Analogue simulation method for circulating cooling water system

Abstract

The invention provides an analogue simulation method for a circulating cooling water system, and belongs to the technical field of system simulation, and the method comprises the steps: obtaining a cooling demand of the circulating cooling water system, determining a plurality of simulation purposes according to the cooling demand, and determining a simulation range corresponding to each simulation purpose; performing scale division on the simulation range and the simulation purpose, and constructing a corresponding scale model; obtaining a scale simulation result corresponding to each scale model, carrying out coincidence analysis to obtain a coincidence area and a non-coincidence area, and constructing an overall simulation model; according to the method, the output result of the whole simulation model is analyzed, key performance indexes are identified, the key performance indexes are visualized, the performance of the circulating cooling water system under different simulation eyes is evaluated based on the visualized result, the potential optimization direction is identified, simulation analysis is more systematic and accurate, the evaluation accuracy of the system performance is remarkably improved, and the system performance evaluation efficiency is improved. The goals of saving energy, reducing consumption and improving equipment safety are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of system simulation, and in particular to a simulation method for a circulating cooling water system. Background Art

[0002] Circulating cooling water systems are widely used in industries, power generation, and construction to ensure the normal operation and safety of equipment. At present, the design and optimization of cooling water systems mainly rely on empirical rules and simple calculation models, and often lack systematic simulation analysis. Existing simulation tools may not fully consider a variety of operating conditions and environmental factors, resulting in unsatisfactory optimization results.

[0003] Therefore, the present invention provides a simulation method for a circulating cooling water system. Summary of the invention

[0004] The present invention provides a simulation method for a circulating cooling water system, which systematically analyzes cooling requirements, clarifies the purpose and scope of simulation, performs scale division to construct models of different scales, and determines the overlapping areas and non-overlapping areas of different models through overlap analysis, thereby constructing an overall simulation model, analyzing model outputs, identifying and visualizing key performance indicators, and evaluating system performance under different simulation purposes, making the simulation analysis more systematic and precise, significantly improving the accuracy of system performance evaluation, and achieving the goals of energy saving, consumption reduction, and improving equipment safety.

[0005] The present invention provides a simulation method for a circulating cooling water system, comprising:

[0006] Step 1: Obtain the cooling demand of the circulating cooling water system, determine multiple simulation purposes according to the cooling demand, and determine the simulation scope corresponding to each simulation purpose;

[0007] Step 2: Divide the simulation scope and simulation purpose into scales, and build the corresponding scale model according to the scale division results;

[0008] Step 3: Obtain the scale simulation results corresponding to each scale model, perform overlap analysis on the scale simulation results, and then obtain the overlap area and the non-overlap area, and construct the overall simulation model based on the overlap area and the non-overlap area;

[0009] Step 4: Analyze the output results of the overall simulation model, identify key performance indicators, and visualize the key performance indicators. Based on the visualization results, evaluate the performance of the circulating cooling water system under different simulation conditions and identify potential optimization directions.

[0010] The present invention provides a simulation method for a circulating cooling water system, which obtains the cooling demand of the circulating cooling water system, determines multiple simulation purposes according to the cooling demand, and determines the simulation range corresponding to each simulation purpose, including:

[0011] Obtaining a cooling demand and a target temperature of the circulating cooling system, determining a first detection parameter according to the cooling demand and the target temperature, and determining a second detection parameter according to a design document of the circulating cooling water system;

[0012] Performing a correlation analysis on the first detection parameter and the second detection parameter, screening out relevant parameters, and collecting relevant data of the circulating cooling water system based on the relevant parameters;

[0013] According to the relevant data, optimization targets are listed, specific simulation objectives are formulated for each optimization target, and the simulation scope of each simulation objective is determined in combination with relevant parameters and simulation objectives.

[0014] The present invention provides a simulation method for a circulating cooling water system, which performs scale division on a simulation scope and a simulation purpose, and constructs a corresponding scale model according to the scale division result, including:

[0015] According to the spatial scale, the simulation purpose is divided into microscopic purpose, mesoscopic purpose and macroscopic purpose, and the simulation scope of the corresponding simulation purpose is refined according to the microscopic purpose, mesoscopic purpose and macroscopic purpose to obtain the microscopic scope, mesoscopic scope and macroscopic scope;

[0016] Construct a micro-scale model based on microscopic purposes and microscopic scope, construct a meso-scale model based on mesoscopic purposes and mesoscopic scope, and construct a macro-scale model based on macroscopic purposes and macroscopic scope.

[0017] The present invention provides a simulation method for a circulating cooling water system, which obtains scale simulation results corresponding to each scale model, performs overlap analysis on the scale simulation results, and then obtains overlap areas and non-overlap areas, and constructs an overall simulation model based on the overlap areas and the non-overlap areas, including:

[0018] Simulate the microscopic model, the mesoscopic model and the macroscopic model respectively, obtain corresponding scale simulation results, compare multiple scale simulation results, identify output values ​​within a preset similar range, obtain similar output values, and determine the overlapped areas among the microscopic model, the mesoscopic model and the macroscopic model based on the similar output values, and the remaining areas are non-overlapped areas;

[0019] Obtaining information transfer conditions between the microscopic model, the mesoscopic model, and the macroscopic model, and selecting a coupling mode from a condition-characteristic-coupling table based on the information transfer conditions and the system characteristics of the circulating cooling water system;

[0020] Selecting information channels based on the microscopic model, mesoscopic model and macroscopic model of the coupling mode;

[0021] Determine a first boundary condition according to the cooling demand, obtain an operating condition of a circulating cooling water system, determine a second boundary condition according to the operating condition, and obtain an overall boundary condition by combining the first boundary condition and the second boundary condition;

[0022] Based on the coupling mode and information channel, the microscopic model, the mesoscopic model and the macroscopic model are integrated to obtain the integrated result, and the first model is constructed according to the overlapped area and the overall boundary conditions;

[0023] The suitability of the non-overlapping area and the overall boundary conditions is evaluated, and the overall boundary conditions are adjusted according to the suitability evaluation results to obtain the second model. The overall simulation model is obtained by combining the first model and the second model.

[0024] The present invention provides a simulation method for a circulating cooling water system, which evaluates the applicability of non-overlapping areas and overall boundary conditions, and adjusts the overall boundary conditions according to the applicability evaluation results, including:

[0025] ,

[0026] Among them, E m represents the average absolute error between the actual observation value in the non-overlapping area under the mth overall boundary condition and the corresponding scale simulation results of the microscopic model, mesoscopic model and macroscopic model; |A| represents the area of ​​the non-overlapping area; A represents the non-overlapping area; Om(x) represents the actual observation value at position x under the mth overall boundary condition; x represents the length from the inlet of the circulating cooling water system to the observation position; S m (x,B m ) represents the microscopic model, mesoscopic model and macroscopic model at position x based on the overall boundary condition B m Scale simulation results of T1 in Indicates the actual observed temperature of the cooling water in the circulating cooling water system when it flows into the system; T1 out It represents the actual observed temperature of cooling water in the circulating cooling water system when it flows out of the system; L represents the length of the pipe through which the cooling water passes in the circulating cooling water system; ρ represents the density of the cooling water; V1 in Indicates the actual observed flow rate of cooling water flowing into the system; P1 in Indicates the actual observed pressure when cooling water flows into the system; P1 out Indicates the actual observed pressure when cooling water flows out of the system; B m represents the mth overall boundary condition, the inlet and outlet conditions of the circulating cooling water system; m represents the mth overall boundary condition; T2 inIndicates the simulated temperature of the cooling water in the circulating cooling water system when it flows into the system; T2 out Indicates the simulated temperature of cooling water in the circulating cooling water system when it flows out of the system; V2 in P2 represents the simulated flow rate of cooling water flowing into the system; in Indicates the simulated pressure when cooling water flows into the system; P2 out Indicates the simulated pressure when cooling water flows out of the system;

[0027] The suitability of the non-overlapping area and the overall boundary is evaluated based on the mean absolute error, an adjustment direction of the overall boundary condition is determined according to the suitability evaluation result, and the overall boundary condition is adjusted based on the adjustment direction.

[0028] The present invention provides a simulation method for a circulating cooling water system, wherein the non-overlapping area is an area where a microscopic model, a mesoscopic model and a macroscopic model do not overlap.

[0029] The present invention provides a simulation method for a circulating cooling water system, which analyzes the output results of the overall simulation model, identifies key performance indicators, and visualizes the key performance indicators. Based on the visualization results, the performance of the circulating cooling water system under different simulation purposes is evaluated, and potential optimization directions are identified, including:

[0030] Extract output data from the output results of the overall simulation model, determine key performance indicators in the output data based on the simulation purpose, perform statistical analysis on the key performance indicators, organize the statistical analysis results in time, and visualize them;

[0031] A time window is set according to the statistical analysis results, and the visualization results are observed within the set time window to determine the change trend of each key performance indicator over time, and then the key time point and the corresponding formation conditions are found according to the change trend, and the cause of the change is determined according to the key time point and the formation conditions;

[0032] Based on the reasons for the changes, the performance of the circulating cooling water system under different simulation purposes is evaluated. Based on the evaluation results, potential optimization directions are identified to optimize the circulating cooling water system.

[0033] The present invention provides a simulation method for a circulating cooling water system, which identifies potential optimization directions based on evaluation results to optimize the circulating cooling water system, including:

[0034] Identify potential optimization directions based on the evaluation results and reasons for changes, formulate optimization goals based on the potential optimization directions, determine corresponding optimization methods based on the optimization goal-method table, and formulate specific measures based on the optimization method to optimize the circulating cooling water system.

[0035] Compared with the prior art, the beneficial effects of the present application are as follows: by systematically analyzing the cooling demand, clarifying the simulation purpose and scope, and performing scale division to construct models of different scales, the overlapping areas and non-overlapping areas of different models are determined through overlap analysis, thereby constructing an overall simulation model, analyzing the model output, identifying and visualizing key performance indicators, and evaluating the system performance under different simulation purposes, making the simulation analysis more systematic and precise, significantly improving the accuracy of system performance evaluation, and achieving the goals of energy saving, consumption reduction, and improving equipment safety.

[0036] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0037] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 It is a flow chart of a simulation method for a circulating cooling water system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0041] Embodiment 1:

[0042] The embodiment of the present invention provides a simulation method for a circulating cooling water system. Figure 1 As shown, including:

[0043] Step 1: Obtain the cooling demand of the circulating cooling water system, determine multiple simulation purposes according to the cooling demand, and determine the simulation scope corresponding to each simulation purpose;

[0044] Step 2: Divide the simulation scope and simulation purpose into scales, and build the corresponding scale model according to the scale division results;

[0045] Step 3: Obtain the scale simulation results corresponding to each scale model, perform overlap analysis on the scale simulation results, and then obtain the overlap area and the non-overlap area, and construct the overall simulation model based on the overlap area and the non-overlap area;

[0046] Step 4: Analyze the output results of the overall simulation model, identify key performance indicators, and visualize the key performance indicators. Based on the visualization results, evaluate the performance of the circulating cooling water system under different simulation conditions and identify potential optimization directions.

[0047] In this embodiment, cooling demand refers to the cooling capacity required by the system under specific operating conditions, usually expressed in flow rate (m 3 / h) and temperature change (°C). For example, in a power plant, the cooling demand may be 1000m per hour. 3 The water inlet temperature is 30℃ and the outlet temperature does not exceed 40℃.

[0048] In this embodiment, the simulation purpose is a specific analysis goal formulated for each optimization goal, such as evaluating the impact of different flow settings on the cooling effect, or analyzing the system performance under different environmental conditions; the process of formulating the simulation purpose includes determining the optimization goal, analyzing relevant parameters and data, and clarifying the problem to be solved or the situation to be evaluated, such as determining the goal: reducing energy consumption, then analyzing the parameters: identifying the impact of flow and temperature, and then clarifying the problem: how to reduce the flow while maintaining the cooling effect.

[0049] In this embodiment, the simulation range refers to the range of conditions and parameters considered in the simulation process, which is determined based on historical data, design documents and the results of correlation analysis. For example, the simulation range may include a flow rate of 400-600m 3 / h, and the inlet water temperature is between 20-35℃.

[0050] In this embodiment, the scale division is the value space scale division, and the spatial scale refers to the physical scale considered in the simulation analysis, which is usually divided into three levels: microscopic, mesoscopic and macroscopic. The scale division results are microscopic purpose, mesoscopic purpose and macroscopic purpose, and the scale models are microscopic model, mesoscopic model and macroscopic model.

[0051] In this embodiment, the overlap analysis refers to obtaining and comparing the scale simulation results of each model through simulation analysis of the microscopic, mesoscopic and macroscopic models, and identifying the overlap area and the non-overlap area.

[0052] In this embodiment, the overlapped area is an area where the scale simulation results of different models are similar, indicating the consistency between the models. For example, within the flow range of 100-200 L / min, the cooling effects of all models meet the expected standards.

[0053] In this embodiment, the non-overlapping area is the area where the model scale simulation results are inconsistent, indicating the difference between the models. For example, when the flow range is 50-100L / min, the thermal efficiency of the microscopic model is 70%, the mesoscopic model is 65%, and the macroscopic model is 60%.

[0054] In this embodiment, the overall simulation model is a complete simulation model formed by integrating the first model and the second model, for example, a comprehensive simulation tool that can evaluate the performance of the entire circulating cooling water system under different operating conditions.

[0055] In this embodiment, the key performance indicators are indicators that have the greatest impact on system performance determined based on simulation purposes, such as cooling efficiency, temperature reduction of cooling water, energy consumption, energy consumption per unit time, flow stability, and flow fluctuation range.

[0056] In this embodiment, potential optimization directions are areas for improvement identified based on the evaluation results and reasons for changes, such as flow optimization, increasing the flow of circulating cooling water to improve cooling efficiency, energy consumption reduction, reducing energy consumption during system operation by optimizing the configuration of pumps and valves, and temperature control, improving temperature control strategies to keep cooling water within the optimal operating temperature range.

[0057] In this embodiment, performance refers to the key indicators and characteristics exhibited by the circulating cooling water system during operation, which are used to evaluate the efficiency, stability and overall effect of the system, including stability, response time, reliability, heat exchange efficiency, etc.

[0058] In this embodiment, the visualization result refers to the performance data and simulation results of the circulating cooling water system presented by charts, graphs or other visual means.

[0059] The working principle and beneficial effects of the above technical solution are: through systematic analysis of cooling requirements, clarifying the purpose and scope of simulation, performing scale division to construct models of different scales, and through overlap analysis, determining the overlapping areas and non-overlapping areas of different models, thereby constructing an overall simulation model, analyzing model outputs, identifying and visualizing key performance indicators, and evaluating system performance under different simulation purposes, making simulation analysis more systematic and precise, significantly improving the accuracy of system performance evaluation, and achieving the goals of energy saving, consumption reduction and improving equipment safety.

[0060] Embodiment 2:

[0061] The embodiment of the present invention provides a simulation method for a circulating cooling water system, which obtains the cooling demand of the circulating cooling water system, determines multiple simulation purposes according to the cooling demand, and determines the simulation range corresponding to each simulation purpose, including:

[0062] Obtaining a cooling demand and a target temperature of the circulating cooling system, determining a first detection parameter according to the cooling demand and the target temperature, and determining a second detection parameter according to a design document of the circulating cooling water system;

[0063] Performing a correlation analysis on the first detection parameter and the second detection parameter, screening out relevant parameters, and collecting relevant data of the circulating cooling water system based on the relevant parameters;

[0064] According to the relevant data, optimization targets are listed, specific simulation objectives are formulated for each optimization target, and the simulation scope of each simulation objective is determined in combination with relevant parameters and simulation objectives.

[0065] In this embodiment, the target temperature is the ideal operating temperature required by the equipment or process, and the cooling system needs to maintain this temperature by adjusting the cooling water. For example, the target temperature of the reactor of a chemical plant is 60°C, and the cooling water needs to be kept below this temperature to ensure safe operation.

[0066] In this embodiment, the first detection parameter is a key indicator that directly affects the cooling effect, usually flow rate, water inlet temperature, water outlet temperature, etc. For example, the cooling water flow rate (e.g. 500m 3 / h) and inlet water temperature (for example, 25°C) can be used as the first detection parameters.

[0067] In this embodiment, the design documents include system design drawings, equipment specifications, operation manuals, etc., providing detailed information on system performance and operating conditions, such as design drawings and equipment technical specifications of a circulating cooling water system.

[0068] In this embodiment, the second detection parameter is other key indicators extracted from the design document, typically including pressure, pump power, cooling tower efficiency, etc. For example, the efficiency of the cooling tower (e.g., 85%) and the power of the pump (e.g., 100kW) are used as the second detection parameter.

[0069] In this embodiment, correlation analysis is a statistical method used to determine the relationship between the first detection parameter and the second detection parameter, and to identify the main factors affecting the cooling effect. For example, the relationship between the cooling water flow rate and the outlet water temperature is analyzed to determine whether an increase in flow rate can effectively reduce the outlet water temperature.

[0070] In this embodiment, the relevant parameters are parameters that have a significant impact on the performance of the cooling system and are screened out through correlation analysis. For example, flow rate, water inlet temperature and water outlet temperature may be identified as relevant parameters; the relevant data are actual operating data corresponding to the relevant parameters, such as historical operation records, sensor data, etc., for example, cooling water flow records and water outlet temperature data in the past year.

[0071] In this embodiment, the optimization target is the specific aspect that is desired to be improved, such as reducing energy consumption, improving cooling efficiency, extending equipment life, etc. For example, reducing energy consumption by 10% or improving cooling efficiency by 5% is used as an optimization target.

[0072] The working principle and beneficial effects of the above technical solution are: by obtaining the cooling demand and the target temperature, determining the first detection parameter, and determining the second detection parameter in combination with the design document, performing a correlation analysis on the two parameters, screening out relevant parameters and collecting relevant data, and based on these data, listing the optimization goals, and formulating specific simulation objectives for each goal, determining the simulation scope of each simulation objective to guide subsequent system optimization, achieve more efficient performance improvement and resource conservation, and ensure the scientificity and accuracy of the simulation analysis.

[0073] Embodiment 3:

[0074] The embodiment of the present invention provides a simulation method for a circulating cooling water system, which performs scale division on a simulation scope and a simulation purpose, and constructs a corresponding scale model according to the scale division result, including:

[0075] According to the spatial scale, the simulation purpose is divided into microscopic purpose, mesoscopic purpose and macroscopic purpose, and the simulation scope of the corresponding simulation purpose is refined according to the microscopic purpose, mesoscopic purpose and macroscopic purpose to obtain the microscopic scope, mesoscopic scope and macroscopic scope;

[0076] Construct a micro-scale model based on microscopic purposes and microscopic scope, construct a meso-scale model based on mesoscopic purposes and mesoscopic scope, and construct a macro-scale model based on macroscopic purposes and macroscopic scope.

[0077] In this embodiment, the microscopic purpose focuses on the microscopic behavior of the fluid and the heat transfer mechanism, usually involving analysis at the level of individual fluid particles or molecules, for example, studying the turbulence characteristics and heat exchange efficiency in cooling water flow.

[0078] In this embodiment, the mesoscopic purpose focuses on the interaction between equipment and system components and analyzes the performance of local areas, for example, simulating the gas-liquid interaction inside a cooling tower and its impact on the overall cooling efficiency.

[0079] In this embodiment, the macro objective focuses on the performance of the entire cooling system and evaluates the overall performance of the system under different operating conditions, for example, optimizing the design of the entire circulating cooling water system to achieve the best energy efficiency and cooling effect.

[0080] In this embodiment, the microscopic range refers to the scale range of fluid particles or molecules considered for microscopic purposes, for example, the microscopic scale of fluid flow is from a few micrometers to a few millimeters, and the temperature change is within 0.1°C.

[0081] In this embodiment, the mesoscopic range refers to the scale range of equipment and areas considered for mesoscopic purposes, such as the size range of cooling towers and pumps, which is usually a few centimeters to a few meters, and the temperature variation is within 1°C.

[0082] In this embodiment, the macroscopic range refers to the scale range of the entire system considered for macroscopic purposes, for example, the scale of the entire cooling water system is usually several meters to tens of meters, and the temperature variation may reach 10°C or higher.

[0083] In this embodiment, the refinement process is to clarify the micro, meso and macro objectives, identify the key parameters associated with each objective, and determine the corresponding scale range based on historical data and design requirements.

[0084] In this embodiment, the microscopic model is used to simulate the behavior of fluid particles and the heat transfer process, usually using computational fluid dynamics methods, for example, using CFD software to simulate the flow and heat exchange of cooling water in a pipe.

[0085] In this embodiment, the mesoscopic model is used to analyze the interaction between devices, which usually involves the simulation of local flow fields and temperature fields, for example, simulating the gas-liquid flow in a cooling tower and its impact on the cooling effect.

[0086] In this embodiment, the macro model is used to evaluate the performance of the entire cooling system, usually based on the overall design parameters of the system, for example, using system modeling software to analyze the energy efficiency and operational stability of the entire cooling water system.

[0087] The working principle and beneficial effects of the above technical solution are: by establishing simulation models at the micro, meso and macro scales, the performance of the circulating cooling water system is systematically analyzed. The micro model focuses on fluid dynamics and heat transfer, the meso model evaluates the interaction between equipment, and the macro model optimizes the overall system design. Through multi-level simulation, the design accuracy is improved, the energy saving and safety goals are achieved, and the adaptability and reliability of the system are improved.

[0088] Embodiment 4:

[0089] The embodiment of the present invention provides a simulation method for a circulating cooling water system, which obtains scale simulation results corresponding to each scale model, performs overlap analysis on the scale simulation results, and then obtains overlap areas and non-overlap areas, and constructs an overall simulation model based on the overlap areas and non-overlap areas, including:

[0090] Simulate the microscopic model, the mesoscopic model and the macroscopic model respectively, obtain corresponding scale simulation results, compare multiple scale simulation results, identify output values ​​within a preset similar range, obtain similar output values, and determine the overlapped areas among the microscopic model, the mesoscopic model and the macroscopic model based on the similar output values, and the remaining areas are non-overlapped areas;

[0091] Obtaining information transfer conditions between the microscopic model, the mesoscopic model, and the macroscopic model, and selecting a coupling mode from a condition-characteristic-coupling table based on the information transfer conditions and the system characteristics of the circulating cooling water system;

[0092] Selecting information channels based on the microscopic model, mesoscopic model and macroscopic model of the coupling mode;

[0093] Determine a first boundary condition according to the cooling demand, obtain an operating condition of a circulating cooling water system, determine a second boundary condition according to the operating condition, and obtain an overall boundary condition by combining the first boundary condition and the second boundary condition;

[0094] Based on the coupling mode and information channel, the microscopic model, the mesoscopic model and the macroscopic model are integrated to obtain the integrated result, and the first model is constructed according to the overlapped area and the overall boundary conditions;

[0095] The suitability of the non-overlapping area and the overall boundary conditions is evaluated, and the overall boundary conditions are adjusted according to the suitability evaluation results to obtain the second model. The overall simulation model is obtained by combining the first model and the second model.

[0096] In this embodiment, similar output values ​​are similar values ​​of scale simulation results of different models within a preset range. For example, the heat exchange efficiency of the microscopic model, the mesoscopic model and the macroscopic model is 85% when the cooling water flow rate is 1 m / s.

[0097] In this embodiment, the information transfer conditions are the conditions and rules for data exchange between models. For example, when the temperature change does not exceed 5° C., the heat flux output by the microscopic model can be used as the input of the mesoscopic model.

[0098] In this embodiment, the system characteristics are key performance indicators and behavioral characteristics of the circulating cooling water system, such as the cooling capacity, flow resistance and heat exchange efficiency of the system.

[0099] In this embodiment, the condition-characteristic-coupling table is a table that lists the relationship between different conditions and system characteristics to help determine the coupling method. For example, the table may list the changing relationship between heat exchange efficiency and flow resistance under high flow rate conditions.

[0100] In this embodiment, the information channel is a specific way and path for transmitting information between models. For example, the output of the microscopic model is directly transmitted to the mesoscopic model through a data interface or file exchange.

[0101] In this embodiment, the first boundary condition is an initial condition determined according to the cooling demand, for example, setting the cooling water inlet temperature to 25°C; the second boundary condition is an additional condition determined according to the operating conditions, for example, setting the cooling water outlet temperature not to exceed 35°C.

[0102] In this embodiment, the operating conditions are the actual working environment and parameters of the system during operation, for example, the cooling water flow rate is 150 L / min and the ambient temperature is 30°C.

[0103] In this embodiment, the overall boundary condition is a complete set of conditions formed by integrating the first and second boundary conditions, for example, the cooling water inlet temperature is 25°C, the outlet temperature does not exceed 35°C, and the flow rate is 150L / min.

[0104] In this embodiment, the integrated result is the output after integrating the microscopic, mesoscopic and macroscopic models. For example, the integrated model shows that at a specific flow rate, the overall cooling efficiency of the system reaches 90%.

[0105] In this embodiment, the applicability assessment is to evaluate the degree of match between the non-overlapping area and the overall boundary conditions. The applicability of the overall boundary conditions is evaluated by calculating the mean absolute error between the actual observations in the non-overlapping area and the simulation results at the micro, meso, and macro model scales. For example, it is evaluated whether the cooling efficiency of the non-overlapping area meets the design requirements under a specific flow rate.

[0106] In this embodiment, the preset similarity range refers to the range in which the output values ​​(such as temperature, flow, pressure, etc.) are considered to be similar or comparable in models of different scales (micro, meso and macro models). For example, in a cooling water system, assuming that the temperature predicted by the micro model is 20°C, the meso model is 19.5°C, and the macro model is 20.5°C, if the preset similarity range is ±1°C, the output values ​​of these three models are all within a similar range.

[0107] In this embodiment, the first model is a preliminary model constructed based on the overlapping area and the overall boundary conditions, for example, a model that simulates the cooling water flow and heat exchange in the overlapping area; the second model is a model adjusted after applicability evaluation, for example, the adjusted model optimizes the cooling effect in the non-overlapping area.

[0108] The working principle and beneficial effects of the above technical solution are: through simulation analysis of microscopic, mesoscopic and macroscopic models, the scale simulation results of each model are obtained and compared, the overlapping areas and non-overlapping areas are identified, and based on the information transmission conditions, the appropriate coupling method and information channel are selected, the overall boundary conditions are determined, and the first and second models are constructed through the integrated model, and finally a comprehensive overall simulation model is formed, thereby improving the accuracy and reliability of the cooling water system design.

[0109] Embodiment 5:

[0110] The embodiment of the present invention provides a simulation method for a circulating cooling water system, which evaluates the suitability of non-overlapping areas and overall boundary conditions, and adjusts the overall boundary conditions according to the suitability evaluation results, including:

[0111] ,

[0112] Among them, E m represents the average absolute error between the actual observation value in the non-overlapping area under the mth overall boundary condition and the corresponding scale simulation results of the microscopic model, mesoscopic model and macroscopic model; |A| represents the area of ​​the non-overlapping area; A represents the non-overlapping area; Om(x) represents the actual observation value at position x under the mth overall boundary condition; x represents the length from the inlet of the circulating cooling water system to the observation position; S m (x,B m ) represents the microscopic model, mesoscopic model and macroscopic model at position x based on the overall boundary condition B m Scale simulation results of T1 in Indicates the actual observed temperature of the cooling water in the circulating cooling water system when it flows into the system; T1 out It represents the actual observed temperature of cooling water in the circulating cooling water system when it flows out of the system; L represents the length of the pipe through which the cooling water passes in the circulating cooling water system; ρ represents the density of the cooling water; V1 in Indicates the actual observed flow rate of cooling water flowing into the system; P1 in Indicates the actual observed pressure when cooling water flows into the system; P1 out Indicates the actual observed pressure when cooling water flows out of the system; B m represents the mth overall boundary condition, the inlet and outlet conditions of the circulating cooling water system; m represents the mth overall boundary condition; T2 in Indicates the simulated temperature of the cooling water in the circulating cooling water system when it flows into the system; T2 out Indicates the simulated temperature of cooling water in the circulating cooling water system when it flows out of the system; V2 in P2 represents the simulated flow rate of cooling water flowing into the system; in Indicates the simulated pressure when cooling water flows into the system; P2 out Indicates the simulated pressure when cooling water flows out of the system;

[0113] The suitability of the non-overlapping area and the overall boundary is evaluated based on the mean absolute error, an adjustment direction of the overall boundary condition is determined according to the suitability evaluation result, and the overall boundary condition is adjusted based on the adjustment direction.

[0114] In this embodiment, the suitability evaluation results generally include: temperature error, the difference between the actual cooling water outlet temperature and the outlet temperature output by the simulation model, for example, the actual outlet temperature is 30°C, while the simulation model predicts 28°C, with an error of 2°C; flow velocity error, the difference between the actual flow velocity and the flow velocity predicted by the simulation model, for example, the actual flow velocity is 1.5m / s, while the simulation predicts 1.2m / s, with an error of 0.3m / s; pressure error, the difference between the actual pressure and the pressure output by the simulation model, for example, the actual inlet pressure is 100kPa, while the simulation model predicts 95kPa, with an error of 5kPa.

[0115] In this embodiment, the adjustment direction includes temperature adjustment. If the output temperature of the simulation model is too low, it may be necessary to increase the input temperature of the cooling water. For example, if the actual cooling water temperature is 25°C and the simulation model predicts 23°C, adjust the input temperature to 27°C; flow rate adjustment. If the simulated flow rate is lower than the actual flow rate, it is necessary to increase the pump speed or pipe diameter. For example, if the actual flow rate is 1.5m / s and the simulation is 1.2m / s, adjust the pump speed to increase the flow rate; pressure adjustment. If the simulated pressure is lower than the actual pressure, it may be necessary to increase the pump pressure output. For example, the actual inlet pressure is 100kPa and the simulation is 95kPa. Adjust the pump setting to increase the pressure to 100kPa; boundary condition redefinition. Re-evaluate and define the input and output boundary conditions of the cooling water. For example, if there is an additional heat source in the actual system, the influence of this heat source needs to be introduced into the model.

[0116] In this embodiment, the specific process of adjusting the overall boundary conditions is to collect actual data of system operation, including temperature, flow rate and pressure, for example, record the actual inlet and outlet temperature, flow rate and pressure of cooling water, calculate the error between the actual observation value and the scale simulation result of the simulation model, for example, use statistical methods to calculate the average absolute error of temperature, flow rate and pressure, and clarify the boundary conditions that need to be adjusted based on the error analysis results. For example, if the temperature error is large, decide to adjust the input temperature, modify the boundary conditions in the simulation model, and update the input parameters. For example, adjust the inlet temperature from 25°C to 27°C in the model, run the updated model, and observe whether the scale simulation results are improved. For example, re-run the model and record the new outlet temperature, and verify the effectiveness of the adjustment by comparing it with the actual observation data. For example, compare the outlet temperature of the new output with the actual outlet temperature to confirm whether the error is reduced.

[0117] The working principle and beneficial effects of the above technical solution are: by calculating the mean absolute error between the actual observation values ​​in the non-overlapping area and the simulation results of the micro, meso and macro model scales, the applicability of the overall boundary conditions is evaluated, and according to the error evaluation results, the adjustment direction is determined, and the overall boundary conditions are optimized to better adapt to the changing operating environment and improve the flexibility of the system.

[0118] Embodiment 6:

[0119] An embodiment of the present invention provides a simulation method for a circulating cooling water system, wherein the non-overlapping area is an area where the microscopic model, the mesoscopic model and the macroscopic model do not overlap.

[0120] The working principle and beneficial effect of the above technical solution are: by identifying non-overlapping areas, the most appropriate model can be selected for a specific physical phenomenon, thereby improving the accuracy and reliability of the simulation.

[0121] Embodiment 7:

[0122] The embodiment of the present invention provides a simulation method for a circulating cooling water system, which analyzes the output results of the overall simulation model, identifies key performance indicators, and visualizes the key performance indicators. Based on the visualization results, the performance of the circulating cooling water system under different simulation purposes is evaluated, and potential optimization directions are identified, including:

[0123] Extract output data from the output results of the overall simulation model, determine key performance indicators in the output data based on the simulation purpose, perform statistical analysis on the key performance indicators, organize the statistical analysis results in time, and visualize them;

[0124] A time window is set according to the statistical analysis results, and the visualization results are observed within the set time window to determine the change trend of each key performance indicator over time, and then the key time point and the corresponding formation conditions are found according to the change trend, and the cause of the change is determined according to the key time point and the formation conditions;

[0125] Based on the reasons for the changes, the performance of the circulating cooling water system under different simulation purposes is evaluated. Based on the evaluation results, potential optimization directions are identified to optimize the circulating cooling water system.

[0126] In this embodiment, the output data is all relevant data extracted from the overall simulation model, including temperature, flow, pressure, energy consumption, etc. For example, the inlet and outlet temperature, flow rate, system pressure and other data of the circulating cooling water recorded during the simulation process.

[0127] In this embodiment, statistical analysis is a numerical analysis of key performance indicators to identify the distribution characteristics, mean value, standard deviation, etc. of the data. For example, the mean value and standard deviation of the cooling efficiency in different time periods are calculated to evaluate the stability of the system.

[0128] In this embodiment, the time window is a set time range for observing and analyzing changes in key performance indicators. For example, a time window of one hour is selected to observe temperature changes in the cooling water system during this time period.

[0129] In this embodiment, the change trend is the change pattern of the key performance indicator over time, which may be rising, falling, or remaining stable. For example, the cooling water temperature gradually increases during high-load operation, indicating that the cooling efficiency decreases.

[0130] In this embodiment, the key time point is a specific moment showing a significant change within the time window. For example, at a certain moment (such as 15:30), the cooling water outlet temperature suddenly increases, which may be caused by equipment failure or load increase.

[0131] In this embodiment, the forming conditions are specific environmental or operating conditions that cause changes in the key time points. For example, at 15:30, it is found that the flow rate of the cooling system decreases and the ambient temperature increases, which may lead to a decrease in the cooling effect.

[0132] In this embodiment, the change reason is the root cause of the change in the key performance indicator. For example, a decrease in flow rate may be due to a pump failure or a pipe blockage, resulting in a decrease in cooling efficiency.

[0133] In this embodiment, the evaluation result is a comprehensive evaluation of the performance of the circulating cooling water system under different simulation purposes, for example, evaluating the cooling efficiency and energy consumption under high load and low load conditions, and determining the performance of the system under different conditions.

[0134] In this embodiment, the optimization process is to implement optimization to improve system performance based on potential improvement measures identified according to the evaluation results. For example, if it is found that flow instability leads to reduced cooling efficiency, it can be considered to replace the pump or clean the pipeline to improve flow stability.

[0135] The working principle and beneficial effects of the above technical solution are: by extracting the output data of the overall simulation model, determining the key performance indicators and performing statistical analysis and visualization, identifying the changing trends of the indicators within the time window, finding the key time points and their formation conditions, and then analyzing the causes of the changes. Based on these causes, the performance of the cooling water system under different simulation purposes is evaluated, and the optimization direction is identified, thereby achieving comprehensive optimization of the system.

[0136] Embodiment 8:

[0137] The embodiment of the present invention provides a simulation method for a circulating cooling water system, which identifies potential optimization directions based on evaluation results to optimize the circulating cooling water system, including:

[0138] Identify potential optimization directions based on the evaluation results and reasons for changes, formulate optimization goals based on the potential optimization directions, determine corresponding optimization methods based on the optimization goal-method table, and formulate specific measures based on the optimization method to optimize the circulating cooling water system.

[0139] In this embodiment, the optimization goal is to clarify the specific direction and expected results of the optimization. For example, to improve cooling efficiency, the goal is to increase cooling efficiency by 10%, to reduce energy consumption, the goal is to reduce energy consumption by 15%, and to improve flow stability, the goal is to control flow fluctuations within 5%.

[0140] In this embodiment, the optimization target-method table is a table that lists different optimization targets and their corresponding optimization methods. For example, if the optimization target is to improve cooling efficiency, the corresponding optimization method is to increase the flow rate of the pump and optimize the pipeline design.

[0141] In this embodiment, the optimization method formulates specific measures according to the specific implementation steps formulated by the optimization method, such as improving cooling efficiency, measures such as increasing the flow setting of the pump, redesigning the pipeline to reduce resistance, reducing energy consumption, measures such as replacing the existing pump with a high-efficiency model, and adjusting the operating mode to reduce energy consumption.

[0142] In this embodiment, the specific process is to collect the operating data of the current circulating cooling water system, evaluate the flow rate, energy consumption and cooling efficiency, identify the causes of unstable flow rate, high energy consumption and low cooling efficiency through data analysis, set specific optimization goals according to the identified problems, use the optimization goal-method table, select the appropriate optimization method, formulate a detailed implementation plan, and implement the optimization measures in stages.

[0143] The working principle and beneficial effects of the above technical solution are: identify the optimization direction through evaluation results, set goals and select corresponding methods, implement specific measures to optimize the system, improve the overall performance of the system, and the optimized system can better respond to environmental changes and ensure the safe operation of the equipment.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A simulation method for a circulating cooling water system, characterized in that: include: Step 1: Obtain the cooling demand of the circulating cooling water system, determine multiple simulation purposes according to the cooling demand, and determine the simulation scope corresponding to each simulation purpose; Step 2: Divide the simulation scope and simulation purpose into scales, and build the corresponding scale model according to the scale division results; Step 3: Obtain the scale simulation results corresponding to each scale model, perform overlap analysis on the scale simulation results, and then obtain the overlap area and the non-overlap area, and construct the overall simulation model based on the overlap area and the non-overlap area; Step 4: Analyze the output results of the overall simulation model, identify key performance indicators, and visualize the key performance indicators. Based on the visualization results, evaluate the performance of the circulating cooling water system under different simulation conditions and identify potential optimization directions.

2. A simulation method for a circulating cooling water system according to claim 1, characterized in that: Obtain the cooling demand of the circulating cooling water system, determine multiple simulation purposes based on the cooling demand, and determine the simulation scope corresponding to each simulation purpose, including: Obtaining a cooling demand and a target temperature of the circulating cooling system, determining a first detection parameter according to the cooling demand and the target temperature, and determining a second detection parameter according to a design document of the circulating cooling water system; Performing a correlation analysis on the first detection parameter and the second detection parameter, screening out relevant parameters, and collecting relevant data of the circulating cooling water system based on the relevant parameters; According to the relevant data, optimization targets are listed, specific simulation objectives are formulated for each optimization target, and the simulation scope of each simulation objective is determined in combination with relevant parameters and simulation objectives.

3. A simulation method for a circulating cooling water system according to claim 1, characterized in that: The simulation scope and simulation purpose are divided into different scales, and the corresponding scale model is constructed according to the scale division results, including: According to the spatial scale, the simulation purpose is divided into microscopic purpose, mesoscopic purpose and macroscopic purpose, and the simulation scope of the corresponding simulation purpose is refined according to the microscopic purpose, mesoscopic purpose and macroscopic purpose to obtain the microscopic scope, mesoscopic scope and macroscopic scope; Construct a micro-scale model based on microscopic purposes and microscopic scope, construct a meso-scale model based on mesoscopic purposes and mesoscopic scope, and construct a macro-scale model based on macroscopic purposes and macroscopic scope.

4. A simulation method for a circulating cooling water system according to claim 3, characterized in that: Obtaining scale simulation results corresponding to each scale model, performing overlap analysis on the scale simulation results, and then obtaining overlap areas and non-overlap areas, and constructing an overall simulation model based on the overlap areas and the non-overlap areas, including: Simulate the microscopic model, the mesoscopic model and the macroscopic model respectively, obtain corresponding scale simulation results, compare multiple scale simulation results, identify output values ​​within a preset similar range, obtain similar output values, and determine the overlapped areas among the microscopic model, the mesoscopic model and the macroscopic model based on the similar output values, and the remaining areas are non-overlapped areas; Obtaining information transfer conditions between the microscopic model, the mesoscopic model, and the macroscopic model, and selecting a coupling mode from a condition-characteristic-coupling table based on the information transfer conditions and the system characteristics of the circulating cooling water system; Selecting information channels based on the microscopic model, mesoscopic model and macroscopic model of the coupling mode; Determine a first boundary condition according to the cooling demand, obtain an operating condition of a circulating cooling water system, determine a second boundary condition according to the operating condition, and obtain an overall boundary condition by combining the first boundary condition and the second boundary condition; Based on the coupling mode and information channel, the microscopic model, the mesoscopic model and the macroscopic model are integrated to obtain the integrated result, and the first model is constructed according to the overlapped area and the overall boundary conditions; The suitability of the non-overlapping area and the overall boundary conditions is evaluated, and the overall boundary conditions are adjusted according to the suitability evaluation results to obtain the second model. The overall simulation model is obtained by combining the first model and the second model.

5. A simulation method for a circulating cooling water system according to claim 4, characterized in that: The non-overlapping area and the overall boundary conditions are evaluated for suitability, and the overall boundary conditions are adjusted according to the results of the applicability evaluation, including: ， Among them, E m represents the average absolute error between the actual observation value in the non-overlapping area under the mth overall boundary condition and the corresponding scale simulation results of the microscopic model, mesoscopic model and macroscopic model; |A| represents the area of ​​the non-overlapping area; A represents the non-overlapping area; Om(x) represents the actual observation value at position x under the mth overall boundary condition; x represents the length from the inlet of the circulating cooling water system to the observation position; S m (x,B m ) represents the microscopic model, mesoscopic model and macroscopic model at position x based on the overall boundary condition B m Scale simulation results of T1 in Indicates the actual observed temperature of the cooling water in the circulating cooling water system when it flows into the system; T1 out It represents the actual observed temperature of cooling water in the circulating cooling water system when it flows out of the system; L represents the length of the pipe through which the cooling water passes in the circulating cooling water system; ρ represents the density of the cooling water; V1 in Indicates the actual observed flow rate of cooling water flowing into the system; P1 in Indicates the actual observed pressure when cooling water flows into the system; P1 out Indicates the actual observed pressure when cooling water flows out of the system; B m represents the mth overall boundary condition, the inlet and outlet conditions of the circulating cooling water system; m represents the mth overall boundary condition; T2 in Indicates the simulated temperature of the cooling water in the circulating cooling water system when it flows into the system; T2 out Indicates the simulated temperature of cooling water in the circulating cooling water system when it flows out of the system; V2 in P2 represents the simulated flow rate of cooling water flowing into the system; in Indicates the simulated pressure when cooling water flows into the system; P2 out Indicates the simulated pressure when cooling water flows out of the system; The suitability of the non-overlapping area and the overall boundary is evaluated based on the mean absolute error, an adjustment direction of the overall boundary condition is determined according to the suitability evaluation result, and the overall boundary condition is adjusted based on the adjustment direction.

6. A simulation method for a circulating cooling water system according to claim 4, characterized in that: The non-overlapping region is the region where the microscopic model, mesoscopic model and macroscopic model do not overlap.

7. The simulation method for a circulating cooling water system according to claim 1, characterized in that: Analyze the output results of the overall simulation model, identify key performance indicators, and visualize the key performance indicators. Based on the visualization results, evaluate the performance of the circulating cooling water system under different simulation purposes and identify potential optimization directions, including: Extract output data from the output results of the overall simulation model, determine key performance indicators in the output data based on the simulation purpose, perform statistical analysis on the key performance indicators, organize the statistical analysis results in time, and visualize them; A time window is set according to the statistical analysis results, and the visualization results are observed within the set time window to determine the change trend of each key performance indicator over time, and then the key time point and the corresponding formation conditions are found according to the change trend, and the cause of the change is determined according to the key time point and the formation conditions; Based on the reasons for the changes, the performance of the circulating cooling water system under different simulation purposes is evaluated. Based on the evaluation results, potential optimization directions are identified to optimize the circulating cooling water system.

8. A simulation method for a circulating cooling water system according to claim 7, characterized in that: Based on the evaluation results, identify potential optimization directions to optimize the circulating cooling water system, including: Identify potential optimization directions based on the evaluation results and reasons for changes, formulate optimization goals based on the potential optimization directions, determine corresponding optimization methods based on the optimization goal-method table, and formulate specific measures based on the optimization method to optimize the circulating cooling water system.

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