A method for acquiring steady-state operation data of a topologically driven heat cycle system

Abstract

A kind of topology-driven thermodynamic cycle system steady-state operation data acquisition method;The thermodynamic cycle system steady-state operation data acquisition method in the present application is to obtain the topology structure corresponding to various devices in the constructed thermodynamic cycle system, establish a topology standard information library after analyzing and identifying the corresponding topology structure, establish the corresponding constraint equation according to the topology standard information library, obtain the output system steady-state operation data through the calculation of the corresponding constraint equation, the process of obtaining the topology structure corresponding to various devices in the constructed thermodynamic cycle system is to obtain the topology structure corresponding to the constructed thermodynamic cycle system, determine the composition structure and connection relationship corresponding to the topology structure, and identify the composition structure and connection relationship corresponding to the topology structure;The present application analyzes the system topology structure and automatically constructs the system thermodynamic equation, realizes the unified modeling and solving of the thermodynamic cycle system, so as to improve the generality and standard corresponding automation of system modeling.

Description

Technical Field

[0001] This invention specifically relates to a method for acquiring steady-state operation data of a topology-driven thermodynamic cycle system. Background Technology

[0002] Thermodynamic cycle systems are widely used in energy and power engineering. A thermodynamic cycle system refers to a complete set of equipment and processes that continuously undergo a series of state changes with working fluids such as water, steam, air, and refrigerant to complete a closed loop, achieving the mutual conversion of thermal energy and mechanical energy. Essentially, it is a closed-loop energy conversion and processing system. Examples include gas turbine cycles, steam power cycles, and various advanced power cycle systems. With the increasing demands for energy efficiency and the growing complexity of system structures, the design and performance analysis of thermodynamic cycle systems increasingly rely on system-level modeling and simulation methods. By establishing a system thermodynamic model, the thermodynamic state parameters, system efficiency, and equipment operating characteristics of the cycle system can be analyzed, providing an important basis for system structure design and optimization.

[0003] Currently, modeling methods for thermodynamic cycle systems typically employ a sequential computation-based approach. This method first determines the computational order of the equipment based on the specific cycle structure, then calculates the outlet thermodynamic state parameters of each device sequentially according to their connection order, thus gradually completing the calculation of the entire cycle system. This method is feasible for cycle systems with simple structures and well-defined computational paths. However, it is highly dependent on the pre-determined cycle structure and equipment computational order. When the system structure changes, the computational process often needs to be redesigned, resulting in poor model versatility.

[0004] Furthermore, with the development of advanced dynamic cycle systems, system structures often include multiple coupled devices such as multi-stage compression, multi-stage expansion, and complex heat exchange networks, making the system structure more complex. In this context, traditional sequential calculation methods are difficult to flexibly adapt to the modeling needs of thermodynamic systems with different topologies. The system modeling process often requires a large amount of manual modeling and programming for specific cycle structures, resulting in low modeling efficiency. Moreover, such sequential calculation methods usually require manual determination of the calculation path and the achievement of system convergence through multi-level nested iterations, leading to a complex modeling process and making it difficult to achieve unified modeling and solving of different thermodynamic cycle system structures.

[0005] While existing equation-driven methods exist, they still rely on human experience to select unknown variables during the variable determination process. They lack standardized and precise unified automatic screening and elimination mechanisms, making it difficult to adapt to the corresponding precise modeling methods for complex topological systems. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, a method for acquiring steady-state operation data of a topology-driven thermodynamic cycle system and a method for continuous multi-mode monitoring of snow depth are provided to solve the above problems.

[0007] A method for acquiring steady-state operation data of a thermodynamic cycle system based on topology-driven approach is proposed. The method involves acquiring the topology corresponding to various devices in the thermodynamic cycle system, parsing and identifying the corresponding topology to establish a topology standard information database, establishing corresponding constraint equations based on the topology standard information database, and obtaining the output steady-state operation data of the system through the calculation of the corresponding constraint equations.

[0008] As a preferred solution, the process of obtaining the topology corresponding to various devices in the constructed thermal cycle system is as follows: First, obtain the topology corresponding to the constructed thermal cycle system. After determining the composition and connection relationships of the topology, identify the composition and connection relationships of the topology. The topology includes the connection ports of various devices and the connectivity between the connection ports. The connection ports that are interconnected and reach the predetermined indicators are divided into the same fluid nodes. Then, determine the type of fluid node, count the types of fluid nodes and the corresponding number sets to construct a topology standard information database.

[0009] As a preferred approach, the process of obtaining the steady-state operating data of the output system through the calculation of the corresponding constraint equations involves automatically constructing a system-level nonlinear algebraic equation system by combining the nodal conservation relationships, equipment physical constraints, and system boundary conditions; and finally, using numerical methods to solve the equation system to obtain the steady-state operating data of the thermodynamic cycle system.

[0010] As a preferred option: the process of establishing corresponding constraint equations based on the topology standard information database involves generating a set of system state variables and unknowns from the topology standard information database, and adjusting the constraint equations corresponding to the equipment based on the system state variables and unknowns. Specifically, the process of generating the set of system state variables and unknowns from the topology standard information database is as follows:

[0011] Define a set of candidate system state variables for each fluid node, and mark the state variables as known based on the boundary conditions input by the user to form a set of known variables;

[0012] Based on the system topology information table and equipment constraint equations, establish the mapping relationship between variables and equations;

[0013] Define variable state function When the variable The value is 1 when the variable is known, and 1 when the variable is unknown. When it is an unknown variable, it takes the value 0;

[0014] Calculate the unknown function of the equation The calculation formula is:

[0015]

[0016] In the above formula, This represents the i-th equation in the set of system equations;

[0017] Calculate the equation with known degree function The calculation formula is:

[0018] ;

[0019] In the above formula, Represents the i-th equation in the set of system equations. The degree of known variables is a function used to characterize the number of known variables in the current equation. Representing variables The state function.

[0020] The set of equations is filtered using an unknown function to construct a candidate solvable set. The calculation formula is as follows:

[0021] ;

[0022] In the above formula, E1 represents the set of candidate solvable equations; U(e i )=1 indicates that the current equation contains only one unknown variable;

[0023] Then calculate the solvability function. The calculation formula is:

[0024] ;

[0025] In the above formula, where To prevent tiny positive numbers with a denominator of zero; Equation The known degree function; Equation The unknown function.

[0026] When there are multiple candidate equations in the set of solvable equations that satisfy the condition of having only one unknown variable, the candidate equations are sorted according to the calculation results of the solvability function, and then... The order of calculation results from largest to smallest determines the solution priority. The corresponding classification of solution priorities is as follows:

[0027] Set the solvability threshold parameters θ_high and θ_low, where:

[0028] 1 > θ_high > θ_low > 0

[0029] In the above formula: θ_high represents the high solvability threshold; θ_low represents the low solvability threshold;

[0030] Based on the solvability function value R(e_i), the candidate equations are classified into the following categories:

[0031] The first type of candidate equation is a highly solvable equation, and the conditions for a highly solvable equation are:

[0032] R(e_i) ≥ θ_high

[0033] Equations with high solvability are assigned the highest solution priority.

[0034] The second type of candidate equation is an equation with moderate solvability. Equations with moderate solvability satisfy the following conditions:

[0035] θ_low ≤ R(e_i) < θ_high

[0036] Equations with moderate solvability are assigned the second-highest solution priority.

[0037] The third type of candidate equation is the low solvability equation, which satisfies the following condition:

[0038] R(e_i) < θ_low

[0039] Equations with low solvability are assigned a lower solution priority.

[0040] The solution priority can be determined by following the order of the first type of candidate equation, the second type of candidate equation, and the third type of candidate equation;

[0041] Within the same category, the results of the solvability function R(e_i) are sorted in descending order, and the candidate equations with the highest ranking are selected as the best-fit equations for solving.

[0042] After solving the best-fit equation, the variables in the best-fit equation are marked as known variables, and the equations containing these variables are updated to determine the influence domain of the variables. For containing variables The set of equations is used to update the unknown function only for equations within the influence domain. This is achieved by removing solved variables from the equations, thus completing a stepwise elimination process for the unknown variables. This continuous process of filtering, solving, and updating is repeated until no equations satisfying the given conditions are found. After obtaining the candidate equation with =1, the remaining variables are obtained. The remaining variables are the set of unknowns in the system, and the remaining variables are the output data of the steady-state operation data of the system.

[0043] As a preferred approach: After statistically analyzing the types of fluid nodes and the corresponding number sets to form the topology analysis results, establish the mapping relationship between various device connection ports and the corresponding fluid nodes. That is, based on the system topology information table and device constraint equations, establish the mapping relationship between variables and equations, and then extract the flow path information of the working fluid in the thermodynamic cycle system based on the topology analysis results to form the fluid network structure.

[0044] As a preferred option: each fluid node consists of a set of connected device ports, representing an independent thermodynamic state point in the thermodynamic cycle system, with each device port within the same node sharing the same thermodynamic state parameters;

[0045] In a fluid network structure, the interconnection between the connection ports achieves predetermined indicators, including port connectivity indicators, connection performance indicators, and topology connection strength indicators.

[0046] Port connectivity metrics include the number of port connections, port degree, out-degree, in-degree, port connectivity reliability, connectivity, cut point degree, and redundancy.

[0047] Connectivity performance metrics include adjacency count, link betweenness, and shortest path data;

[0048] Topology connectivity metrics include port bandwidth, port utilization, and throughput.

[0049] As a preferred option: include variables The set of equations undergoes a consistency check between the number of equations and the number of unknowns to ensure the variables are consistent. The set of equations satisfies the solvability condition, and the degree-of-freedom consistency verification process is as follows:

[0050] variables After representing the set of equations in the form of an unknown vector, the unknowns are automatically generated, and the expression is:

[0051]

[0052] In the above formula, Indicates unknown thermodynamic parameters in the system;

[0053] The device constraint equations are invoked. Based on the type of fluid node obtained after identification, the predefined constraint equations corresponding to each device are invoked, and the constraint equations are mapped to the corresponding devices.

[0054] When the equipment is a turbine, the turbine's operation process is calculated using the isentropic efficiency relationship, and the calculation formula is as follows:

[0055]

[0056] In the above formula, Specific enthalpy at the turbine inlet; For turbine outlet specific enthalpy; The specific enthalpy at the isentropic expansion outlet; To improve entropy efficiency;

[0057] When the device is a compressor, the compressor's operation process is calculated using the isentropic efficiency relationship, and the calculation formula is as follows:

[0058]

[0059] In the above formula, The isentropic compression outlet specific enthalpy;

[0060] When the equipment is a heat exchanger, the calculation formula for the heat exchange process between the hot fluid and the cold fluid is determined by the energy conservation principle:

[0061]

[0062] In the above formula, These represent the mass flow rates on the hot and cold sides, respectively. Specific enthalpy of the hot-side fluid inlet; Specific enthalpy of the hot-side fluid outlet; Specific enthalpy of the cold-side fluid inlet; Specific enthalpy of the cold-side fluid outlet;

[0063] The formula for calculating the isentropic exit state satisfying the relationship is as follows: ;

[0064] In the above formula, The specific entropy of the outlet working fluid in an isentropic process;

[0065] The specific entropy of the working fluid at the equipment inlet;

[0066] The formula for determining the corresponding thermodynamic state parameters using the working fluid's physical property functions is as follows:

[0067]

[0068] In the above formula, Specific enthalpy of the working fluid; The working fluid temperature;

[0069] P is the working fluid pressure; The working fluid property function represents the functional relationship between specific enthalpy and temperature and pressure, and is determined through a property database or equation of state.

[0070] The beneficial effects of this invention are as follows:

[0071] This invention, in the process of acquiring steady-state operating data of thermodynamic cycle systems, does not rely on a pre-set cycle structure. It can adapt to different thermodynamic system structures according to specific requirements, and perform a unified, standardized, and accurate modeling and solution process. This improves the versatility and automation of system modeling, and enhances the accuracy of the unified and standardized processing. This invention simultaneously improves the versatility, automation, and computational efficiency of system modeling. This invention provides a standardized and accurate processing method for the analysis of complex thermodynamic system structures and performance.

[0072] The processing flow of this invention is as follows: First, the system equipment and their connection relationships are constructed to form a system topology; then, the nodes and equipment ports in the system are identified through topology analysis, and the connection relationships between ports and nodes are established to construct a system topology information table; on this basis, the system state variables and unknown quantity set are automatically generated, and predefined equipment constraint equations are called according to the equipment type; then, the system-level nonlinear algebraic equation system is automatically constructed by combining the node conservation relationship, equipment physical constraints and system boundary conditions; finally, the equation system is solved using numerical methods to obtain the steady-state operating parameters of the thermodynamic cycle system. Attached Figure Description

[0073] Figure 1 This is a flowchart of the present invention;

[0074] Figure 2 A schematic diagram of the process for generating system variables and constructing equations. Detailed Implementation

[0075] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0076] Specific implementation method one: Combining Figure 1 and Figure 2 This embodiment describes a method for acquiring steady-state operating data of a thermodynamic cycle system. The method involves acquiring the topology corresponding to various devices within the thermodynamic cycle system, analyzing and identifying the corresponding topology to establish a topology standard information database, establishing corresponding constraint equations based on the topology standard information database, and then calculating the corresponding constraint equations to obtain the output steady-state operating data of the system.

[0077] Specific Implementation Method Two: This implementation method is a further limitation of Specific Implementation Method One. In this implementation method, the process of obtaining the topology corresponding to multiple devices in the constructed thermal cycle system is as follows: First, obtain the topology corresponding to the constructed thermal cycle system. After determining the composition structure and connection relationship of the topology, identify the composition structure and connection relationship of the topology. The topology includes the connection ports of multiple devices and the connectivity between the connection ports. The connection ports that are interconnected and reach a predetermined index are divided into the same fluid node. Then, determine the type of fluid node and count the types of fluid nodes and the corresponding number of types to construct a topology standard information database.

[0078] In this embodiment, various devices include compressors, turbines, heat exchangers, coolers, etc., and one or more of the compressors, turbines, heat exchangers, and coolers are used in combination.

[0079] In this embodiment, the system topology can be represented by a graph structure, which is a directed graph. Device units are represented as vertices in the graph, the connection relationships between devices are represented as edges, and the direction of the edges represents the flow direction of fluid between devices.

[0080] Specific Implementation Method 3: This implementation method is a further limitation of Specific Implementation Method 1 or 2. In this implementation method, the process of obtaining the steady-state operation data of the output system by calculating the corresponding constraint equations is to automatically construct a system-level nonlinear algebraic equation system by combining the node conservation relationship, equipment physical constraints and system boundary conditions; and finally, to solve the equation system by numerical methods to obtain the steady-state operation data of the thermodynamic cycle system.

[0081] In this embodiment, the system-level nonlinear algebraic equations include nodal mass conservation equations, nodal energy conservation equations, and equipment constraint equations.

[0082] Specific Implementation Method Four: This implementation method is a further limitation of Specific Implementation Methods One, Two, or Three. In this implementation method, the process of establishing corresponding constraint equations based on the topology standard information library is as follows: The system state variables and unknown quantity set are generated through the topology standard information library, and the corresponding constraint equations for the equipment are adjusted based on the system state variables and unknown quantity set. Specifically, the process of generating the system state variables and unknown quantity set through the topology standard information library is as follows:

[0083] Define a set of candidate system state variables for each fluid node, and mark the state variables as known based on the boundary conditions input by the user to form a set of known variables;

[0084] Based on the system topology information table and equipment constraint equations, establish the mapping relationship between variables and equations;

[0085] Define variable state function When the variable The value is 1 when the variable is known, and 1 when the variable is unknown. When it is an unknown variable, it takes the value 0;

[0086] Calculate the unknown function of the equation The calculation formula is:

[0087]

[0088] In the above formula, This represents the i-th equation in the set of system equations;

[0089] Calculate the equation with known degree function The calculation formula is:

[0090] ;

[0091] In the above formula, Represents the i-th equation in the set of system equations. The degree of known variables is a function used to characterize the number of known variables in the current equation. Representing variables The state function.

[0092] The set of equations is filtered using an unknown function to construct a candidate solvable set. The calculation formula is as follows:

[0093] ;

[0094] In the above formula, E1 represents the set of candidate solvable equations; U(ei)=1 indicates that the current equation contains only one unknown variable;

[0095] Then calculate the solvability function. The calculation formula is:

[0096] ;

[0097] In the above formula, where To prevent tiny positive numbers with a denominator of zero; Equation The known degree function; Equation The unknown function.

[0098] When there are multiple candidate equations in the set of solvable equations that satisfy the condition of having only one unknown variable, the candidate equations are sorted according to the calculation results of the solvability function, and then... The order of calculation results from largest to smallest determines the solution priority. The corresponding classification of solution priorities is as follows:

[0099] Set the solvability threshold parameters θ_high and θ_low, where:

[0100] 1 > θ_high > θ_low > 0

[0101] In the above formula: θ_high represents the high solvability threshold; θ_low represents the low solvability threshold;

[0102] Based on the solvability function value R(e_i), the candidate equations are classified into the following categories:

[0103] The first type of candidate equation is a highly solvable equation, and the conditions for a highly solvable equation are:

[0104] R(e_i) ≥ θ_high

[0105] Equations with high solvability are assigned the highest solution priority.

[0106] The second type of candidate equation is an equation with moderate solvability. Equations with moderate solvability satisfy the following conditions:

[0107] θ_low ≤ R(e_i) < θ_high

[0108] Equations with moderate solvability are assigned the second-highest solution priority.

[0109] The third type of candidate equation is the low solvability equation, which satisfies the following condition:

[0110] R(e_i) < θ_low

[0111] Equations with low solvability are assigned a lower solution priority.

[0112] The solution priority can be determined by following the order of the first type of candidate equation, the second type of candidate equation, and the third type of candidate equation.

[0113] Within the same category, the solvability function R(e_i) is sorted in descending order, and the candidate equations ranked higher are selected as the best-fit equations for solving. In a typical implementation, based on engineering experience, θ_high = 0.7 and θ_low = 0.3 are set. However, these values ​​can be adjusted according to specific circumstances.

[0114] After solving the best-fit equation, the variables in the best-fit equation are marked as known variables, and the equations containing these variables are updated to determine the influence domain of the variables. For containing variables The set of equations is used to update the unknown function only for equations within the influence domain. This is achieved by removing solved variables from the equations, thus completing a stepwise elimination process for the unknown variables. This continuous process of filtering, solving, and updating is repeated until no equations satisfying the given conditions are found. After obtaining the candidate equation with =1, the remaining variables are obtained. The remaining variables are the set of unknowns in the system, and the remaining variables are the output data of the steady-state operation data of the system.

[0115] In this embodiment, the topology information included in the topology standard information database specifically includes port number, device number, and corresponding node number. The above information is used to describe the topology connection structure between system devices.

[0116] In this embodiment, the system state variables include one or more of temperature, pressure, specific enthalpy, and mass flow rate, and the corresponding combination is selected according to specific predetermined requirements.

[0117] The equipment constraint equations in this embodiment include the equipment energy conservation equation, equipment efficiency relationship, and other similar equations.

[0118] In this embodiment, the numerical solution method is Newton's iteration method, quasi-Newton method, or other numerical solution methods for nonlinear equations.

[0119] Specific Implementation Method 5: This implementation method is a further limitation of Specific Implementation Methods 1, 2, 3 or 4. In this implementation method, after statistically analyzing the types of fluid nodes and the corresponding number sets of the types to form the topology analysis results, a mapping relationship between various device connection ports and the corresponding fluid nodes is established. That is, based on the system topology information table and the device constraint equations, a mapping relationship between variables and equations is established, and then the flow path information of the working fluid in the thermodynamic cycle system is extracted based on the topology analysis results to form a fluid network structure.

[0120] In this embodiment, during the process of defining the candidate system state variable set for each fluid node, the unknown system variables are first screened and determined through the mapping relationship between variables and equations. The screening and determination process includes solving equations containing only one unknown variable and updating the set of known variables. In this embodiment, the fluid network structure of the thermodynamic cycle system includes connection ports of various devices, namely the connection ports of compressors, turbines, heat exchangers, condensers, and connecting pipes; each device includes several fluid ports, and the connection ports of compressors, turbines, heat exchangers, condensers, and connecting pipes are connected by working fluid.

[0121] Specific Implementation Method Six: Combination Figure 1 and Figure 2 This embodiment describes the specific steps of the method for acquiring steady-state operation data of the thermodynamic cycle system as follows:

[0122] Step 1: System Topology Input and Identification: Obtain the constructed thermodynamic cycle system topology, which includes multiple equipment units and their connection relationships, and identify the topology to determine the types of equipment and their connection forms contained in the system.

[0123] Step 2: System Topology Analysis and Node Identification: The system topology is analyzed to identify the connection ports of each device; based on the connectivity between ports, interconnected device ports are divided into the same fluid nodes, thereby determining the set of nodes in the system; wherein, each fluid node represents an independent thermodynamic state point in the system, and the device ports within the same node share the same thermodynamic state parameters;

[0124] Step 3: Establish a system topology information database, specifically a topology information table: Based on the topology parsing results obtained in Step 2, construct a system topology information table to record device port numbers, associated device numbers, and corresponding node numbers; the system topology information table is used to structurally describe the system topology connection relationships and provide a data foundation for subsequent system variable generation and equation construction.

[0125] Step 4: Generate the system state variables and unknowns set. Based on the system topology and boundary conditions, the system state variables and unknowns set are automatically generated. Specifically, in the process of equation-guided modeling of thermodynamic systems, an equation screening mechanism based on variable unknown criterion, a solvability evaluation mechanism, and a variable propagation elimination mechanism are introduced to realize the automatic determination of the system unknowns set.

[0126] The system state variables and unknowns set includes defining a candidate system state variable set for each fluid node, and marking the state variables as known based on the boundary conditions input by the user, forming a known variable set;

[0127] Based on the system topology information table and equipment constraint equations, establish the mapping relationship between variables and equations;

[0128] Define variable state function When the variable The value is 1 if the variable is known, otherwise it is 0.

[0129] Define the unknown function of the equation for:

[0130]

[0131] And define the equation with known degree function for:

[0132]

[0133] Based on the aforementioned unknown function, the set of equations is filtered to construct a set of candidate solvable equations:

[0134]

[0135] Define the solvability function of the equation:

[0136]

[0137] Where ε is a tiny positive number to prevent the denominator from being zero;

[0138] The candidate equations are sorted according to the solvability function, and the optimal equation is selected for solution.

[0139] After a variable is solved, it is marked as a known variable, and the equations containing that variable are updated; the influence domain of the variable is defined. For containing variables The set of equations, updating the unknown function only for equations within the influence domain;

[0140] By removing solved variables from the equation, the unknown variables in the equation are gradually eliminated; the above screening-solving-updating process is repeated until there are no candidate equations that meet the conditions, and the remaining variables are output as the set of unknowns of the system.

[0141] Step 5: Invoke the device constraint equations: Based on the device units identified in Step 1, invoke the predefined constraint equations corresponding to each device, and map the constraint equations to the corresponding device instances;

[0142] Step Six: Constructing a System-Level Equation Set: Combining the nodal mass conservation relationship, the nodal energy conservation relationship, and the equipment constraint equations described in Step Five, and introducing system boundary conditions, a system-level nonlinear algebraic equation set describing the steady-state thermodynamic parameters of the system is constructed, thereby achieving unified modeling of thermodynamic cycle systems with different topologies.

[0143] Specifically, the system mass conservation equation and energy conservation equation are established based on the connection relationship of the system nodes.

[0144] For any fluid node in the system, a mass conservation relationship can be established:

[0145]

[0146] Simultaneously establish the nodal energy conservation relationship:

[0147]

[0148] The nodal conservation equations can be used to describe the flow and energy transfer relationships of fluids between different devices in a system.

[0149] By combining equipment constraint equations, nodal conservation equations, and system boundary conditions, a set of nonlinear algebraic equations describing the system's operating state is automatically generated.

[0150] Step 7: Steady-state solution of the system: Under the condition that the number of system equations matches the number of unknowns, the nonlinear algebraic equations are solved by numerical solution methods to obtain the steady-state operating parameters of the thermodynamic cycle system.

[0151] Specifically, the steady-state solution of the system:

[0152] The generated system of equations can be expressed as:

[0153]

[0154] In the above formula, The system's unknown vector; This is the system of equations.

[0155] The structure of the system equations can be automatically determined by the topological analysis results, enabling the automatic construction of system equations without having to rebuild the calculation model for different thermodynamic cycle structures.

[0156] When the number of system equations matches the number of unknowns, numerical methods can be used to solve the system equations.

[0157] In this embodiment, the system equations can be solved using Newton's iteration method or other nonlinear equation solving methods. By continuously updating the unknowns of the system and calculating the equation residuals until the preset convergence conditions are met, the thermodynamic state parameters of each node of the system can be obtained.

[0158] Through the above steps, unified modeling and solving of thermodynamic cycle systems with different structural forms can be achieved accurately and systematically. Procedures not mentioned in this embodiment are the same as those in specific embodiments one, two, three, four, five, or six.

[0159] Specific Implementation Method Seven: This implementation method is a further limitation of Specific Implementation Method Six. Step four of this implementation method also includes the following: First, define a set of candidate system state variables for each fluid node, including temperature T, pressure P and specific enthalpy h. Based on the connection relationship described in the system topology information table, determine the mass flow rate m of each connection branch, thereby constructing the set of system state variables.

[0160] Secondly, the state variables are marked as known based on the boundary conditions input by the user, forming a set of known variables; on this basis, a mapping relationship between variables and equations is established based on the system topology information table and equipment constraint equations.

[0161] Furthermore, define the variable state function. And construct the unknown function of the equation. and the equation with known degree function This is used to quantify the distribution of unknown and known variables in the equation; based on the unknown function... Equations containing only a single unknown variable are filtered out to construct a set of candidate solvable equations;

[0162] At the same time, a solvability function is introduced. The candidate equations are evaluated and ranked to determine the equations to be solved first. After the variables are solved, they are marked as known variables, and the unknown function is updated only for the relevant equations based on the influence domain of the variables, so as to realize the local propagation of variable information.

[0163] In the above process, the equation structure is gradually simplified and the unknown variables are gradually eliminated by removing the solved variables from the relevant equations; the process of screening-solving-propagation-elimination is repeated until there are no directly solvable equations in the system; finally, the undetermined variables are output as the set of unknowns of the system.

[0164] In this embodiment, the variable selection and elimination process involves establishing the mapping relationship between variables and equations, and then introducing a criterion for the degree of unknown variables to achieve automatic selection and determination of unknown variables in the system.

[0165] First, define the variable state function:

[0166]

[0167] Redefine the unknown function of the equation:

[0168] ;

[0169] And the equation's degree function is known:

[0170] ;

[0171] in, Equation Number of unknown variables This indicates the number of known variables.

[0172] Based on the aforementioned unknown function, the set of equations is filtered to construct a set of candidate solvable equations:

[0173] ;

[0174] When there are multiple equations in the candidate equation set, a solvability function is introduced:

[0175] ;

[0176] Where ε is a tiny positive number to prevent the denominator from being zero;

[0177] And select those that meet the requirements. The largest equation is used as the current equation to solve.

[0178] For a selected equation, if it is an explicit solvable form, the unknown variables are solved directly; if it is an implicit nonlinear equation, numerical methods are used to solve it.

[0179] In variables Once determined, it is marked as a known variable, i.e. ;

[0180] Simultaneously define the influence domain of the variables:

[0181] ;

[0182] Only the unknown function is updated for the equations within the influence domain. and known degree function This enables the local propagation of variable information.

[0183] During the variable update process, the equation structure is updated by removing the variable x_k from the relevant equations:

[0184] ;

[0185] This gradually reduces the number of unknown variables in the equation, achieving gradual elimination of variables.

[0186] The formula for calculating the total unknowns of the system is defined as follows:

[0187] ;

[0188] when When the variable selection and elimination process stops changing during continuous iterations, it is considered terminated. The above selection-solution-propagation-elimination process is then repeated.

[0189] Specific Implementation Method Eight: This implementation method is a further limitation of Specific Implementation Method One, Two, Three, Four, Five, Six or Seven. In this implementation method, each fluid node is composed of a set of connected device ports, representing an independent thermodynamic state point in the thermodynamic cycle system. Each device port in the same node shares the same thermodynamic state parameters.

[0190] In a fluid network structure, the interconnection between the connection ports achieves predetermined indicators, including port connectivity indicators, connection performance indicators, and topology connection strength indicators.

[0191] Port connectivity metrics include the number of port connections, port degree, out-degree, in-degree, port connectivity reliability, connectivity, cut point degree, and redundancy.

[0192] Connectivity performance metrics include adjacency count, link betweenness, and shortest path data;

[0193] Topology connectivity metrics include port bandwidth, port utilization, and throughput.

[0194] Specific Implementation Method Nine: This implementation method is a further limitation of Specific Implementation Methods One, Two, Three, Four, Five, Six, Seven, or Eight. This implementation method includes variables. The set of equations undergoes a consistency check between the number of equations and the number of unknowns to ensure the variables are consistent. The set of equations satisfies the solvability condition, and the degree-of-freedom consistency verification process is as follows:

[0195] variables After representing the set of equations in the form of an unknown vector, the unknowns are automatically generated, and the expression is:

[0196]

[0197] In the above formula, Indicates unknown thermodynamic parameters in the system;

[0198] The device constraint equations are invoked. Based on the type of fluid node obtained after identification, the predefined constraint equations corresponding to each device are invoked, and the constraint equations are mapped to the corresponding devices.

[0199] In this embodiment, various devices include compressors, turbines, heat exchangers, and coolers.

[0200] When the equipment is a turbine, the turbine's operation process is calculated using the isentropic efficiency relationship, and the calculation formula is as follows:

[0201]

[0202] In the above formula, Specific enthalpy at the turbine inlet; For turbine outlet specific enthalpy; The specific enthalpy at the isentropic expansion outlet; To improve entropy efficiency;

[0203] When the device is a compressor, the compressor's operation process is calculated using the isentropic efficiency relationship, and the calculation formula is as follows:

[0204]

[0205] In the above formula, The isentropic compression outlet specific enthalpy;

[0206] When the equipment is a heat exchanger, the calculation formula for the heat exchange process between the hot fluid and the cold fluid is determined by the energy conservation principle:

[0207]

[0208] In the above formula, These represent the mass flow rates on the hot and cold sides, respectively. Specific enthalpy of the hot-side fluid inlet; Specific enthalpy of the hot-side fluid outlet; Specific enthalpy of the cold-side fluid inlet; Specific enthalpy of the cold-side fluid outlet;

[0209] The formula for calculating the isentropic exit state satisfying the relationship is as follows: ;

[0210] In the above formula, The specific entropy of the outlet working fluid in an isentropic process;

[0211] The specific entropy of the working fluid at the equipment inlet;

[0212] The formula for determining the corresponding thermodynamic state parameters using the working fluid's physical property functions is as follows:

[0213]

[0214] In the above formula, Specific enthalpy of the working fluid; The working fluid temperature;

[0215] P is the working fluid pressure; The working fluid property function represents the functional relationship between specific enthalpy and temperature and pressure, and is determined through a property database or equation of state.

[0216] Equipment constraint equations are used to describe the thermodynamic relationship between the equipment inlet and outlet, including but not limited to the isentropic efficiency relationship between the compressor and the turbine and the energy conservation relationship of the heat exchanger, thereby establishing constraints at the equipment level.

[0217] The turbine, compressor, and heat exchanger data involved in this embodiment are detailed in Table 1 below:

[0218] Table 1

[0219] P1 D1 (Compressor) N1 P2 D1 (Compressor) N2 P3 D2 (Heat Exchanger) N2 P4 D2 (Heat Exchanger) N3 P5 D3 (Turbine) N3 P6 D3 (Turbine) N1

[0220] This invention enables a unified modeling process for thermodynamic cycle systems.

[0221] This invention models systems based on their topology. By analyzing the system topology and automatically identifying the connections between devices, it can be applied to modeling thermodynamic cycle systems with different structural forms, thus improving the versatility of the system modeling method.

[0222] This invention can also automatically generate system thermodynamic equations. By establishing a system topology information table and calling the equipment constraint equations, it can automatically construct a system-level thermodynamic balance equation set, reducing the workload of manual modeling and improving system modeling efficiency.

[0223] This invention adopts the equation-oriented modeling approach, representing the entire thermodynamic cycle system as a unified set of nonlinear algebraic equations, and solving them through numerical methods. It does not rely on the traditional sequential calculation process, thus adapting to the modeling needs of complex thermodynamic cycle systems.

[0224] This invention also has good scalability, can be applied to thermodynamic cycle systems of different scales and topologies, and can extend the equipment constraint model according to the equipment type, thus having good scalability.

[0225] This invention can also automatically determine the unknowns of the system. In the process of modeling guided by the equations of a thermodynamic system, this invention introduces a criterion for the degree of unknown variables and a variable propagation and update mechanism to automatically screen and determine the set of unknowns of the thermodynamic system, avoids manually specifying unknown variables, improves the degree of automation of modeling, and improves the solution efficiency of complex systems.

Claims

1. A method for acquiring steady-state operation data of a topology-driven thermodynamic cycle system, characterized in that: The method for acquiring steady-state operating data of a thermodynamic cycle system involves obtaining the topology corresponding to various devices in the thermodynamic cycle system, analyzing and identifying the corresponding topology to establish a topology standard information database, establishing corresponding constraint equations based on the topology standard information database, and obtaining the output steady-state operating data of the system through the calculation of the corresponding constraint equations.

2. The method for acquiring steady-state operation data of a topology-driven thermodynamic cycle system according to claim 1, characterized in that: The process of obtaining the topology corresponding to various devices in a constructed thermal cycle system is as follows: First, obtain the topology corresponding to the constructed thermal cycle system. After determining the composition and connection relationships of the topology, identify the composition and connection relationships of the topology. The topology includes the connection ports of various devices and the connectivity between the connection ports. The connection ports that are interconnected and reach the predetermined indicators are divided into the same fluid nodes. Then, determine the type of fluid node, count the types of fluid nodes and the corresponding number of types to construct a topology standard information database.

3. A method for acquiring steady-state operation data of a topology-driven thermodynamic cycle system according to claim 1 or 2, characterized in that: The process of obtaining the steady-state operating data of the output system by calculating the corresponding constraint equations involves automatically constructing a system-level nonlinear algebraic equation system by combining the nodal conservation relationships, equipment physical constraints, and system boundary conditions; and finally, solving the equation system using numerical methods to obtain the steady-state operating data of the thermodynamic cycle system.

4. The method for acquiring steady-state operation data of a topology-driven thermodynamic cycle system according to claim 3, characterized in that: The process of establishing corresponding constraint equations based on the topology standard information database involves generating a set of system state variables and unknowns from the topology standard information database, and then adjusting and forming the corresponding constraint equations for the equipment based on the system state variables and unknowns. Specifically, the process of generating the set of system state variables and unknowns from the topology standard information database is as follows: Define a set of candidate system state variables for each fluid node, and mark the state variables as known based on the boundary conditions input by the user to form a set of known variables; Based on the system topology information table and equipment constraint equations, establish the mapping relationship between variables and equations; Define variable state function When the variable The value is 1 when the variable is known, and 1 when the variable is unknown. When it is an unknown variable, it takes the value 0; Calculate the unknown function of the equation The calculation formula is: In the above formula, This represents the i-th equation in the set of system equations; Calculate the equation with known degree function The calculation formula is: ； In the above formula, Represents the i-th equation in the set of system equations. The degree of known variables is a function used to characterize the number of known variables in the current equation. Representing variables State function; The set of equations is filtered using an unknown function to construct a candidate set of solvable equations. The calculation formula is as follows: ； In the above formula, E1 represents the set of candidate solvable equations; U(e i )=1 indicates that the current equation contains only one unknown variable; Then calculate the solvability function. The calculation formula is: ； In the above formula, where To prevent tiny positive numbers with a denominator of zero; Equation The known degree function; Equation The unknown function; When there are multiple candidate equations in the set of solvable equations that satisfy the condition of having only one unknown variable, the candidate equations are sorted according to the calculation results of the solvability function, and then... The order of calculation results from largest to smallest determines the solution priority. The corresponding classification of solution priorities is as follows: Set the solvability threshold parameters θ_high and θ_low, where: 1 > θ_high > θ_low > 0 In the above formula: θ_high represents the high solvability threshold; θ_low represents the low solvability threshold; Based on the solvability function value R(e_i), the candidate equations are classified into the following categories: The first type of candidate equation is a highly solvable equation, and the conditions for a highly solvable equation are: R(e_i) ≥ θ_high Equations with high solvability are assigned the highest solution priority. The second type of candidate equation is an equation with moderate solvability. Equations with moderate solvability satisfy the following conditions: θ_low ≤ R(e_i) < θ_high Equations with moderate solvability are assigned the second-highest solution priority. The third type of candidate equation is the low solvability equation, which satisfies the following condition: R(e_i) < θ_low Equations with low solvability are assigned a lower solution priority. The solution priority can be determined by following the order of the first type of candidate equation, the second type of candidate equation, and the third type of candidate equation; Within the same category, the results of the solvability function R(e_i) are sorted in descending order, and the candidate equations with the highest ranking are selected as the best-fit equations for solving. After solving the best-fit equation, the variables in the best-fit equation are marked as known variables, and the equations containing these variables are updated to determine the influence domain of the variables. For containing variables The set of equations is used to update the unknown function only for equations within the influence domain. This is achieved by removing solved variables from the equations, thus completing a stepwise elimination process for the unknown variables. This continuous process of filtering, solving, and updating is repeated until no equations satisfying the given conditions are found. After obtaining the candidate equation with =1, the remaining variables are obtained. The remaining variables are the set of unknowns in the system, and the remaining variables are the output data of the steady-state operation data of the system.

5. A method for acquiring steady-state operation data of a topology-driven thermodynamic cycle system according to claim 1, 2, 3 or 4, characterized in that: After statistically analyzing the types of fluid nodes and the corresponding number sets to form the topology analysis results, a mapping relationship between various device connection ports and corresponding fluid nodes is established. That is, based on the system topology information table and device constraint equations, a mapping relationship between variables and equations is established. Then, based on the topology analysis results, the flow path information of the working fluid in the thermodynamic cycle system is extracted to form the fluid network structure.

6. The method for acquiring steady-state operation data of a topology-driven thermodynamic cycle system according to claim 2, characterized in that: Each fluid node consists of a set of connected device ports, representing an independent thermodynamic state point in the thermodynamic cycle system. Within the same node, each device port shares the same thermodynamic state parameters. In a fluid network structure, the interconnection between the connection ports achieves predetermined indicators, including port connectivity indicators, connection performance indicators, and topology connection strength indicators. Port connectivity metrics include the number of port connections, port degree, out-degree, in-degree, port connectivity reliability, connectivity, cut point degree, and redundancy. Connectivity performance metrics include adjacency count, link betweenness, and shortest path data; Topology connectivity metrics include port bandwidth, port utilization, and throughput.

7. The method for acquiring steady-state operation data of a topology-driven thermodynamic cycle system according to claim 4, characterized in that: For included variables The set of equations undergoes a consistency check between the number of equations and the number of unknowns to ensure the variables are consistent. The set of equations satisfies the solvability condition, and the degree-of-freedom consistency verification process is as follows: variables After representing the set of equations in the form of an unknown vector, the unknowns are automatically generated, and the expression is: In the above formula, Indicates unknown thermodynamic parameters in the system; The device constraint equations are invoked. Based on the type of fluid node obtained after identification, the predefined constraint equations corresponding to each device are invoked, and the constraint equations are mapped to the corresponding devices. When the equipment is a turbine, the turbine's operation process is calculated using the isentropic efficiency relationship, and the calculation formula is as follows: In the above formula, Specific enthalpy at the turbine inlet; For turbine outlet specific enthalpy; The specific enthalpy at the isentropic expansion outlet; To improve entropy efficiency; When the device is a compressor, the compressor's operation process is calculated using the isentropic efficiency relationship, and the calculation formula is as follows: In the above formula, The isentropic compression outlet specific enthalpy; When the equipment is a heat exchanger, the calculation formula for the heat exchange process between the hot fluid and the cold fluid is determined by the energy conservation principle: In the above formula, These represent the mass flow rates on the hot and cold sides, respectively. Specific enthalpy of the hot-side fluid inlet; Specific enthalpy of the hot-side fluid outlet; Specific enthalpy of the cold-side fluid inlet; Specific enthalpy of the cold-side fluid outlet; The formula for calculating the isentropic exit state satisfying the relationship is as follows: ; In the above formula, The specific entropy of the outlet working fluid in an isentropic process; The specific entropy of the working fluid at the equipment inlet; The formula for determining the corresponding thermodynamic state parameters using the working fluid's physical property functions is as follows: In the above formula, Specific enthalpy of the working fluid; The working fluid temperature; P is the working fluid pressure; The working fluid property function represents the functional relationship between specific enthalpy and temperature and pressure, and is determined through a property database or equation of state.

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