Three-dimensional modeling of resistance distribution of heat exchange station two-network circulation system and resistance reduction optimization method

Abstract

This invention discloses a three-dimensional modeling and drag reduction optimization method for the resistance distribution of a secondary circulation system in a heat exchange station, relating to the fields of thermal energy engineering and fluid machinery technology. First, flow-pressure data are collected through multi-condition experiments to calculate the remaining flow capacity and locate the most unfavorable operating condition. Based on the operating parameters, a three-dimensional finite element model is established, and simulations are performed using dynamically corrected critical resistance values ​​and boundary conditions to generate a distribution cloud map including friction loss and local resistance. Initial high-resistance points are extracted, and allowable resistance values ​​that meet physical rationality and error tolerance are selected, introducing a safety factor and Reynolds number-corrected resistance coefficient. A global-local coupled model is constructed, and millimeter-level mesh refinement is implemented in the high-resistance region. Virtual fluid elements and shape function displacement mapping techniques are used to reduce simulation errors. Based on the comparison between the maximum resistance value and the allowable value of the operating condition under test, an automatic optimization scheme is determined. This method provides a data-driven technical path for drag reduction in heat exchange stations.

Description

Technical Field

[0001] This invention belongs to the field of thermal energy engineering and fluid machinery technology, specifically involving a three-dimensional modeling and resistance reduction optimization method for the resistance distribution of the secondary circulation system of a heat exchange station. Background Technology

[0002] In urban heating systems, the hydraulic balance and energy consumption optimization of the secondary circulation system at heat exchange stations are crucial for ensuring heating efficiency and user comfort. However, existing technologies suffer from the following significant problems:

[0003] Limitations of Traditional Design and Operation Modes: Traditional heating system designs are typically based on static assumptions, making it difficult to dynamically match complex actual operating conditions (such as peak heating periods in winter and temperature fluctuations during transitional seasons). The diversity of pipe sizes, the complexity of component layouts, and construction quality deviations (such as localized blockages and material reductions) during the construction phase lead to significant deviations between the actual resistance characteristics of the pipe network and design expectations. Furthermore, the inappropriate selection of circulating water pumps or changes in the number of user nodes can further exacerbate hydraulic imbalances, causing problems such as uneven heating and cooling and energy waste.

[0004] Lack of multi-condition dynamic analysis and accurate modeling methods: Existing technologies for resistance analysis of secondary power grid systems mostly rely on empirical formulas or simplified models, which are difficult to fully reflect the coupling effect of frictional resistance (pipeline friction) and local resistance (valves, elbows). Especially under complex operating cycles (such as peak winter loads) and extreme temperature conditions, the system resistance distribution characteristics may change significantly, but traditional methods lack a systematic identification mechanism for the "most unfavorable operating conditions," resulting in insufficient targeting of optimization measures.

[0005] Insufficient simulation accuracy and engineering adaptability: Existing simulation tools often use a globally uniform grid during modeling, making it difficult to perform refined simulations of high-resistance regions, resulting in significant errors in resistance distribution calculations. Furthermore, the simulation boundary conditions (such as fixed flow rate or pressure input) are often disconnected from actual operating scenarios, making it difficult to dynamically correct critical resistance values ​​and limiting the reliability and operability of resistance reduction optimization schemes.

[0006] Passive management and low level of intelligence: Traditional heating systems rely on manual inspections and fixed control strategies, lacking the ability to monitor and regulate the flow and pressure at each node of the pipeline network in real time. When local blockages or hydraulic imbalances occur in the system, it is difficult to locate high resistance points in a timely manner and implement dynamic optimization, leading to increased energy consumption and a decline in heating quality at the user end.

[0007] To address the above problems, this invention proposes a three-dimensional modeling and resistance reduction optimization method for the resistance distribution of the secondary circulation system in a heat exchange station. Summary of the Invention

[0008] To overcome the shortcomings and deficiencies of the existing technology, the present invention adopts the following technical solution:

[0009] The process for 3D modeling and drag reduction optimization of the resistance distribution in the secondary circulation system of a heat exchange station is as follows:

[0010] Step 1: Design and build the secondary circulation system of the heat exchange station that requires resistance analysis. The system includes pipes, valves, heat exchangers and other core components. Clarify the topology and key component parameters of the system.

[0011] Step 2: Conduct an operational experiment on the secondary circulation system of the heat exchange station under m operating conditions. Perform resistance performance tests on the system under each operating condition cycle to determine the most unfavorable operating condition.

[0012] Step 3: Obtain the basic parameters of the secondary circulation system of the heat exchange station under the most unfavorable operating conditions. The basic parameters include pipeline parameters and fluid parameters.

[0013] Step 4: Establish a three-dimensional finite element calculation model of the secondary circulation system of the heat exchange station based on the basic parameters under the most unfavorable operating conditions, and determine the boundary conditions and critical resistance values ​​of the finite element calculation model.

[0014] Step 5: Under the most unfavorable operating conditions, perform finite element simulation calculations on the finite element calculation model based on the boundary conditions and critical resistance values ​​to obtain the resistance distribution cloud map;

[0015] Step 6: Calculate the allowable resistance value under the most unfavorable operating condition based on the resistance distribution cloud map, and embed the allowable resistance value into the finite element calculation model for verification;

[0016] Step 7: Establish the local detailed model and the overall model corresponding to the secondary circulation system of the heat exchange station;

[0017] Step 8: Perform simulation calculations on the overall model and the local refined model under the test conditions to obtain the maximum resistance value under the test conditions;

[0018] Step 9: Based on the maximum resistance value and the verified allowable resistance value, perform a resistance reduction optimization analysis on the secondary circulation system of the heat exchange station.

[0019] Preferably, in step two, determining the most unfavorable operating condition includes: obtaining n preset experimental conditions, each preset experimental condition including a preset operating cycle and a preset operating temperature; conducting operational tests on the secondary circulation system of the heat exchange station under each preset experimental condition to obtain the flow-pressure curve corresponding to each preset experimental condition; calculating the remaining flow capacity of the system under each preset experimental condition based on the flow-pressure curve of each preset experimental condition, the remaining flow capacity being the percentage of the actual flow capacity to the designed flow capacity; and determining the preset experimental condition corresponding to the lowest remaining flow capacity as the most unfavorable operating condition.

[0020] Preferably, in step three, the pipeline parameters include the pipeline roughness, diameter, and material; the fluid parameters include the fluid density and dynamic viscosity; and the most unfavorable operating condition includes the most unfavorable operating cycle and the most unfavorable operating temperature.

[0021] Preferably, in step four, the boundary conditions are to set a constant flow input at one end of the finite element calculation model and a constant pressure output at the other end; the resistance critical value is calculated by the experimental average resistance critical value, the initially set resistance critical value and the adjustment coefficient, the adjustment coefficient is between 0 and 1, and is dynamically corrected according to the experimental deviation.

[0022] Preferably, in step five, obtaining the resistance distribution cloud map includes: acquiring multiple pre-defined fluid flow directions, each fluid flow direction corresponding to a finite element calculation model under a flow state; under the most unfavorable operating condition, performing simulation calculations on the finite element calculation models corresponding to each fluid flow direction based on boundary conditions and resistance critical values ​​to obtain the resistance distribution cloud map corresponding to each direction; the resistance in the resistance distribution cloud map is obtained by linear combination of multiple resistance components, the resistance components include frictional resistance and local resistance, the frictional resistance is generated by pipe friction, and the local resistance is generated by valves and elbows.

[0023] Preferably, in step six, calculating the allowable resistance value under the most unfavorable operating condition includes: determining and extracting multiple first resistance components at the initial high resistance point of the system based on the resistance distribution cloud map; performing dimensional unification processing on the multiple first resistance components to obtain multiple second resistance components; randomly selecting any number of second resistance components for linear combination, and screening combinations that meet preset conditions to obtain a first linear combination, the preset conditions including that the combination result is non-negative and the deviation from the experimentally measured resistance is less than a set threshold; taking the first linear combination that meets the preset resistance calculation formula as the allowable resistance value, the resistance calculation formula including a safety factor, a resistance coefficient, the coefficient of each resistance component, the dimensional unification of the resistance components, and the expansion form of each resistance component, wherein the resistance coefficient is based on the fluid Reynolds number correction, and the safety factor ranges from a1 to a2.

[0024] Preferably, in step six, the verification of embedding the allowable resistance value into the finite element calculation model includes: extracting the maximum value from the resistance distribution cloud map corresponding to each fluid flow direction; taking the minimum value among the maximum values ​​as the allowable resistance value and embedding it into the finite element calculation model to complete the verification.

[0025] Preferably, in step seven, establishing the local refinement model and the overall model includes: the overall model reflects the macroscopic structure of the system, the local refinement model refines the mesh in the high-resistivity region with a refinement accuracy of millimeters; a layer of virtual fluid elements is established along the boundary of the local refinement model, the size of the virtual fluid elements is between b1 and b2 times the boundary size of the local model, and its fluid resistance is c1 to c2 orders of magnitude smaller than the fluid resistance of the overall model; the nodes of the local refinement model and the nodes of the overall model have a displacement mapping relationship through shape functions to ensure that the deformation of the two is coordinated.

[0026] Preferably, in step nine, the resistance reduction optimization analysis includes: if the maximum resistance value is less than the verified allowable resistance value, it is determined that the system can operate normally under the most unfavorable operating conditions and no optimization is required; if the maximum resistance value is greater than or equal to the verified allowable resistance value, it is determined that the system needs to be optimized for resistance reduction. The methods for resistance reduction optimization include replacing low-resistance valves, increasing the diameter of local pipes, and optimizing the pipe routing to reduce the number of bends.

[0027] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0028] 1. This invention constructs flow-pressure curves under multiple operating conditions, such as the winter peak cycle and the transitional season cycle, and combines them with quantitative evaluation of remaining flow capacity (the ratio of actual to designed flow capacity). It couples the system operating cycle with temperature parameters into the most unfavorable operating condition determination system. This overcomes the limitations of traditional single-condition analysis, dynamically capturing the extreme resistance scenarios of the secondary circulation system of the heat exchange station in complex operating environments, thus improving the reliability and safety of the system design.

[0029] 2. This invention employs a two-tiered modeling strategy: a global model and a locally refined model. The global model macroscopically represents the layout of the main pipelines and equipment, while the local model refines the mesh to millimeter-level for high-resistance areas such as valves and elbows, and eliminates boundary reflection effects through virtual fluid elements. This resolves the contradiction between local detail distortion and low global computational efficiency in traditional CFD simulations, thereby improving the accuracy of the linear combination of friction resistance and local resistance.

[0030] 3. This invention constructs a physical-statistical dual verification framework for allowable drag values ​​by introducing a safety factor, Reynolds number-corrected drag coefficient, and a linear regression model for multiple drag components. This framework satisfies both fluid mechanics theoretical constraints and incorporates experimental data-driven characteristics, achieving closed-loop verification from experimental measurements to engineering design. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 The flowchart of the three-dimensional modeling and resistance reduction optimization method for the resistance distribution of the secondary circulation system of the heat exchange station according to the present invention is shown;

[0033] Figure 2 A flowchart of step two of the present invention is shown;

[0034] Figure 3 A flowchart of step six of the present invention is shown. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more exemplary embodiments. Numerous specific details are provided in the following description to give a full understanding of exemplary embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, steps, etc., can be employed. In other instances, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0037] Example 1:

[0038] See Figure 1 As shown, a three-dimensional modeling and drag reduction optimization method for the resistance distribution of a secondary circulation system in a heat exchange station is presented, and the process is as follows:

[0039] Step 1: Design and build the secondary circulation system of the heat exchange station that requires resistance analysis, including core components such as pipes, valves, and heat exchangers, and clarify the system topology and key component parameters (such as pipe routing, pipe diameter change nodes, etc.).

[0040] Step Two, Refer to Figure 2As shown, operational experiments were conducted on the secondary circulation system of the heat exchange station under multiple operating conditions, and the system resistance performance was tested under each operating condition to determine the most unfavorable operating condition. Experiments were carried out under multiple preset operating conditions (such as the winter peak period, the transitional season period, etc.) and operating temperatures (such as 50℃, 60℃, 70℃) to test the system resistance performance (by collecting flow-pressure data through flow meters and pressure sensors).

[0041] Based on the flow-pressure curves obtained from experiments for each operating condition, the remaining flow capacity of the system is calculated (remaining flow capacity = actual flow capacity / design flow capacity × 100%); the operating condition corresponding to the lowest remaining flow capacity (including operating cycle and temperature) is selected as the most unfavorable operating condition.

[0042] Step 3: Obtain the basic parameters of the secondary circulation system of the heat exchange station under the most unfavorable operating conditions; the basic parameters include...

[0043] Pipe parameters: roughness, pipe diameter, material;

[0044] Fluid parameters: density, dynamic viscosity.

[0045] The definition of the most unfavorable operating condition includes the most unfavorable operating cycle (such as the peak period of winter heating) and the most unfavorable operating temperature (such as the lowest water supply temperature).

[0046] Step 4: Establish a three-dimensional finite element model of the secondary circulation system of the heat exchange station based on the basic parameters under the most unfavorable operating conditions, and determine the boundary conditions and critical resistance values ​​of the finite element model; the boundary conditions are a constant flow input at one end of the finite element model and a constant pressure output at the other end; the set critical resistance values ​​are: ;in, This is the final set critical resistance value; The critical value of the average resistance during the test (obtained through statistical analysis of multi-condition experiments); This is the initially set critical resistance value; To adjust the coefficient, The results are dynamically corrected based on the experimental deviations.

[0047] Step 5: Under the most unfavorable operating conditions, perform finite element simulation calculations on the finite element calculation model based on boundary conditions and critical resistance values ​​to obtain a resistance distribution cloud map. This involves acquiring multiple pre-defined fluid flow directions; each fluid flow direction corresponds to a finite element calculation model of the heat exchange station's secondary circulation system under a specific flow state. Under the most unfavorable operating conditions, perform finite element simulation calculations on the finite element calculation model based on boundary conditions and critical resistance values ​​to obtain the resistance distribution cloud map corresponding to each fluid flow direction. The resistance in the resistance distribution cloud map is obtained by a linear combination of multiple resistance components, used to represent the resistance distribution of the heat exchange station's secondary circulation system; the resistance includes frictional resistance (pipeline friction) and local resistance (valves, elbows).

[0048] ;

[0049] in, For the j-th resistance component (e.g. For the friction resistance along the straight pipe, (This refers to the local resistance of a 90° bend).

[0050] Step Six, Refer to Figure 3 As shown, the allowable resistance value under the most unfavorable operating condition is calculated based on the resistance distribution cloud map and then embedded into the finite element calculation model for verification: the maximum value in the resistance distribution cloud map corresponding to each fluid flow direction is extracted; the minimum value among the maximum values ​​is taken as the allowable resistance value.

[0051] The allowable resistance value under the most unfavorable operating condition is calculated based on the resistance distribution cloud map, including:

[0052] Based on the resistance distribution cloud map, multiple first resistance components of the initial high resistance point of the secondary circulation system of the heat exchange station are determined and extracted.

[0053] By unifying the dimensions of multiple first resistance components, multiple second resistance components are obtained.

[0054] Randomly select any number of second resistance components for linear combination, and filter the linear combinations that meet the following preset conditions to obtain the first linear combination;

[0055] Physical rationale: The combined result is non-negative (resistance cannot be negative);

[0056] Error tolerance: Deviation from the experimentally measured resistance <5%;

[0057] The first linear combination that satisfies the preset resistance calculation formula is taken as the allowable resistance value. The resistance calculation formula is as follows: ;in, This is the allowable resistance value; For safety factor (taken as 1.2~1.5, considering system aging margin); The drag coefficient is calculated based on the fluid Reynolds number Re; for laminar flow, γ = 64 / Re, and for turbulent flow, γ = 0.316 / Re. 0 •² 5 ); The coefficient of the j-th resistance component is determined by linear regression. For the j-th resistance component (value after dimensionless measurement); The expansion form of the j-th resistance component (e.g.) ;in This is the friction coefficient; For the position of manager; Pipe diameter; For fluid density; (Flow rate).

[0058] Determining the allowable resistance value: Extract the maximum value from the resistance cloud diagrams in each flow direction, and take the minimum value as the final allowable resistance value (to ensure that the system meets safety requirements under all flow conditions).

[0059] Step 7: Establish the local detailed model and the overall model corresponding to the secondary circulation system of the heat exchange station;

[0060] Overall model: Reflects the macroscopic structure of the system (such as main pipelines and large equipment); Local refined model: Refines the mesh in high-resistance areas (such as valves and tees) with millimeter-level accuracy.

[0061] Virtual fluid element setup: Create a layer of virtual fluid elements along the boundary of the local refined model. The size of the virtual fluid element is 1.5 to 2 times the size of the local model boundary. The fluid resistance (e.g., 1 Pa) is 2 to 3 orders of magnitude smaller than that of the overall model (e.g., 1000 Pa) (the purpose is to reduce boundary reflection effects and improve coupling accuracy).

[0062] Displacement mapping relationship: The displacement transfer between nodes of the local model and the global model is realized through shape functions, that is:

[0063] ;in, It is a shape function;

[0064] This is to account for the displacement of local model nodes, ensuring that the deformation of both is coordinated.

[0065] Step 8: Perform simulation calculations on the overall model and the locally refined model under the test conditions to obtain the maximum resistance value under the test conditions. ;

[0066] Step 9: Based on the maximum resistance value and the verified allowable resistance value, perform a resistance reduction optimization analysis on the secondary circulation system of the heat exchange station: If the maximum resistance value... Less than the verified allowable resistance value If the heat exchange station's secondary circulation system can operate normally under the most unfavorable operating conditions, no optimization is needed; if the maximum resistance value Greater than or equal to the verified allowable resistance value If the heat exchange station's secondary circulation system is operating under the most unfavorable conditions, it needs to be optimized to reduce resistance, including replacing valves with low-resistance ones, increasing the diameter of local pipes, and optimizing the pipe routing to reduce the number of bends.

[0067] The most unfavorable operating conditions include the most unfavorable operating cycle and the most unfavorable operating temperature. Multiple operating cycle tests were conducted on the secondary circulation system of the heat exchange station under various operating conditions, and resistance performance tests were performed on the system (resistance section) under each operating cycle to determine the most unfavorable operating conditions. This included obtaining multiple preset experimental conditions; each preset experimental condition included a preset operating cycle and a preset operating temperature.

[0068] The secondary circulation system of the heat exchange station was tested under various preset experimental conditions to obtain the flow-pressure curves corresponding to each preset experimental condition.

[0069] The remaining flow capacity of the secondary circulation system of the heat exchange station under each preset experimental condition is determined based on the flow-pressure curves under each preset experimental condition; the preset experimental condition corresponding to the lowest remaining flow capacity is taken as the most unfavorable operating condition.

[0070] The beneficial effects of this embodiment are as follows: the method accurately locates the most unfavorable operating state through multi-condition experiments, and combines three-dimensional finite element modeling with a linear combination algorithm of resistance components to achieve visualized quantitative analysis of resistance distribution, with high resistance point identification accuracy reaching the millimeter level; the overall-local model coupling technology combined with virtual fluid units reduces simulation errors; the allowable resistance calculation with safety factor and Reynolds number correction ensures the system's aging margin and flow state adaptability; the automated optimization decision-making mechanism makes the resistance reduction measures more targeted, ultimately achieving improved energy efficiency of the heating system and reduced operation and maintenance costs.

[0071] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0072] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0073] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A three-dimensional modeling and resistance reduction optimization method for heat exchange station two-network circulation system, characterized in that, The method, the process is as follows: Step one: design and build the heat exchange station two network circulation system that needs to carry out resistance analysis, the system contains pipeline, valve, heat exchanger core components, the topological structure of the system and the key component parameters are clear; Step two: the heat exchange station two network circulation system is carried out under m operating condition cycles, the resistance performance test is carried out to each operating condition cycle, and the most unfavorable operating state condition is determined, including: obtaining n preset experimental conditions, each of the preset experimental conditions includes a preset operating period and a preset operating temperature; the heat exchange station two network circulation system is operated under each preset experimental condition, and the flow-pressure curve corresponding to each preset experimental condition is obtained; the residual flow capacity of the system under each preset experimental condition is calculated according to the flow-pressure curve of each preset experimental condition, and the residual flow capacity is the percentage of actual flow capacity and design flow capacity; the preset experimental condition corresponding to the lowest residual flow capacity is determined as the most unfavorable operating state condition; Step three: obtain the basic parameters in the heat exchange station two network circulation system under the most unfavorable operating state condition, the basic parameters include pipeline parameters and fluid parameters; Step four: a three-dimensional finite element calculation model of the heat exchange station two network circulation system is established according to the basic parameters under the most unfavorable operating state condition, and the boundary conditions and resistance critical value of the finite element calculation model are determined; Step five: under the most unfavorable operating state condition, the finite element calculation model is calculated according to the boundary conditions and resistance critical value, and the resistance distribution cloud picture is obtained; Step six: the allowable resistance value under the most unfavorable operating state condition is calculated according to the resistance distribution cloud picture, and the allowable resistance value is embedded in the finite element calculation model for verification; Step seven: the local refined model and the overall model corresponding to the heat exchange station two network circulation system are established; Step eight: the overall model and the local refined model are simulated and calculated under the to-be-detected condition, and the maximum resistance value under the to-be-detected condition is obtained; Step nine: according to the maximum resistance value and the verified allowable resistance value, the heat exchange station two network circulation system is subjected to resistance reduction optimization analysis.

2. The heat exchange station two-network circulation system resistance distribution three-dimensional modeling and resistance reduction optimization method according to claim 1, characterized in that, In step three, the pipeline parameters include the roughness, diameter and material of the pipeline; the fluid parameters include the density and dynamic viscosity of the fluid; the most unfavorable operating state condition includes the most unfavorable operating period and the most unfavorable operating temperature.

3. The heat exchange station two-net circulation system resistance distribution three-dimensional modeling and resistance reduction optimization method according to claim 1, characterized in that, In step four, the boundary condition is to set constant flow input at one end of the finite element calculation model and constant pressure output at the other end; the resistance critical value is calculated by the average resistance critical value, the initially set resistance critical value and the adjustment coefficient, the value range of the adjustment coefficient is 0 to 1, and the adjustment coefficient is dynamically corrected according to the test deviation.

4. The heat exchange station two-net circulation system resistance distribution three-dimensional modeling and resistance reduction optimization method according to claim 1, characterized in that, The step five includes: obtaining a plurality of fluid flow directions, each of which corresponds to a finite element calculation model under a flow state; simulating the finite element calculation model corresponding to each fluid flow direction according to boundary conditions and a resistance critical value under the most unfavorable operating state condition to obtain a resistance distribution cloud atlas corresponding to each direction; the resistance in the resistance distribution cloud atlas is obtained by linear combination of a plurality of resistance components, the resistance components include a frictional resistance generated by a pipeline and a local resistance generated by a valve and a bend.

5. The heat exchange station two-net circulation system resistance distribution three-dimensional modeling and resistance reduction optimization method according to claim 1, characterized in that, The step six includes: determining and extracting a plurality of first resistance components of the initial high resistance point of the system according to the resistance distribution cloud atlas; performing dimension-unified processing on the plurality of first resistance components to obtain a plurality of second resistance components; randomly selecting any number of second resistance components to perform linear combination, and screening a first linear combination that meets a preset condition to obtain a first linear combination, the preset condition includes that the combination result is non-negative and the deviation from the experimental measured resistance is less than a set threshold; taking the first linear combination that meets a preset resistance calculation formula as the allowable resistance value, the resistance calculation formula includes a safety factor, a resistance coefficient, a coefficient of each resistance component, a unified dimension resistance component, and an expansion form of each resistance component, wherein the resistance coefficient is corrected based on the Reynolds number of the fluid, and the safety factor is in a range of 1.2 to 1.

5.

6. The heat exchange station two-net circulation system resistance distribution three-dimensional modeling and resistance reduction optimization method according to claim 5, characterized in that, The step six includes: extracting the maximum value in the resistance distribution cloud atlas corresponding to each fluid flow direction; taking the minimum value in the maximum value as the allowable resistance value and embedding the allowable resistance value into the finite element calculation model to complete the verification.

7. The heat exchange station two-net circulation system resistance distribution three-dimensional modeling and resistance reduction optimization method according to claim 1, characterized in that, The step seven includes: the overall model reflects the macro structure of the system, and the local refined model performs grid encryption on the high resistance area with a millimeter-level encryption accuracy; a layer of virtual fluid units is established along the boundary of the local refined model, the size of the virtual fluid units is 1.5 to 2 times the size of the local model boundary, and the fluid resistance of the virtual fluid units is 2 to 3 orders of magnitude smaller than that of the overall model; the nodes of the local refined model and the nodes of the overall model have displacement mapping relationship through shape functions to ensure deformation coordination.

8. The heat exchange station two-net circulation system resistance distribution three-dimensional modeling and resistance reduction optimization method according to claim 1, characterized in that, The step nine includes: if the maximum resistance value is less than the allowable resistance value verified, it is determined that the system can normally operate under the most unfavorable operating state condition and optimization is not needed; if the maximum resistance value is greater than or equal to the allowable resistance value verified, it is determined that the system needs to be optimized, and the resistance reduction optimization mode includes replacing low-resistance valves, increasing the diameter of local pipelines, and optimizing the pipeline layout to reduce the number of bends.

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