioxide functional equation, and dependent variables are carbon dioxide parameters solved through the method. And constructing a two-dimensional node information table by using the reference node information and the corresponding dependent variable reference values. When supercritical carbon dioxide parameters are calculated, the two-dimensional node information table is searched for and solved according to known input node information and solved dependent variables. Belongs to the field of supercritical carbon dioxide power systems.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of supercritical carbon dioxide power systems, in particular to a supercritical carbon dioxide parameter calculation method and device and a storage medium. BACKGROUND

[0002] Nowadays, power grids are mostly operated in a distributed multi-energy complementary manner. The supercritical carbon dioxide power system has high flexibility and fast dynamic response, which provides a new solution to the problem of energy waste caused by renewable energy grid connection, and can also alleviate the pressure of large-scale application of energy storage technology for multi-energy complementary distributed energy.

[0003] The current method for calculating carbon dioxide parameters of supercritical carbon dioxide power systems includes directly solving function equations, building fitting functions, and linear table interpolation. Because the thermophysical properties of supercritical carbon dioxide are different from those of conventional gases and liquids, it exhibits obvious real gas effects near the critical point, and the compression factor deviates far from the ideal gas law. At the same time, when crossing the critical line or the pseudo-critical line, parameters such as specific heat, density, and sound speed will change sharply, causing obvious differences in the flow thermodynamic properties of the working fluid in the supercritical carbon dioxide power system from those of conventional gases, which leads to the following defects of the above three methods:

[0004] Directly solving function equations has high parameter calculation accuracy, but each solution involves a large number of loop iterations, resulting in huge resource consumption and cost waste. Building a fitting function has a lower parameter calculation cost, but it cannot fully capture the near-critical nonlinear distortion characteristics of supercritical carbon dioxide, and the parameter accuracy is severely lost. Linear table interpolation can also meet the low cost requirement, but existing low-order algorithms cannot meet the first-order derivative continuity of supercritical carbon dioxide parameters, and most of the constructed functions do not fully consider the asymptotic singularity and power law of near-critical point parameters, resulting in low parameter calculation accuracy. This further directly affects the efficient design and optimization of supercritical carbon dioxide power systems.

[0005] Therefore, how to provide a supercritical carbon dioxide parameter calculation method that can solve the above problems is a problem that those skilled in the art need to solve. SUMMARY

[0006] The present application is to solve the problem that the method used by the current supercritical carbon dioxide power system in calculating supercritical carbon dioxide parameters cannot balance the calculation accuracy and the calculation cost, and the parameter calculation accuracy is low, and further proposes a supercritical carbon dioxide parameter calculation method, device and storage medium.

[0007] The technical scheme adopted by the present application is:

[0008] It includes the following steps:

[0009] S1, obtain all thermodynamic physical quantities of supercritical carbon dioxide, combine any two thermodynamic physical quantities as a class of input nodes, until all combinations of thermodynamic physical quantities are completed, and obtain all input nodes;

[0010] The thermodynamic physical quantities include pressure, temperature, density, specific heat capacity, sound speed, viscosity, thermal conductivity coefficient, internal energy, dryness, enthalpy and entropy;

[0011] Obtain the input node information of each class of input nodes, and the input node information is all actual values of the input nodes in application;

[0012] S2, according to each class of input node information, construct the reference node information of the corresponding input node, the reference node information is a reference value distribution greater than the value range of the corresponding input node information, and obtain the reference node information corresponding to each class of input node;

[0013] According to each class of reference node information, obtain the corresponding reference node;

[0014] S3, add twin Gaussian term and explicit cross function into Span-Wanger equation to obtain supercritical carbon dioxide function equation, update twin Gaussian term by using whale optimization algorithm, and optimize supercritical carbon dioxide function equation with updated twin Gaussian term;

[0015] S4, sequentially add each class of reference node information to the optimized supercritical carbon dioxide function equation to obtain the dependent variable of the corresponding reference node, the dependent variable is all carbon dioxide thermodynamic physical quantities except the corresponding reference node and the reference value of each carbon dioxide thermodynamic physical quantity in application, then the dependent variable of each class of reference node is multiple, and the dependent variable corresponding to all reference nodes is obtained, and the dependent variable is a supercritical carbon dioxide parameter;

[0016] S5, according to each class of reference node information and the dependent variable of the reference node, construct a corresponding two-dimensional node information table, each two-dimensional node information table includes a class of reference node information and all reference values of one dependent variable corresponding to the reference node information, then each class of reference node information obtains multiple two-dimensional node information tables according to the number of dependent variables, and all two-dimensional node information tables of all reference node information are obtained.

[0017] S6, obtain the input node information to be queried and the dependent variable to be solved, find the corresponding two-dimensional node information table according to the input node information and the dependent variable, send the input node information to the two-dimensional node information table for query, obtain the reference node information closest to the input node information, and solve and output the actual value of the dependent variable corresponding to the input node information according to the closest reference node information.

[0018] Further, the expression of the input node in S1 is (x, y), x, y represent different thermodynamic physical quantities, the expression of the input node information is (x i ,y i ), x i represents the i-th actual value of the thermodynamic physical quantity x, and y i represents the i-th actual value of the thermodynamic physical quantity y.

[0019] Further, the expression of the reference node information in S2 is (x i ′,y i ′), x i ′ represents the i-th reference value of the thermodynamic physical quantity x, and y i ′ represents the i-th reference value of the thermodynamic physical quantity y, and the expression of the reference node is (x′, y′).

[0020] Further, for each type of reference node information in S2, the following settings exist:

[0021] 1) The number of values of each type of reference node information is MxN, and the values are equidistantly distributed, then the interval between adjacent values of the thermodynamic physical quantity x in each type of reference node information is the interval between adjacent values of the thermodynamic physical quantity y is the (m, n)th information in each type of reference node information is (MIN x +(m-1)l x ,MIN y +(n-1)l y ).

[0022] 2) The minimum value of the thermodynamic physical quantity in each type of reference node information is MIN x and MIN y , and the maximum value is MAX x and MAX y .

[0023] Further, the twin Gaussian term and the explicit cross function are added into the Span-Wanger equation in S3 to obtain a supercritical carbon dioxide function equation, the whale optimization algorithm is used to update the twin Gaussian term, and the supercritical carbon dioxide function equation is optimized by using the updated twin Gaussian term, and the specific process is as follows:

[0024] The twin Gaussian term and the explicit cross function are added into the Span-Wanger equation, then the ideal part of the Span-Wanger equation is unchanged, and the remaining part is converted into the following expression type:

[0025]

[0026] where a is the molar Helmholtz free energy, R is the ideal gas constant, T is the temperature, φ is the dimensionless Helmholtz free energy, the superscript r represents the residual part, δ is the dimensionless density, τ is the dimensionless temperature, n i , d i , t i , c i , α i , β i , γ i , ε i are equation-related parameters;

[0027] According to the formula characteristics of the twin Gaussian term, when i = 40 or 42 or 44, and are respectively expressed as:

[0028]

[0029] The supercritical carbon dioxide function equation is:

[0030]

[0031] where the superscript bg represents the environmental part, and the superscript cr represents the key part;

[0032]

[0033]

[0034] where p a is the analysis pressure, ρ c is the critical density, T c is the critical temperature, is the cross-over conversion dimensionless temperature, is the cross-over conversion dimensionless density, K is the kernel term, which represents the critical asymptotic singularity and scaling law of the parameter and the Wegner correction;

[0035] The whale optimization algorithm is used to update the equation-related parameters n i in the twin Gaussian term, the position vector of the equation-related parameters n i in the twin Gaussian term is set, and it is determined whether the modulus of the coefficient vector in the whale optimization algorithm is less than 1;

[0036] If the whale optimization algorithm randomly selects the surrounding or spiral mode to update the position vector of the equation-related parameters n i with a probability of 50%, based on the updated position vector, the variance of the dependent variable and the isochoric heat capacity of supercritical carbon dioxide at the near-critical point is calculated, and when the variance is the smallest, the equation-related parameters n i in the twin Gaussian term are obtained.the optimal solution position vector of the equation-related parameter n in the twin Gauss term to update the twin Gauss term, and optimizing the supercritical carbon dioxide function equation with the updated twin Gauss term.

[0037] If The whale optimization algorithm adopts a global random search update equation related parameter n i The position vector of the equation-related parameter n in the twin Gauss term, based on the updated position vector, calculates the variance of the dependent variable and the isochoric specific heat of supercritical carbon dioxide at the near-critical point, and obtains the optimal solution position vector of the equation-related parameter n in the twin Gauss term when the variance is the smallest. i the optimal solution position vector of the equation-related parameter n in the twin Gauss term to update the twin Gauss term, and optimizing the supercritical carbon dioxide function equation with the updated twin Gauss term.

[0038] Further, the expression of the coefficient vector is:

[0039]

[0040] wherein, and is a coefficient vector, is a control parameter, and the control parameter linearly decreases from 2 to 0 with the number of iterations, is a random vector in [0, 1].

[0041] Further, S5 constructs a corresponding two-dimensional node information table according to each type of reference node information and the dependent variable of the reference node. Each two-dimensional node information table includes one type of reference node information and all reference values of one dependent variable corresponding to the reference node information. Each type of reference node information obtains multiple two-dimensional node information tables according to the number of dependent variables. Similarly, all reference node information obtains all two-dimensional node information tables. The specific process is as follows:

[0042] Based on each type of reference node information and all reference values of one dependent variable of the reference node, a two-dimensional table is constructed. The x i ′ in the current reference node information is arranged in the first row of the two-dimensional table in ascending order. The y i ′ in the current reference node information is arranged in the first column of the two-dimensional table in ascending order. The reference values of the current dependent variable are stored in the cross position of x i ′ and y i ′, to obtain a two-dimensional node information table. Similarly, two-dimensional node information tables of each type of reference node information and different dependent variables are obtained, and all two-dimensional node information tables of all reference node information are obtained.

[0043] The two-dimensional node information table is stored in a computer system in a.csv format.

[0044] Further, the S6 obtains the input node information to be queried and the dependent variable to be solved, finds the corresponding two-dimensional node information table according to the input node information and the dependent variable, sends the input node information to the two-dimensional node information table for querying, obtains the reference node information closest to the input node information, solves and outputs the actual value of the dependent variable corresponding to the input node information according to the closest reference node information, and the specific process is as follows:

[0045] S61, select any input node information (x h ,y h ) to be queried and a dependent variable to be solved, find the corresponding two-dimensional node information table according to the input node information (x h ,y h ) and the dependent variable, and index the input node information (x h ,y h ) in the corresponding two-dimensional node information table using a hash function to obtain the reference node information closest to the input node information (x h ,y h ), and the closest reference node information is four;

[0046] S62, the four closest reference node information surrounds the input node information (x h ,y h ), and the four closest reference node information forms a normalization processing unit, and the input node information (x h ,y h ) is normalized in the horizontal and vertical directions in the normalization processing unit to obtain the processed input node information (t, u):

[0047]

[0048] Wherein, x p is the actual value of the thermodynamic physical quantity x at the index value p, and y q is the actual value of the thermodynamic physical quantity y at the index value q;

[0049] S63, the processed input node information (t, u) is calculated using bicubic interpolation to obtain the actual value of the dependent variable corresponding to the input node information (x h ,y h ).

[0050] A supercritical carbon dioxide parameter calculation device, which comprises an input node information generation module, a reference node information generation module, a dependent variable generation module, a table construction module and a query module;

[0051] The input node information generation module is configured to combine all thermodynamic physical quantities of supercritical carbon dioxide input by a user in pairs to obtain multiple types of input nodes, and to obtain input node information corresponding to each type of input node according to all actual values of each thermodynamic physical quantity in application and each type of input node, and send each type of input node information to the reference node information generation module.

[0052] The thermodynamic physical quantities include pressure, temperature, density, specific heat capacity, sound speed, viscosity, thermal conductivity coefficient, internal energy, dryness, enthalpy, and entropy.

[0053] The input node information is all actual values of the input node in application.

[0054] The reference node information generation module is configured to construct reference node information of the corresponding input node according to the self-defined settings and each type of input node information received to obtain reference node information corresponding to each type of input node, and send each type of reference node information to the dependent variable generation module and the table construction module respectively, wherein the reference node information is a reference value distribution greater than the value range of the corresponding input node information.

[0055] The self-defined settings are:

[0056] 1) Set the number of values of each type of reference node information as M×N, and the values are equidistantly distributed.

[0057] 2) Set the minimum value of the thermodynamic physical quantity in each type of reference node information as MIN x and MIN y , and the maximum value as MAX x and MAX y .

[0058] The dependent variable generation module includes an equation unit and a dependent variable unit.

[0059] The equation unit is configured to add a twin Gaussian term and an explicit cross-over function into a Span-Wanger equation to construct a supercritical carbon dioxide function equation, update the twin Gaussian term by using a whale optimization algorithm, optimize the supercritical carbon dioxide function equation with the updated twin Gaussian term, and send the optimized supercritical carbon dioxide function equation to the dependent variable unit.

[0060] The dependent variable unit is configured to receive each type of reference node information and the optimized supercritical carbon dioxide function equation, add each type of reference node information into the optimized supercritical carbon dioxide function equation in sequence to obtain a dependent variable corresponding to the reference node, and send the dependent variable of each type of reference node to the table construction module.

[0061] The dependent variables are all the carbon dioxide thermodynamic physical quantities except the corresponding reference nodes and the reference values of each carbon dioxide thermodynamic physical quantity in application, so the dependent variables of each type of reference node are multiple, and the dependent variables are supercritical carbon dioxide parameters;

[0062] A table construction module is configured to construct a corresponding two-dimensional node information table according to the received reference node information of each type and the dependent variables of the reference nodes;

[0063] Each two-dimensional node information table includes reference node information of one type and all reference values of one dependent variable corresponding to the reference node information, specifically:

[0064] Based on the reference node information of each type and all reference values of one dependent variable of the reference nodes, a two-dimensional table is constructed, x i ' is arranged in the first row of the two-dimensional table in ascending order, y i ' is arranged in the first column of the two-dimensional table in ascending order, and the reference values of the current dependent variable are stored in the cross position of the corresponding x i ' and y i ' to obtain the two-dimensional node information table; similarly, the two-dimensional node information tables of each type of reference node information and different dependent variables, and all two-dimensional node information tables of all reference node information are obtained;

[0065] The two-dimensional node information table is stored in a computer system in a.csv format;

[0066] A query module is configured to receive input node information to be queried and a dependent variable to be solved sent by a user, find a corresponding two-dimensional node information table in the table construction module according to the input node information to be queried and the dependent variable to be solved, query and output the actual value of the dependent variable corresponding to the input node information on the two-dimensional node information table by using a hash function.

[0067] A storage medium, at least one instruction is stored in the storage medium, the at least one instruction is loaded and executed by a processor to implement any step of a supercritical carbon dioxide parameter calculation method.

[0068] The beneficial effects of the present application are:

[0069] The present application constructs an input node according to the thermodynamic physical quantity of supercritical carbon dioxide, and obtains input node information of the input node. Reference node information corresponding to the input node is constructed according to the input node information, and the reference node information is a reference value distribution greater than the numerical range of the corresponding input node information. Under the premise of considering the near-critical parameter asymptotic singularity and power law of supercritical carbon dioxide, the present application adds a twin Gaussian term and an explicit cross function into the Span-Wanger equation to construct a supercritical carbon dioxide function equation. The whale optimization algorithm is used to update the twin Gaussian term, and the supercritical carbon dioxide function equation is optimized by using the updated twin Gaussian term. The reference node information is added to the optimized supercritical carbon dioxide function equation to obtain the dependent variable of the corresponding reference node, which is all the thermodynamic physical quantities of carbon dioxide and the reference values of each thermodynamic physical quantity of carbon dioxide in application. According to the reference node information and the dependent variable of the reference node, a corresponding two-dimensional node information table is constructed, which establishes the relationship between the input node and the reference node information, facilitates normalization processing, and implements high-order bicubic interpolation calculation to realize the solution of the parameter meeting the first-order derivative continuity. When applied, the user finds the corresponding two-dimensional node information table according to the input node information to be queried and the dependent variable to be solved, and finds the corresponding dependent variable actual value on the table according to the input node information index, which is the supercritical carbon dioxide parameter solved by the present application.

[0070] The present application realizes fast, accurate and reproducible asymptotic singularity and power law solution of the thermodynamic physical quantities (such as isobaric specific heat, density, viscosity and thermal conductivity) of supercritical carbon dioxide, improves the calculation accuracy of supercritical carbon dioxide parameters, and the accurate and fast calculation of supercritical carbon dioxide parameters is directly related to the thermal design of supercritical carbon dioxide dynamic system and components and the accurate prediction of compressor and turbine aerodynamic performance. The present application helps to realize low-cost, efficient and accurate supercritical carbon dioxide parameter characterization, and promotes more detailed and low-cost supercritical carbon dioxide dynamic system research while facilitating convenient and efficient working fluid calculation. BRIEF DESCRIPTION OF DRAWINGS

[0071] Figure 1 The flowchart of the method of the present application;

[0072] Figure 2 The structure schematic diagram of the device of the present application;

[0073] Figure 3 The.csv format file schematic diagram in the embodiment;

[0074] Figure 4 The bicubic interpolation algorithm schematic diagram in the embodiment;

[0075] Figure 5 The isobaric specific heat parameter calculation result graph in the embodiment;

[0076] Figure 6 for the cost comparison chart in the examples; DETAILED DESCRIPTION

[0077] Detailed implementation one: combination Figures 1-6 To illustrate the present embodiment, the present embodiment describes a supercritical carbon dioxide parameter calculation method, which comprises the following steps:

[0078] S1, obtain all thermodynamic physical quantities of supercritical carbon dioxide, the thermodynamic physical quantities include pressure, temperature, density, specific heat capacity, sound speed, viscosity, thermal conductivity, internal energy, dryness, enthalpy and entropy, etc. Any two thermodynamic physical quantities are combined as a class of input nodes, the expression of the input node is (x, y), x and y represent different thermodynamic physical quantities. Until all thermodynamic physical quantities are combined, all input nodes are obtained.

[0079] Obtain the input node information of each class of input nodes, the input node information is all actual values of the input node when applied, the expression of the input node information is (x i ,y i ), x i represents the i-th actual value of the thermodynamic physical quantity x, and y i represents the i-th actual value of the thermodynamic physical quantity y. Obtain the input node information corresponding to each class of input nodes.

[0080] For a class of input nodes (x, y), x represents pressure, and y can represent any one of temperature, density, specific heat capacity, sound speed, viscosity, thermal conductivity, internal energy, dryness, enthalpy and entropy. It should be noted that the dependent variable of (pressure, temperature) is consistent with (temperature, pressure).

[0081] S2, construct reference node information corresponding to the input node according to each class of input node information, the reference node information is a reference value distribution greater than the value range of the corresponding input node information, that is, the value range of the reference node information is greater than the value range of the input node information, and the reference node information corresponding to each class of input nodes is obtained.

[0082] The expression of the reference node information is (x i ′,y i ′), x i ′ represents the i-th reference value of the thermodynamic physical quantity x, and y i ′ represents the i-th reference value of the thermodynamic physical quantity y. According to each class of reference node information, the corresponding reference node is obtained, and the expression of the reference node is (x′, y′).

[0083] For each class of reference node information, the following parameters are set:

[0084] 1) The number of values ​​of each type of reference node information is M×N, and the values ​​are equidistantly distributed. Then the adjacent value interval of the thermodynamic physical quantity x in each type of reference node information is The adjacent numerical intervals of the thermodynamic quantity y are The (m,n)th information in each type of reference node information is (MIN x +(m-1)l x ,MIN y +(n-1)l y ). The present invention sets the number of each type of reference node information to 401×401.

[0085] 2) The minimum value of the thermodynamic physical quantity in each type of reference node information is MIN x and MIN y , the maximum value is MAX x and MAX y .

[0086] The present invention requires that the range of reference nodes can cover the input nodes, for example, reference nodes: (1,100), (2,101), (3,102), (4,103), the input nodes can be (1.5,102.7) or (3.6,101.3) and so on.

[0087] S3. Add the twin Gaussian term and the explicit crossover function to the Span-Wanger equation to obtain the supercritical carbon dioxide function equation. Use the whale optimization algorithm to update the twin Gaussian term, and use the updated twin Gaussian term to optimize the supercritical carbon dioxide function equation.

[0088] The Span-Wanger equation is a multi-parameter equation for the physical properties of carbon dioxide based on the Helmholtz free energy. The equation consists of two parts: the ideal part and the residual part:

[0089] Ideal part:

[0090]

[0091] The rest:

[0092]

[0093] Where a is the molar Helmholtz free energy, R is the ideal gas constant, T is the temperature, φ is the dimensionless Helmholtz free energy, δ is the dimensionless density, and δ = ρ / ρ c , ρ is the density, ρ c is the critical density, τ is the dimensionless temperature, τ=T c / T,T cFor critical temperature, superscript 0 represents ideal part, superscript r represents residual part, delta is a parameter in non-analytic term, a1, a2, a3, n i , d i , t i , c i , alpha i , beta i , gamma i , epsilon i , b i , C i and D i are equation related parameters, are fixed values.

[0094] The present application introduces an explicit cross-over function and a twin Gaussian term into the Span-Wanger equation by considering the parametric asymptotic singularity and power law near the critical point, the ideal part is unchanged, and the residual part is converted into the following expression form:

[0095]

[0096] According to the formula characteristics of the twin Gaussian term, when i=40 or 42 or 44, and are respectively expressed as:

[0097]

[0098] Therefore, the supercritical carbon dioxide function equation is represented as:

[0099]

[0100] Wherein, superscript bg represents the environmental part, superscript cr represents the key part, and the specific expression is:

[0101]

[0102] Wherein, p a is the analysis pressure, is the cross-over conversion dimensionless temperature, is the cross-over conversion dimensionless density, K is the kernel term, which represents the critical asymptotic singularity and scaling law of the parameter and the Wegner correction.

[0103] In order to improve the accuracy of the supercritical carbon dioxide function equation, the whale optimization algorithm is used to optimize the equation related parameters n i in the twin Gaussian term in real time, the position vector of the equation related parameters n i in the twin Gaussian term is set, and it is judged whether the modulus of the coefficient vector in the whale optimization algorithm is less than 1. The expression of the coefficient vector is:

[0104]

[0105] wherein, and is a coefficient vector, is a random vector within [0,1], is a control parameter, the control parameter linearly decreases from 2 to 0 with the iteration number. The above parameters are all parameters of the whale optimization algorithm, wherein, the random vector and the control parameter change with the iteration, so and are variable values at each iteration, realizing real-time optimization of the equation-related parameter n i in the twin Gaussian term.

[0106] If the whale optimization algorithm enters a local optimization stage, the whale optimization algorithm randomly selects, with a probability of 50%, to update the position vector of the equation-related parameter n i in the twin Gaussian term in a surrounding or spiral manner, based on the updated position vector, the variance between the dependent variable of the near-critical point and the isobaric heat capacity of supercritical carbon dioxide is calculated. The present application takes the variance between the dependent variable of the near-critical point and the isobaric heat capacity of supercritical carbon dioxide at the current iteration as the optimization target. When the variance is the smallest, the optimal solution position vector of the equation-related parameter n i in the twin Gaussian term is obtained, the twin Gaussian term is updated with the optimal solution position vector, and the supercritical carbon dioxide function equation is optimized with the updated twin Gaussian term. The surrounding or spiral updating method gradually decreases and with the increase of the iteration number t.

[0107] If the whale optimization algorithm updates the position vector of the equation-related parameter n i in the twin Gaussian term in a global random search manner, based on the updated position vector, the variance between the dependent variable of the near-critical point and the isobaric heat capacity of supercritical carbon dioxide is calculated, when the variance is the smallest, the optimal solution position vector of the equation-related parameter n i in the twin Gaussian term is obtained, the twin Gaussian term is updated with the optimal solution position vector, and the supercritical carbon dioxide function equation is optimized with the updated twin Gaussian term.

[0108] When the surrounding method is used to update the position vector of the equation-related parameter n i in the twin Gaussian term, the current position vector of the equation-related parameter n i is updated by the current optimal solution, and the specific expression is:

[0109]

[0110] wherein, the distance between the current position vector of the equation-related parameter n i and the optimal solution position vector of the equation-related parameter n , t is the current iteration number, and t is the current iteration number i , t is the current iteration number, and t is the current iteration number , t is the current iteration number, and t is the current iteration number i , t is the current iteration number, and t is the current iteration number , t is the current iteration number, and t is the current iteration number i .

[0111] When the position vector of the equation-related parameter n i is updated using the spiral method, the update of the equation-related parameter n i is expressed by a logarithmic spiral equation:

[0112]

[0113]

[0114] wherein, is the distance between the current position vector of the equation-related parameter n i and the optimal solution position vector of the equation-related parameter n i , b is a spiral shape constant, and l is a random number in [-1, 1].

[0115] The specific expression of the global random search strategy is:

[0116]

[0117] wherein, is a randomly generated position vector of the equation-related parameter n i . When the maximum iteration number or the convergence threshold is reached, the optimization is completed, and the optimal solution position vector of the equation-related parameter n i is obtained.

[0118] S4, sequentially add each type of reference node information to the optimized supercritical carbon dioxide function equation to obtain the dependent variable corresponding to the reference node, wherein the dependent variable is all carbon dioxide thermodynamic physical quantities except the corresponding reference node and the reference value of each carbon dioxide thermodynamic physical quantity in application, and the dependent variable of each type of reference node is multiple, and the multiple is greater than or equal to two, thereby obtaining the dependent variable corresponding to all reference nodes.

[0119] Each type of reference node information is sequentially imported into the compiled supercritical carbon dioxide function equation code, and the dependent variable corresponding to the reference node is output, and the dependent variable is a supercritical carbon dioxide parameter.

[0120] S5. Construct a corresponding two-dimensional node information table based on each type of reference node information and the dependent variable of the reference node. Each two-dimensional node information table includes a type of reference node information and all reference values ​​of a dependent variable corresponding to the reference node information. Then, multiple two-dimensional node information tables are obtained for each type of reference node information based on the number of dependent variables. Similarly, all two-dimensional node information tables for all reference node information are obtained. The specific process is as follows:

[0121] A two-dimensional table is constructed based on each type of reference node information and all reference values ​​of a dependent variable of the reference node, and the x in the current reference node information is converted into a i ' Arrange them in the first row of the two-dimensional table in ascending order, and put the y i 'Arrange them in the first column of the two-dimensional table in ascending order, and store the reference value of the current dependent variable in the corresponding x i ′ and y i ' intersection position, a two-dimensional node information table is obtained. Similarly, a two-dimensional node information table for each type of reference node information and different dependent variables can be obtained. Then, based on the number of dependent variables for each type of reference node information, multiple two-dimensional node information tables corresponding to each type of reference node information can be obtained, and ultimately all two-dimensional node information tables for all reference node information are obtained. The present invention thus ensures that different thermodynamic physical quantities in the two-dimensional node information table correspond to the corresponding dependent variables. The two-dimensional node information table is stored in a computer system in .csv format.

[0122] In this embodiment, if Figure 3 As shown, the A1 box of the two-dimensional node information table shows the type of thermodynamic physical quantity in this table row and column, that is, the reference node of this table is (temperature, pressure). The first row is the actual application value of temperature T from small to large, and the first column is the actual application value of pressure P from small to large. Each dependent variable of different temperature and pressure is stored in the x value associated with it. i with y i The two-dimensional node information table is the prerequisite for interpolation calculations. Near the critical point, when the temperature or pressure changes only slightly, the physical properties of carbon dioxide will change dramatically. Therefore, it is particularly important to capture the area near the critical point of carbon dioxide more precisely.

[0123] S6. Get the input node information to be queried (x h ,y h ) and the dependent variable to be solved, according to the input node information (x h ,y h ) and the dependent variable to find the corresponding two-dimensional node information table, and the input node information (x h ,y h) to the two-dimensional node information table to obtain reference node information closest to the input node information (x h ,y h ), solve and output the actual value of the dependent variable corresponding to the input node information (x h ,y h ). The specific process is as follows:

[0124] S61, select an arbitrary input node information (x h ,y h ) to be queried and a dependent variable to be solved, find the corresponding two-dimensional node information table according to the input node information (x h ,y h ) and the dependent variable, and use a hash function to index and find the input node information (x h ,y h ) in the corresponding two-dimensional node information table to obtain reference node information closest to the input node information (x h ,y h ). The closest reference node information is four.

[0125] The hash function has the smallest time complexity compared with other algorithms, which is only O(1). As shown in Figure 3 , the index search needs to be performed along the thermodynamic physical quantity x and the thermodynamic physical quantity y directions of the two-dimensional node information table respectively. For the input node information (x h ,y h ), the index value obtained by using the hash function is:

[0126]

[0127] Wherein, p is the index value along the thermodynamic physical quantity x direction, and q is the index value along the thermodynamic physical quantity y direction.

[0128] S62, normalize the input node information (x h ,y h ) by using the four closest reference node information to obtain the processed input node information, and the specific process is as follows:

[0129] In this embodiment, as shown in Figure 4 , the four closest reference node information surrounds the input node information (x h ,y h ), and the four closest reference node information forms a normalization processing unit, and the input node information (x h ,y h) along the two directions of horizontal and vertical, to obtain the processed input node information (t, u), and the specific expression is:

[0130]

[0131] wherein, x p is the actual value of the thermodynamic physical quantity x at the index value p, y q is the actual value of the thermodynamic physical quantity y at the index value q.

[0132] S63, using bicubic interpolation to calculate the processed input node information, to obtain the actual value of the dependent variable corresponding to the input node information (x h , y h ).

[0133] The bicubic interpolation applies a second-order polynomial, which can ensure the first-order derivative continuity of the function equation. Due to the increase of reference information, it can capture the law of the nonlinear mutation of supercritical carbon dioxide near the critical point, which is beneficial to realize the low-distortion precision parameter solution. As shown in Figure 4 in the two-dimensional node information table, the bicubic interpolation method makes the input node information (x h , y h ) simultaneously constrained by the information of 16 reference nodes, and the 16 reference node information has been determined through S1 and S2. By substituting the processed input node information, the corresponding dependent variable can be solved, that is:

[0134] A(t, u) = [1 t t 2 t 3 ]a Matirx [1 u u 2 u 3 ] T

[0135] wherein, A is the dependent variable, a Matirx is the coefficient matrix, a Matirx is composed of the parameter matrix P Matrix and the parameter matrix A Matrix , and the specific expression is:

[0136]

[0137] wherein, A(x0, y0), A(x0, y1),..., A(x3, y3) are the dependent variables stored by the 16 reference nodes.

[0138] Specific implementation method two: combined with Figures 1-6The embodiment discloses a supercritical carbon dioxide parameter calculation device, which comprises an input node information generation module, a reference node information generation module, a dependent variable generation module, a table construction module and a query module.

[0139] The input node information generation module is used for combining all thermodynamic physical quantities of supercritical carbon dioxide input by a user in pairs to obtain multiple types of input nodes, and is also used for obtaining input node information corresponding to each type of input node according to all actual values of each thermodynamic physical quantity in application and each type of input node, and sending each type of input node information to the reference node information generation module.

[0140] The thermodynamic physical quantities include pressure, temperature, density, specific heat capacity, sound velocity, viscosity, thermal conductivity coefficient, internal energy, dryness, enthalpy and entropy, etc. The expression of the input node is (x, y), and x and y represent different thermodynamic physical quantities. The input node information is all actual values of the input node in application, and the expression is (x i ,y i ), x i represents the i th actual value of the thermodynamic physical quantity x, and y i represents the i th actual value of the thermodynamic physical quantity y.

[0141] The reference node information generation module is used for constructing reference node information of the corresponding input node according to self-defined settings and each type of input node information received, the reference node information is a reference value distribution greater than the value range of the corresponding input node information, that is, the value range of the reference node information is greater than the value range of the input node information, reference node information corresponding to each type of input node is obtained, and each type of reference node information is sent to the dependent variable generation module and the table construction module respectively.

[0142] The expression of the reference node information is (x i ′,y i ′), x i ′ represents the i th reference value of the thermodynamic physical quantity x, and y i ′ represents the i th reference value of the thermodynamic physical quantity y. The self-defined settings are as follows:

[0143] 1) The value number of each type of reference node information is set as MxN, and the values are equidistantly distributed, so that the adjacent value interval of the thermodynamic physical quantity x in each type of reference node information is the adjacent value interval of the thermodynamic physical quantity y is the (m, n) th information in each type of reference node information is (MIN x +(m-1)l x ,MIN y +(n-1)l y). The application sets the number of each type of reference node information as 401*401.

[0144] 2) Set the minimum value of the thermodynamic physical quantity in each type of reference node information as MIN x and MIN y , and the maximum value as MAX x and MAX y .

[0145] Dependent variable generation module, including equation unit and dependent variable unit.

[0146] The equation unit is used to add a twin Gaussian term and an explicit cross function into a Span-Wanger equation to construct a supercritical carbon dioxide function equation, and then a whale optimization algorithm is used to update the twin Gaussian term, so as to optimize the supercritical carbon dioxide function equation with the updated twin Gaussian term. The optimized supercritical carbon dioxide function equation is sent to the dependent variable unit. Specifically,

[0147] The twin Gaussian term and the explicit cross function are added into the Span-Wanger equation, and then the ideal part of the Span-Wanger equation remains unchanged, and the remaining part is converted into the following expression type:

[0148]

[0149] Wherein, a is the molar Helmholtz free energy, R is the ideal gas constant, T is the temperature, φ is the dimensionless Helmholtz free energy, the superscript r represents the remaining part, δ is the dimensionless density, τ is the dimensionless temperature, n i , d i , t i , c i , alpha i , beta i , gamma i , epsilon i are equation related parameters.

[0150] According to the formula characteristics of the twin Gaussian term, when i=40 or 42 or 44, and are respectively expressed as:

[0151]

[0152] The supercritical carbon dioxide function equation is:

[0153]

[0154] Wherein, the superscript bg represents the environment part, and the superscript cr represents the key part.

[0155]

[0156] where p a is the pressure, p c is the critical density, T c is the critical temperature, is the cross-over conversion dimensionless temperature, is the cross-over conversion dimensionless density, K is the nuclear term, which characterizes the critical asymptotic singularity and scaling law of the parameter and the Wegner correction.

[0157] The whale optimization algorithm is used to update the equation-related parameter n i in the twin Gaussian term, the position vector of the equation-related parameter n i in the twin Gaussian term is set, and it is determined whether the modulus of the coefficient vector in the whale optimization algorithm is less than 1. The expression of the coefficient vector is as follows:

[0158]

[0159] where, and are the coefficient vectors, is a random vector in [0, 1], is a control parameter, and the control parameter linearly decreases from 2 to 0 with the number of iterations. The above parameters are all parameters of the whale optimization algorithm, wherein the random vector and the control parameter change with iterations, so and are variable values at each iteration.

[0160] If the whale optimization algorithm randomly selects the surrounding or spiral method to update the position vector of the equation-related parameter n i with a probability of 50%, based on the updated position vector, the variance of the dependent variable of the near-critical point and the isobaric heat capacity of supercritical carbon dioxide is calculated, the optimal solution position vector of the equation-related parameter n i in the twin Gaussian term is obtained when the variance is the smallest, the twin Gaussian term is updated with the optimal solution position vector, and the function equation of supercritical carbon dioxide is optimized with the updated twin Gaussian term. The surrounding or spiral updating method gradually decreases and with the increase of the number of iterations t.

[0161] If the whale optimization algorithm uses global random search to update the position vector of the equation-related parameter n i , based on the updated position vector, the variance of the dependent variable of the near-critical point and the isobaric heat capacity of supercritical carbon dioxide is calculated, and the equation-related parameter n iThe optimal solution position vector is used to update the twin Gaussian term, and the updated twin Gaussian term is used to optimize the supercritical carbon dioxide function equation.

[0162] When the bracketing method is used to update the equation-related parameters n in the twin Gaussian term i When the position vector is , the relevant parameter n in the equation i The current position vector of is updated by adjusting the current optimal solution. The specific expression is:

[0163]

[0164] in, is the equation-related parameter n in the twin Gaussian term considering the coefficient vector i The distance between the current position vector and the optimal solution position vector, is the equation-related parameter n in the current twin Gaussian term i The optimal solution position vector, t is the current iteration number, is the equation-related parameter n in the current twin Gaussian term i The position vector of is the equation-related parameter n in the updated twin Gaussian term i The position vector of .

[0165] When the spiral method is used to update the equation-related parameters n in the twin Gaussian term i When the position vector is , the equation parameter n in the twin Gaussian term is i The update is expressed by the logarithmic spiral equation:

[0166]

[0167] in, is the parameter n related to the equation in the twin Gaussian term i The distance between the current position vector and the optimal solution position vector, b is the spiral shape constant, and l is a random number in [-1,1].

[0168] The specific expression of the global random search strategy is:

[0169]

[0170] in, is the randomly generated equation-related parameter n i The position vector of the equation. When the maximum number of iterations or the convergence threshold is reached, the optimization is completed and the equation-related parameters n are obtained. i The optimal solution position vector.

[0171] The dependent variable unit is configured to receive each type of reference node information and the optimized supercritical carbon dioxide function equation, sequentially add each type of reference node information into the optimized supercritical carbon dioxide function equation to obtain the dependent variable corresponding to the reference node, and send the dependent variable of each type of reference node to the table construction module. Specifically,

[0172] Each type of reference node information is sequentially imported into the prepared supercritical carbon dioxide function equation code, and the dependent variable corresponding to the reference node is output. The dependent variable is a supercritical carbon dioxide parameter. The dependent variable is all thermodynamic physical quantities of carbon dioxide except the corresponding reference node, and the reference values of each thermodynamic physical quantity of carbon dioxide in application. Therefore, the dependent variable of each type of reference node is multiple, and the multiple is greater than or equal to two. The dependent variable corresponding to all reference nodes is obtained.

[0173] The table construction module is configured to construct a corresponding two-dimensional node information table according to the received each type of reference node information and the dependent variable of the reference node.

[0174] Each two-dimensional node information table includes one type of reference node information and all reference values of one dependent variable corresponding to the reference node information. Specifically,

[0175] Based on each type of reference node information and all reference values of one dependent variable of the reference node, one two-dimensional table is constructed. x i ′ in the current reference node information is arranged in the first row of the two-dimensional table in ascending order. y i ′ in the current reference node information is arranged in the first column of the two-dimensional table in ascending order. The reference values of the current dependent variable are stored in the cross position of corresponding x i ′ and y i ′ to obtain the two-dimensional node information table. Similarly, the two-dimensional node information table of each type of reference node information and different dependent variables, and all two-dimensional node information tables of all reference node information are obtained. The two-dimensional node information table is stored in the computer system in the.csv format.

[0176] The query module is configured to receive the input node information to be queried and the dependent variable to be solved sent by the user, find the corresponding two-dimensional node information table in the table construction module according to the input node information to be queried and the dependent variable to be solved, and query and output the actual value of the dependent variable corresponding to the input node information on the two-dimensional node information table by using a hash function. The specific process is as follows:

[0177] S61, select an arbitrary input node information (x h ,y h ) to be queried and a dependent variable to be solved, and find the corresponding two-dimensional node information table in the table construction module according to the input node information (x h ,y h) and the dependent variable to be solved, the corresponding two-dimensional node information table is found, the input node information (x h ,y h ) is indexed and looked up by using a hash function in the corresponding two-dimensional node information table, the closest reference node information to the input node information (x h ,y h ) is obtained, and the closest reference node information is four;

[0178] S62, the four closest reference node information surrounds the input node information (x h ,y h ), the four closest reference node information is composed into a normalization processing unit, the input node information (x h ,y h ) is normalized in the horizontal and vertical directions in the normalization processing unit, and the processed input node information (t, u) is obtained:

[0179]

[0180] Wherein, x p is the actual value of the thermodynamic physical quantity x at the index value p, y q is the actual value of the thermodynamic physical quantity y at the index value q;

[0181] S63, the processed input node information (t, u) is calculated by using bicubic interpolation, and the actual value of the dependent variable corresponding to the input node information (x h ,y h ) is obtained.

[0182] Specific embodiment three: the storage medium described in the embodiment, the storage medium stores at least one instruction, the at least one instruction is loaded by the processor and executed to realize any step of the supercritical carbon dioxide parameter calculation method.

[0183] It should be understood that any method described in the present application can be provided as a computer program product, software or computerized method, which can include a non-transitory machine-readable medium having instructions stored thereon, which can be used to program a computer system or other electronic device. The storage medium can include, but is not limited to, magnetic storage medium, optical storage medium, magneto-optical storage medium, read-only memory (ROM), random access memory (RAM), erasable programmable memory (such as EPROM and EEPROM) and flash memory layer; or other types of media suitable for storing electronic instructions.

[0184] Embodiment:

[0185] As Figure 5As shown, when the input node is (temperature, density), the isobaric specific heat is solved. The temperature is taken as 304.1382 K, the density is taken as a variable, and the density rising range is set as 445 kg·m 3 to 495 kg·m 3 , each time increasing by 5 kg·m 3 In the two-dimensional node information table constructed by the present application, the corresponding two-dimensional node information table of (temperature, density, isobaric specific heat) is found, and the isobaric specific heat near the critical point is solved by normalization processing and bicubic interpolation. The present application takes into account the asymptotic singularity and power law of the near-critical parameter, and the calculated solution is extremely close to the direct solution of the supercritical carbon dioxide function equation SW-Crossover, and compared with the traditional SW function equation, the near-critical parameter variation characteristics of supercritical carbon dioxide are more accurately simulated. For another example, see the attached Figure 6 As shown, compared with the direct solution of the function equation SW-Crossover, the calculation cost of the present application is significantly reduced, and is less affected by the number of input nodes, greatly saving the calculation cost.

[0186] The above examples of the present application are only to illustrate the calculation model and calculation process of the present application, and are not limited to the embodiments of the present application. Based on the above description, other different forms of changes or variations can be made by those skilled in the art, and it is impossible to enumerate all the embodiments here, and any obvious changes or variations derived from the technical solutions of the present application still fall within the protection scope of the present application.

Claims

1. A method for calculating supercritical carbon dioxide parameters, characterized by: It includes the following steps: S1. Obtain all thermodynamic quantities of supercritical carbon dioxide, and combine any two thermodynamic quantities as a type of input node until all thermodynamic quantities are combined to obtain all input nodes; The thermodynamic physical quantities include pressure, temperature, density, specific heat capacity, speed of sound, viscosity, thermal conductivity, internal energy, dryness, enthalpy and entropy; Obtaining input node information of each type of input node, wherein the input node information is all actual values ​​of the input node when applied; S2. Construct reference node information of the corresponding input node based on each type of input node information, wherein the reference node information is a reference value distribution that is larger than the value range of the corresponding input node information, and obtain reference node information corresponding to each type of input node; Obtain the corresponding reference node according to each type of reference node information; S3. Add the twin Gaussian term and the explicit crossover function to the Span-Wanger equation to obtain the supercritical carbon dioxide function equation. Use the whale optimization algorithm to update the twin Gaussian term, and use the updated twin Gaussian term to optimize the supercritical carbon dioxide function equation. S4. Adding each type of reference node information to the optimized supercritical carbon dioxide function equation in turn to obtain the dependent variable of the corresponding reference node, wherein the dependent variable is all carbon dioxide thermodynamic quantities except the corresponding reference node and the reference value of each carbon dioxide thermodynamic quantity when applied. There are multiple dependent variables for each type of reference node, and the dependent variables corresponding to all reference nodes are obtained, and the dependent variables are supercritical carbon dioxide parameters; S5. Construct a corresponding two-dimensional node information table based on each type of reference node information and the dependent variable of the reference node. Each two-dimensional node information table includes a type of reference node information and all reference values ​​of a dependent variable corresponding to the reference node information. Multiple two-dimensional node information tables are obtained for each type of reference node information based on the number of dependent variables. Similarly, all two-dimensional node information tables for all reference node information are obtained. S6. Obtain the input node information to be queried and the dependent variable to be solved, find the corresponding two-dimensional node information table based on the input node information and the dependent variable, send the input node information to the two-dimensional node information table for query, obtain the reference node information closest to the input node information, solve and output the actual value of the dependent variable corresponding to the input node information based on the closest reference node information.

2. A supercritical carbon dioxide parameter calculation method according to claim 1, characterized in that: The expression of the input node in S1 is (x, y), where x and y represent different thermodynamic physical quantities. The expression of the input node information is (x i ,y i ), x i represents the actual value of the thermodynamic physical quantity x, y i Represents the i-th actual value of the thermodynamic quantity y.

3. A supercritical carbon dioxide parameter calculation method according to claim 1, characterized in that: The expression of the reference node information in S2 is (x i ′,y i ′), x i ′ represents the i-th reference value of the thermodynamic physical quantity x, y i ′ represents the i-th reference value of the thermodynamic physical quantity y, and the expression of the reference node is (x′, y′).

4. The method for calculating supercritical carbon dioxide parameters according to claim 1, wherein: Each type of reference node information in S2 has the following settings: 1) The number of values ​​of each type of reference node information is M×N, and the values ​​are equidistantly distributed. Then the adjacent value interval of the thermodynamic physical quantity x in each type of reference node information is The adjacent numerical intervals of the thermodynamic quantity y are The (m,n)th information in each type of reference node information is (MIN x +(m-1)l x ,MIN y +(n-1)l y ); 2) The minimum value of the thermodynamic physical quantity in each type of reference node information is MIN x and MIN y , the maximum value is MAX x and MAX y .

5. The method for calculating supercritical carbon dioxide parameters according to claim 1, wherein: In S3, the twin Gaussian term and the explicit crossover function are added to the Span-Wanger equation to obtain the supercritical carbon dioxide function equation. The whale optimization algorithm is used to update the twin Gaussian term, and the supercritical carbon dioxide function equation is optimized with the updated twin Gaussian term. The specific process is as follows: By adding the twin Gaussian terms and explicit crossover functions to the Span-Wanger equation, the ideal part of the Span-Wanger equation remains unchanged, and the remaining part is converted to the following expression: where a is the molar Helmholtz free energy, R is the ideal gas constant, T is the temperature, φ is the dimensionless Helmholtz free energy, the superscript r indicates the remainder, δ is the dimensionless density, τ is the dimensionless temperature, and n i , d i , t i , c i , α i , β i , γ i , ε i are the parameters related to the equation; According to the formula characteristics of the twin Gaussian term, when i=40 or 42 or 44, and Expressed as: Supercritical carbon dioxide function equation: Among them, the superscript bg represents the environmental part, and the superscript cr represents the critical part; Among them, p a To analyze pressure, ρ c is the critical density, T c is the critical temperature, is the dimensionless temperature for the crossover conversion, To bridge the dimensionless density, K is the kernel term, which characterizes the critical asymptotic singularity and scaling law of the parameter and the Wegner correction; Use the whale optimization algorithm to update the equation-related parameters n in the twin Gaussian term i , set the equation related parameters n in the twin Gaussian term i The position vector of the whale optimization algorithm determines the coefficient vector Is the modulus less than 1? like The whale optimization algorithm randomly chooses to use the encirclement or spiral method to update the equation-related parameters n with a probability of 50%. i The position vector of the supercritical point is updated. Based on the updated position vector, the variance of the near-critical point dependent variable and the isobaric specific heat of supercritical carbon dioxide is calculated. When the variance is minimized, the equation-related parameter n in the twin Gaussian term is obtained. i The optimal solution position vector is used to update the twin Gaussian term, and the supercritical carbon dioxide function equation is optimized with the updated twin Gaussian term; like The whale optimization algorithm uses global random search to update the relevant parameters n of the equation i The position vector of the supercritical point is updated. Based on the updated position vector, the variance of the near-critical point dependent variable and the isobaric specific heat of supercritical carbon dioxide is calculated. When the variance is minimized, the equation parameter n in the twin Gaussian term is obtained. i The optimal solution position vector is used to update the twin Gaussian term, and the updated twin Gaussian term is used to optimize the supercritical carbon dioxide function equation.

6. A supercritical carbon dioxide parameter calculation method according to claim 5, characterized in that: The coefficient vector The expression is: in, and is the coefficient vector, is the control parameter, which decreases linearly from 2 to 0 with the number of iterations. is a random vector in [0,1].

7. A supercritical carbon dioxide parameter calculation method according to claim 1, characterized in that: In S5, a corresponding two-dimensional node information table is constructed according to each type of reference node information and the dependent variable of the reference node. Each two-dimensional node information table includes a type of reference node information and all reference values ​​of a dependent variable corresponding to the reference node information. Therefore, multiple two-dimensional node information tables are obtained for each type of reference node information according to the number of dependent variables. Similarly, all two-dimensional node information tables of all reference node information are obtained. The specific process is as follows: A two-dimensional table is constructed based on each type of reference node information and all reference values ​​of a dependent variable of the reference node, and the x in the current reference node information is converted into a i ' Arrange them in the first row of the two-dimensional table in ascending order, and put the y i 'Arrange them in the first column of the two-dimensional table in ascending order, and store the reference value of the current dependent variable in the corresponding x i ′ and y i ′’s intersection position, and obtain a two-dimensional node information table. Similarly, obtain a two-dimensional node information table of each type of reference node information and different dependent variables, as well as all two-dimensional node information tables of all reference node information; The two-dimensional node information table is stored in a computer system in a .csv format.

8. A supercritical carbon dioxide parameter calculation method according to claim 1, characterized in that: In S6, the input node information to be queried and the dependent variable to be solved are obtained, and a corresponding two-dimensional node information table is found according to the input node information and the dependent variable. The input node information is sent to the two-dimensional node information table for query, and the reference node information closest to the input node information is obtained. The actual value of the dependent variable corresponding to the input node information is solved and output according to the closest reference node information. The specific process is as follows: S61, select any input node information to be queried (x h ,y h ) and the dependent variable to be solved, according to the input node information (x h ,y h ) and the dependent variable to find the corresponding two-dimensional node information table, and use the hash function to perform the input node information (x h ,y h ) to perform index search and obtain the input node information (x h ,y h ) the closest reference node information, wherein the number of the closest reference node information is four; S62, the four closest reference node information surround the input node information (x h ,y h ), the four closest reference node information are combined into a normalized processing unit, and the input node information (x h ,y h ) is normalized in both horizontal and vertical directions to obtain the processed input node information (t,u): Among them, x p is the actual value of the thermodynamic quantity x at index p, y q is the actual value of the thermodynamic quantity y at index value q; S63, using bicubic interpolation to calculate the processed input node information (t,u), and obtain the input node information (x h ,y h ) corresponds to the actual value of the dependent variable.

9. A supercritical carbon dioxide parameter calculation device, characterized in that: It includes input node information generation module, reference node information generation module, dependent variable generation module, table construction module and query module; An input node information generation module is used to perform pairwise combinations of all thermodynamic physical quantities of supercritical carbon dioxide input by the user to obtain multiple types of input nodes; it is also used to obtain input node information corresponding to each type of input node based on all actual values ​​of each thermodynamic physical quantity input by the user during application and each type of input node, and send each type of input node information to the reference node information generation module; The thermodynamic physical quantities include pressure, temperature, density, specific heat capacity, speed of sound, viscosity, thermal conductivity, internal energy, dryness, enthalpy and entropy; The input node information is all actual values ​​of the input node when it is applied; A reference node information generation module is used to construct reference node information of the corresponding input node based on the custom settings and each type of input node information received, obtain reference node information corresponding to each type of input node, and send each type of reference node information to the dependent variable generation module and the table construction module respectively; The custom settings are: 1) Set the number of values ​​for each type of reference node information to M×N, and the values ​​are evenly distributed; 2) Set the minimum value of the thermodynamic physical quantity in each type of reference node information to MIN x and MIN y , the maximum value is MAX x and MAX y ; Dependent variable generation module, including equation unit and dependent variable unit; The equation unit is used to add the twin Gaussian term and the explicit crossover function into the Span-Wanger equation to construct the supercritical carbon dioxide function equation, use the whale optimization algorithm to update the twin Gaussian term, optimize the supercritical carbon dioxide function equation with the updated twin Gaussian term, and send the optimized supercritical carbon dioxide function equation to the dependent variable unit; A dependent variable unit is used to receive the information of each type of reference node and the optimized supercritical carbon dioxide function equation, add the information of each type of reference node to the optimized supercritical carbon dioxide function equation in turn, obtain the dependent variable of the corresponding reference node, and send the dependent variable of each type of reference node to the table construction module; The dependent variables are all carbon dioxide thermodynamic quantities except the corresponding reference nodes and the reference values ​​of each carbon dioxide thermodynamic quantity when applied. There are multiple dependent variables for each type of reference node, and the dependent variables are supercritical carbon dioxide parameters. A table construction module, configured to construct a corresponding two-dimensional node information table according to each type of received reference node information and the dependent variable of the reference node; Each two-dimensional node information table includes a type of reference node information and all reference values ​​of a dependent variable corresponding to the reference node information, specifically: A two-dimensional table is constructed based on each type of reference node information and all reference values ​​of a dependent variable of the reference node, and the x in the current reference node information is converted into a i ' Arrange them in the first row of the two-dimensional table in ascending order, and put the y i 'Arrange them in the first column of the two-dimensional table in ascending order, and store the reference value of the current dependent variable in the corresponding x i ′ and y i ’, and obtain the two-dimensional node information table; similarly, obtain the two-dimensional node information table of each type of reference node information and different dependent variables, as well as all the two-dimensional node information tables of all reference node information; The two-dimensional node information table is stored in a computer system in a .csv format; The query module is used to receive the input node information to be queried and the dependent variable to be solved sent by the user, find the corresponding two-dimensional node information table in the table construction module according to the input node information to be queried and the dependent variable to be solved, and use the hash function to query and output the actual value of the dependent variable corresponding to the input node information on the two-dimensional node information table.

10. A storable medium, characterized in that: The storable medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the supercritical carbon dioxide parameter calculation method according to any one of claims 1 to 8.

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