Collaborative design and control method, device and equipment of heat exchanger and storage medium

Abstract

The invention relates to the technical field of heat exchangers, in particular to a collaborative design and control method, device and equipment for a heat exchanger and a storage medium, and the method comprises the steps that operation data of a target heat exchanger under at least one working condition are collected, and performance coefficient data meeting preset performance conditions are output through a multi-target collaborative optimization model; after operation is conducted according to the performance coefficient data, the flow value, the pressure value and the temperature value of the target heat exchanger are collected, and a corresponding adjusting instruction is determined; and based on the digital twin body, determining performance attenuation data and / or a fault diagnosis result, further generating an optimization instruction, and controlling the target heat exchanger. Therefore, the problems that in the related technology, under the variable load working condition, when the load deviates from a design point, the heat transfer and efficiency become poor due to fluid flow and flow velocity changes, vibration is caused by uneven flow, the temperature control precision is poor, and physical property changes and temperature pinch points are prone to occurring in a supercritical working medium are solved; the post-adjustment adaptability is poor, the performance is difficult to optimize, the response is slow, and potential safety hazards exist.

Description

Technical Field

[0001] This application relates to the field of heat exchanger technology, and in particular to a collaborative design and control method, apparatus, equipment and storage medium for heat exchangers. Background Technology

[0002] In related technologies, the heat exchanger's structural parameters (such as heat exchange area, flow channel layout, etc.) or operating parameters (such as fluid velocity, temperature, etc.) can be adjusted to maximize its heat exchange efficiency at a pre-set specific rated operating point. Alternatively, a bypass valve can be used to divert the fluid and change the actual flow rate of the fluid participating in heat exchange, or a frequency converter can be used to adjust the speed of the drive equipment (such as pumps and fans) to change the fluid velocity, thereby enabling post-processing adjustment of the heat exchanger's heat exchange effect when deviations occur in its operation.

[0003] However, in related technologies, under variable load conditions, when the system load deviates from the design point, changes in fluid flow rate and velocity can easily lead to a deterioration in heat transfer coefficient and efficiency; uneven flow can cause vibration, noise, and structural failure; and reduce the accuracy of temperature control. Furthermore, when using supercritical working fluids, changes in physical properties and temperature pinch points are also likely to occur. In addition, the post-adjustment scheme does not consider the adaptability to variable loads, making it difficult to achieve optimal overall performance over a wide operating range, resulting in slow control response and potential safety hazards, which urgently need improvement. Summary of the Invention

[0004] This application provides a collaborative design and control method, device, equipment, and storage medium for heat exchangers to solve the problems in related technologies, such as: under variable load conditions, when the load deviates from the design point, changes in fluid flow rate and velocity easily lead to poor heat transfer and efficiency, uneven flow causes vibration, poor temperature control accuracy, and in supercritical working fluids, changes in physical properties and temperature pinch points are also prone to occur; in addition, post-adjustment does not consider the adaptability to variable load, making it difficult to achieve optimal performance, slow response, and posing safety hazards.

[0005] The first aspect of this application provides a collaborative design and control method for a heat exchanger, comprising the following steps: collecting operating data of a target heat exchanger under at least one operating condition, and inputting the operating data into a pre-constructed multi-objective collaborative optimization model to output performance coefficient data of the target heat exchanger satisfying preset performance conditions; controlling the target heat exchanger to operate according to the performance coefficient data, collecting the flow rate, pressure, and temperature values ​​of the target heat exchanger in the current state, and determining the adjustment commands of the bypass valve and / or frequency converter valve in the target heat exchanger based on the flow rate, pressure, and temperature values; determining the performance degradation data and / or fault diagnosis results of the target heat exchanger based on the flow rate, pressure, temperature, adjustment commands, and the digital twin corresponding to the target heat exchanger, generating optimization commands for the target heat exchanger according to the performance degradation data and / or fault diagnosis results, and controlling the target heat exchanger according to the optimization commands.

[0006] Optionally, in one embodiment of this application, before inputting the operating data into a pre-constructed multi-objective collaborative optimization model, the method further includes: calculating the thermogravimetric ratio per unit temperature difference of the target heat exchanger based on the thermogravimetric ratio and heat transfer temperature difference of the target heat exchanger; determining the objective function of the multi-objective collaborative optimization model based on the thermogravimetric ratio per unit temperature difference; determining the constraints of the multi-objective collaborative optimization model based on the heat transfer temperature difference and pressure drop; and constructing the multi-objective collaborative optimization model based on the objective function and the constraints.

[0007] Optionally, in one embodiment of this application, the step of inputting the operating data into a pre-constructed multi-objective collaborative optimization model to output performance coefficient data of the target heat exchanger satisfying preset performance conditions includes: determining the flow rate data in the performance coefficient data based on the process data, flow path data, and flow channel configuration data in the operating data; determining the heat transfer temperature difference data in the performance coefficient data based on the structural dimension data in the operating data; determining the pressure drop data in the performance coefficient data based on the fluid data and heat exchange unit data in the operating data; and determining the thermal stress data in the performance coefficient data based on the thermal fatigue resistance performance data, material data, and fatigue data in the operating data.

[0008] Optionally, in one embodiment of this application, determining the adjustment command for the bypass valve and / or variable frequency valve in the target heat exchanger based on the flow rate, pressure value, and temperature value includes: inputting the flow rate, pressure value, and temperature value into a target model predictive controller to output the load change rate of the target heat exchanger; in response to the load change rate of the target heat exchanger being greater than a preset threshold, determining the adjustment command as a bypass adjustment command for the bypass valve, and determining the opening adjustment value of the bypass valve according to the bypass adjustment command; in response to the load change rate being less than or equal to the preset threshold, determining the adjustment command as a variable frequency adjustment command for the variable frequency valve, and determining the variable speed pump speed value of the variable frequency valve according to the variable frequency adjustment command.

[0009] Optionally, in one embodiment of this application, determining the performance degradation data and / or fault diagnosis results of the target heat exchanger based on the flow rate value, the pressure value, the temperature value, the adjustment command, and the digital twin corresponding to the target heat exchanger includes: determining the initial performance degradation data and / or initial fault diagnosis results of the digital twin based on the flow rate value, the pressure value, the temperature value, and the adjustment command; and obtaining the performance degradation data and / or the fault diagnosis results based on the initial performance degradation data and / or the initial fault diagnosis results.

[0010] Optionally, in one embodiment of this application, determining the initial performance degradation data and / or initial fault diagnosis result of the digital twin based on the flow rate value, the pressure value, the temperature value, and the adjustment command includes: selecting a target machine learning model suitable for the digital twin based on the historical and real-time data of the digital twin; and inputting the flow rate value, the pressure value, the temperature value, and the adjustment command into the target machine learning model to output the initial performance degradation data and / or the initial fault diagnosis result.

[0011] A second aspect of this application provides a collaborative design and control device for a heat exchanger, comprising: an output module, configured to collect operating data of a target heat exchanger under at least one operating condition, and input the operating data into a pre-constructed multi-objective collaborative optimization model to output performance coefficient data of the target heat exchanger satisfying preset performance conditions; a first determining module, configured to control the target heat exchanger to operate according to the performance coefficient data, collect the flow rate, pressure value, and temperature value of the target heat exchanger in the current state, and determine the adjustment command of the bypass valve and / or frequency converter valve in the target heat exchanger based on the flow rate, pressure value, and temperature value; and a control module, configured to determine the performance degradation data and / or fault diagnosis results of the target heat exchanger based on the flow rate, pressure value, temperature value, adjustment command, and digital twin corresponding to the target heat exchanger, to generate optimization commands for the target heat exchanger according to the performance degradation data and / or fault diagnosis results, and control the target heat exchanger according to the optimization commands.

[0012] Optionally, in one embodiment of this application, it further includes: a calculation module, configured to calculate the thermogravimetric ratio per unit temperature difference of the target heat exchanger based on the thermogravimetric ratio and heat transfer temperature difference of the target heat exchanger before inputting the operating data into the pre-constructed multi-objective collaborative optimization model; a second determination module, configured to determine the objective function of the multi-objective collaborative optimization model based on the thermogravimetric ratio per unit temperature difference; a third determination module, configured to determine the constraints of the multi-objective collaborative optimization model based on the heat transfer temperature difference and pressure drop; and a construction module, configured to construct the multi-objective collaborative optimization model based on the objective function and the constraints.

[0013] Optionally, in one embodiment of this application, the output module includes: a first determining unit, configured to determine the flow rate data in the performance coefficient data based on the process data, flow path data, and flow channel configuration data in the operating data; a second determining unit, configured to determine the heat transfer temperature difference data in the performance coefficient data based on the structural dimension data in the operating data; a third determining unit, configured to determine the pressure drop data in the performance coefficient data based on the fluid data and heat exchange unit data in the operating data; and a fourth determining unit, configured to determine the thermal stress data in the performance coefficient data based on the thermal fatigue resistance performance data, material data, and fatigue data in the operating data.

[0014] Optionally, in one embodiment of this application, the first determining module includes: an output unit, configured to input the flow rate, the pressure value, and the temperature value into a target model predictive controller to output the load change rate of the target heat exchanger; a fifth determining unit, configured to determine the adjustment command as a bypass adjustment command of the bypass valve in response to the load change rate of the target heat exchanger being greater than a preset threshold, and to determine the opening adjustment value of the bypass valve according to the bypass adjustment command; and a sixth determining unit, configured to determine the adjustment command as a frequency conversion adjustment command of the frequency converter valve in response to the load change rate being less than or equal to the preset threshold, and to determine the variable speed pump speed value of the frequency converter valve according to the frequency conversion adjustment command.

[0015] Optionally, in one embodiment of this application, the control module includes: a seventh determining unit, configured to determine the initial performance degradation data and / or initial fault diagnosis results of the digital twin based on the flow rate value, the pressure value, the temperature value, and the adjustment command; and a generating unit, configured to obtain the performance degradation data and / or the fault diagnosis results based on the initial performance degradation data and / or the initial fault diagnosis results.

[0016] Optionally, in one embodiment of this application, the generation unit includes: a selection subunit, configured to select a target machine learning model suitable for the digital twin based on the historical and real-time data of the digital twin; and an output subunit, configured to input the flow rate value, the pressure value, the temperature value, and the adjustment command into the target machine learning model to output the initial performance degradation data and / or the initial fault diagnosis result.

[0017] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the co-design and control method for heat exchangers as described in the above embodiments.

[0018] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described co-design and control method for a heat exchanger.

[0019] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, implements the above-described collaborative design and control method for heat exchangers.

[0020] This application embodiment can input the collected operating data of the target heat exchanger under at least one operating condition into a pre-constructed multi-objective collaborative optimization model, and then output performance coefficient data that meets preset performance conditions. After controlling the target heat exchanger to operate according to the performance coefficient data, the corresponding adjustment command is determined based on the flow rate, pressure and temperature values ​​of the target heat exchanger in the current state. Then, combined with the digital twin, the performance degradation data and / or fault diagnosis results of the target heat exchanger are determined to generate optimization commands for the target heat exchanger and control the target heat exchanger. This ensures the dynamic adaptability of the heat exchanger throughout its entire life cycle from its inception to operation, and ensures that the heat exchanger can maintain high heat transfer efficiency throughout the entire range from low load to overload. This greatly improves the reliability and lifespan of the equipment, significantly improves the annual average energy efficiency of the system, and realizes a leap from "passive maintenance" to "proactive operation and maintenance". This solves the problems in related technologies, such as the tendency for fluid flow rate and velocity to change and thus deteriorate heat transfer and efficiency when the load deviates from the design point under variable load conditions, and the vibration caused by uneven flow. In addition, the temperature control accuracy is poor, and the material properties and temperature pinch points are also prone to occur in supercritical working fluids. Furthermore, the post-adjustment does not take into account the adaptability of variable load, making it difficult to achieve optimal performance, resulting in slow response and safety hazards.

[0021] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0022] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a block diagram of a collaborative design and control system for a heat exchanger according to an embodiment of this application; Figure 2 This is a flowchart of a collaborative design and control method for a heat exchanger according to an embodiment of this application; Figure 3 A flowchart of the main steps of the performance design layer according to an embodiment of this application; Figure 4(a) is a schematic diagram of the structure of a forked diamond-shaped microplate fin provided according to an embodiment of the present application; Figure 4(b) is a schematic diagram of the structure of a row of square microfins according to an embodiment of the present application; Figure 4(c) is a schematic diagram of a rectangular prism-shaped turbulence channel structure provided according to an embodiment of this application; Figure 4(d) is a schematic diagram of a variable cross-section adaptive channel structure provided according to an embodiment of this application; Figure 5 This is a schematic diagram of a heat exchange unit flow splitting design concept according to an embodiment of this application; Figure 6 A flowchart of the main components of an online control layer according to an embodiment of this application; Figure 7 This is a flowchart of the main components of an intelligent operation and maintenance layer according to an embodiment of this application; Figure 8 This is a block diagram of a heat exchanger co-design and control device provided according to an embodiment of this application; Figure 9 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0024] Before introducing the collaborative design and control method for heat exchangers proposed in the embodiments of this application, we will first introduce a collaborative design and control system for heat exchangers involved in the embodiments of this application.

[0025] Specifically, Figure 1 This is a block diagram of a collaborative design and control system for a heat exchanger provided according to one embodiment of this application.

[0026] like Figure 1 As shown, the collaborative design and control system of this heat exchanger includes a performance design layer based on all operating conditions, an online control layer based on active adjustment, and a data-driven intelligent operation and maintenance layer.

[0027] Among them, the performance design layer defines the physical structure of the heat exchanger core, and through analysis, dynamic modeling and multi-objective optimization of the expected full operating range, determines the flow channel configuration, flow arrangement and material selection of the heat exchanger, so that the heat exchanger body has inherent high performance and robustness over a wide Reynolds number range.

[0028] Furthermore, in this embodiment, the performance design layer includes flow-adaptive design, a coordinated strategy for heat transfer temperature difference and structural dimensions, control-oriented reserved design, material and safety coordinated design, and dynamic modeling and full-condition performance simulation. Among these, dynamic modeling and full-condition performance simulation are the core verification and optimization engines, continuously providing feedback to the front-end structural design until the optimal solution is obtained.

[0029] This application can be understood as follows: in the performance design layer, the analysis of the heat exchanger's full operating range is completed; a transient model of the heat exchanger is established; and the flow channel structure, process layout, and materials are collaboratively optimized using the unit temperature difference thermogravimetric ratio as the objective function.

[0030] The online control layer is integrated into the heat exchanger body defined by the performance design layer, and a sensor group, actuator group, and controller are installed on the heat exchanger. The sensor group is used to monitor the inlet and outlet flow rates, inlet and outlet pressures, and inlet and outlet temperatures of the fluids on both sides of the heat exchanger in real time. These sensors may include, but are not limited to, temperature sensors, pressure sensors, and flow sensors; this application does not impose specific limitations. The actuator group includes at least a variable frequency pump or variable frequency fan (variable frequency valve) installed in the fluid passage of the heat exchanger, and a bypass pipeline and regulating valve (bypass valve) installed on at least one side of the heat exchanger. The controller is used to coordinate the control of the bypass valve and variable frequency valve based on the data collected by the sensor group and the adjustment commands, so as to maintain the optimal outlet flow rate and outlet temperature inside the heat exchanger under varying loads.

[0031] It should be noted that, in order to achieve rapid response and precise control of the heat exchanger under a wide range of operating conditions, this application embodiment integrates at least two types of adjustment commands to form a multi-command collaborative adjustment strategy with complementary advantages. This multi-command collaborative adjustment strategy ensures that the heat exchanger can operate stably and accurately under various dynamic loads.

[0032] It can be understood that the online control layer in this application embodiment configures the sensor group, actuator group and controller for the optimized heat exchanger structure; designs a multi-instruction coordinated adjustment strategy, clarifies the combination logic and switching conditions of frequency conversion adjustment instructions and bypass adjustment instructions; and tunes the controller parameters, such as the parameters of the target model prediction controller, the parameters of the feedback controller, the parameters of the multi-mode coordinated logic, the state estimation and filter parameters, etc. This application does not impose specific limitations.

[0033] The intelligent operation and maintenance layer is used to build and maintain a digital twin that is synchronized with the target heat exchanger. The digital twin is a high-fidelity dynamic virtual model integrating physical mechanisms and real-time operational data. It continuously receives real-time operational data from the target heat exchanger through a data interface, such as the temperature, pressure, flow rate of the fluids on both sides, and actuator status (this application does not impose specific limitations). This allows for real-time calibration and status synchronization of key parameters within the digital twin, such as heat transfer coefficient and fouling thermal resistance, ensuring that the digital twin accurately maps the current state and performance evolution of the target heat exchanger.

[0034] It can be understood that the intelligent operation and maintenance layer in this application embodiment can construct a digital twin; deploy a target machine learning model for performance prediction and fault diagnosis; and establish collaborative control from diagnostic results to optimization instructions.

[0035] This application embodiment connects three levels—performance design, online control, and intelligent operation and maintenance—through information flow and control flow, adopting an integrated architecture to systematically overcome the technical challenges of performance degradation, low efficiency, and unstable operation of heat exchangers under variable load conditions.

[0036] The following description, with reference to the accompanying drawings, outlines a collaborative design and control method, apparatus, device, and storage medium for heat exchangers according to embodiments of this application. Addressing the issues mentioned in the background art, such as the tendency for fluid flow rate and velocity to deviate from the design point under varying load conditions, leading to decreased heat transfer and efficiency, uneven flow causing vibration, poor temperature control accuracy, and the potential for property changes and temperature pinch-offs in supercritical working fluids; furthermore, the failure to consider load adaptability in post-processing results in suboptimal performance, slow response, and safety hazards. This application provides a collaborative design and control method for heat exchangers. In this method, the collected operating data of the target heat exchanger under at least one operating condition is input into a pre-constructed multi-objective collaborative optimization model, thereby outputting performance coefficient data that meets preset performance conditions. After controlling the target heat exchanger to operate according to the performance coefficient data, the system determines the corresponding adjustment commands based on the flow rate, pressure, and temperature values ​​of the target heat exchanger under the current state. Then, by combining the digital twin to determine the performance degradation data and / or fault diagnosis results of the target heat exchanger, it generates optimization commands for the target heat exchanger and controls it. This ensures the dynamic adaptability of the heat exchanger throughout its entire lifecycle from inception to operation, ensuring that the heat exchanger maintains high heat transfer efficiency across the entire range from low load to overload. This significantly improves equipment reliability and lifespan, and substantially increases the system's annual energy efficiency, achieving a leap from "passive maintenance" to "proactive operation and maintenance." This solves the problems in related technologies where, under variable load conditions, when the load deviates from the design point, changes in fluid flow rate and velocity can lead to decreased heat transfer and efficiency, uneven flow can cause vibration, and temperature control accuracy is poor. Furthermore, in supercritical working fluids, changes in physical properties and temperature pinch points are also prone to occur. In addition, post-event adjustments do not consider variable load adaptability, resulting in difficulty in achieving optimal performance, slow response, and potential safety hazards.

[0037] Specifically, Figure 2 This is a flowchart of a collaborative design and control method for a heat exchanger provided according to an embodiment of this application.

[0038] like Figure 2 As shown, the collaborative design and control method for this heat exchanger includes the following steps: In step S201, the operating data of the target heat exchanger under at least one operating condition is collected, and the operating data is input into a pre-constructed multi-objective collaborative optimization model to output the performance coefficient data of the target heat exchanger to meet the preset performance conditions.

[0039] It is understood that, in the embodiments of this application, the operating data may include, but is not limited to, process data, flow path data, flow channel configuration data, structural dimension data, fluid data, heat exchange unit data, thermal fatigue performance data, material data, and fatigue data, etc., and this application does not impose specific limitations.

[0040] In some embodiments, this application can collect operating data of the target heat exchanger under different operating conditions and input the operating data into a pre-built multi-objective collaborative optimization model to output performance coefficient data that meets preset performance conditions. The preset performance conditions can be set by those skilled in the art according to actual conditions, and this application does not impose specific limitations.

[0041] Optionally, in one embodiment of this application, before inputting the operating data into the pre-constructed multi-objective collaborative optimization model, the method further includes: calculating the unit temperature difference heat weight ratio of the target heat exchanger based on the heat weight ratio and heat transfer temperature difference of the target heat exchanger; determining the objective function of the multi-objective collaborative optimization model based on the unit temperature difference heat weight ratio; determining the constraints of the multi-objective collaborative optimization model based on the heat transfer temperature difference and pressure drop; and constructing the multi-objective collaborative optimization model based on the objective function and constraints.

[0042] It is understood that the embodiments of this application abandon the single "rated operating condition performance" or "minimum heat transfer temperature difference" and adopt an index that can reflect the comprehensive performance throughout the entire life cycle, such as the heat-to-gravity ratio per unit temperature difference, so as to obtain the objective function of the multi-objective collaborative optimization model.

[0043] The thermogravimetric ratio per unit temperature difference is defined as the ratio of the target heat exchanger's thermogravimetric ratio to the heat transfer temperature difference, measuring the efficiency of "lightweighting" and "heat transfer driving force." A good wide-condition design should achieve high power density even with a relatively low average temperature difference. The formula for calculating the thermogravimetric ratio per unit temperature difference can be, but is not limited to, the following: , in, This indicates heat exchange power, in kW. This indicates the weight of the heat exchanger, in kg. This indicates the temperature difference in heat transfer within the heat exchanger, expressed in °C.

[0044] Furthermore, in this embodiment of the application, the heat transfer temperature difference being greater than the target threshold and the pressure drop being less than the target threshold are used as constraints for the multi-objective collaborative optimization model, and then a multi-objective collaborative optimization model is constructed based on the objective function and the constraints.

[0045] The optimization variables involved in the multi-objective collaborative optimization model may include, but are not limited to, heat exchanger length, flow channel hydraulic diameter, fin structure, flow arrangement, etc., and this application does not impose specific restrictions. The optimization algorithm may be a genetic algorithm, particle swarm optimization, etc., and this application does not impose specific restrictions. In this application, the embodiments can use the optimization algorithm to automatically find the best solution in a large design space and find a balance point—that is, appropriately increasing the heat transfer temperature difference under rated operating conditions (thereby reducing size and weight), but in return, achieving stable and efficient performance throughout the entire operating range.

[0046] For example, the embodiments of this application use Taking the design of a fuel cooler as an example, the relevant design is carried out under 100% load ( To minimize the temperature difference at 550℃ (imported temperature), a compact but elongated heat exchanger was designed. However, when the system operates at 30% load, The flow rate is greatly reduced, the flow velocity is lowered, and the heat transfer coefficient is significantly reduced. Meanwhile, Changes in physical properties may cause the quasi-critical point to shift. At this point, the actual heat transfer temperature difference may shrink drastically to 1-2℃, resulting in a temperature pinch point where the heat exchange task cannot be completed.

[0047] This application embodiment is based on a multi-objective collaborative optimization model. Through operating domain scanning, it was found that 40% load is the critical operating condition with the smallest heat transfer temperature difference in the entire operating domain. Optimization is then performed with the heat-to-weight ratio per unit temperature difference as the objective, thereby providing a design scheme with a slightly shorter flow channel and a slightly larger flow path through the optimization algorithm. Table 1 shows the results and performance comparison of the heat exchangers designed by the two methods.

[0048] Table 1

[0049] As shown in Table 1, the heat transfer temperature difference in this embodiment increases from 8°C to 10°C at 100% load (sacrificing rated point performance), but significantly reduces weight. Furthermore, at 40% load, the heat transfer temperature difference remains within a safe range of 4.5°C (ensuring overall performance). By introducing the "unit temperature difference heat-to-weight ratio," and simultaneously considering both lightweighting (heat-to-weight ratio) and heat transfer driving force utilization (minimum heat transfer temperature difference under all operating conditions), the design goal was successfully shifted from "pursuing single-point excellence" to "ensuring overall efficiency," achieving a significant reduction in system weight (-20%) and a 28% increase in the unit temperature difference heat-to-weight ratio.

[0050] Furthermore, embodiments of this application may also use the average annual comprehensive performance coefficient as the objective function of the multi-objective collaborative optimization model, thereby constructing a multi-objective collaborative optimization model.

[0051] The annual average comprehensive performance coefficient can be understood as the weighted average performance of the heat exchanger calculated based on the expected annual load distribution curve, requiring that the heat exchanger design must consider performance under low load conditions. Its calculation formula can be, but is not limited to, the following: , Among them, the performance coefficient can be calculated or queried through experimental data or verified simulation models. It can be used to represent the performance coefficient of the heat exchanger at representative operating points (such as the midpoint) at each level, such as heat transfer efficiency, heat transfer coefficient, and heat-to-gravity ratio per unit temperature difference. The weighting coefficient is usually determined by the proportion of operating time or energy contribution of the operating condition. It can be set by those skilled in the art according to the actual situation. This application does not impose specific restrictions.

[0052] Optionally, in one embodiment of this application, the operating data is input into a pre-built multi-objective collaborative optimization model to output performance coefficient data of the target heat exchanger to meet preset performance conditions. This includes: determining the flow rate data in the performance coefficient data based on the process data, flow path data, and flow channel configuration data in the operating data; determining the heat transfer temperature difference data in the performance coefficient data based on the structural dimension data in the operating data; determining the pressure drop data in the performance coefficient data based on the fluid data and heat exchange unit data in the operating data; and determining the thermal stress data in the performance coefficient data based on the thermal fatigue resistance performance data, material data, and fatigue data in the operating data.

[0053] It is understood that, in the embodiments of this application, the performance coefficient data may include, but is not limited to, flow rate data, heat transfer temperature difference data, pressure drop data, and thermal stress data, etc., and this application does not impose specific limitations.

[0054] In some embodiments, the present application embodiments can determine the flow data in the performance coefficient data based on the process data, flow path data and flow channel configuration data in the running data.

[0055] For example, in order to achieve stable and efficient operation of the target heat exchanger over a wide flow range, the embodiments of this application are as follows: Figure 3 As shown, flow design was performed, which involved targeted design of the basic flow path and configuration of the target heat exchanger. This included: (1) Multi-pass, multi-flow path design: adopt multi-pipe or multi-plate flow path arrangement, so that when the total flow rate of the system decreases due to the load reduction, the working fluid velocity in a single flow path is effectively maintained within a reasonable range by reducing the number of flow channels and increasing the number of flow passes, thereby avoiding the significant deterioration of the heat transfer coefficient due to the low flow velocity and ensuring the basic heat transfer performance of the heat exchanger under low load conditions.

[0056] (2) Wide-range high-efficiency flow channel configuration: In terms of the micro-configuration of the flow channel, the enhanced structure that can maintain excellent heat transfer characteristics over a wide Reynolds number range is preferred. For example, micro-fin structure (as shown in Figure 4(a) and Figure 4(b)), square columnar turbulence channel structure (as shown in Figure 4(c)), adaptive channel (as shown in Figure 4(d)), etc., can maintain a high Nusselt number over a large flow velocity fluctuation range by periodically disturbing and redeveloping the boundary layer, thus making the heat transfer performance insensitive to flow rate changes.

[0057] In some embodiments, the present application embodiments can determine the heat transfer temperature difference data in the performance coefficient data based on the structural dimension data in the operating data.

[0058] It should be noted that related technologies often fall into a dilemma: pursuing the minimum heat transfer temperature difference under rated operating conditions will significantly increase the heat exchange area and manufacturing cost; conversely, deliberately reducing the area to achieve equipment miniaturization may result in an excessively large heat transfer temperature difference, limiting performance. Neither of these single-objective optimization strategies is suitable for the needs of wide-range operation. To solve this problem, such as... Figure 3 As shown in the figure, this application proposes a collaborative design strategy for heat transfer temperature difference and structural dimensions. The main content is to introduce evaluation indicators for the entire operating condition system and use these indicators for collaborative optimization. This shifts the design objective from an isolated rated operating point to an "operating condition domain" encompassing all expected operating states, thereby ensuring that the heat exchanger possesses acceptable or even excellent performance throughout the entire domain. The main content is as follows: (1) Define the full-condition performance index.

[0059] In this application, the embodiments abandon the single "rated operating condition performance" or "minimum heat transfer temperature difference" and adopt an index that can reflect the comprehensive performance throughout the entire life cycle, such as the annual average comprehensive performance coefficient and the heat-to-gravity ratio per unit temperature difference.

[0060] (2) Working condition domain scanning and identification based on dynamic model.

[0061] Establish a parametric model: Create a steady-state and transient model of the heat exchanger that includes all key geometric parameters (such as length, hydraulic diameter, fin spacing, and number of passes).

[0062] Define and scan the operating domain: clearly define all the boundaries that the heat exchanger needs to operate on, which can be ( The combination, in which, For hot-side fluid flow rate, This represents the cold-side fluid flow rate. This refers to the inlet temperature on the hot side. This refers to the cold side inlet temperature.

[0063] Identify critical operating conditions: Identify all boundaries and find the operating point with the smallest heat transfer temperature difference. This point is usually not under rated operating conditions, but rather under partial load, thus ensuring that the heat transfer temperature difference at the critical point is greater than the allowable minimum value, such as 3-5℃.

[0064] In some embodiments, the present application embodiments can determine the pressure drop data in the performance coefficient data based on the fluid data and heat exchange unit data in the operating data; For example, this application embodiment aims to provide heat exchanger systems with flexible and reliable adjustment capabilities under a wide range of operating conditions. For application scenarios with extremely wide load variations, a modular architecture is adopted, distributing the total heat exchange capacity to multiple parallel heat exchange units that can be independently started and stopped. Thus, under low-load conditions, by controlling valves, some heat exchange units are disconnected from the flow path, ensuring that the still-operating units can maintain their high-efficiency flow range. This not only broadens the stable operating range of the entire system but also significantly improves the overall energy efficiency under partial loads through a "start-stop instead of adjustment" approach. The design concept of the modular heat exchanger architecture is as follows: Figure 5 As shown, when the main fluid flows into the main pipeline, the control valve in the fluid distributor cuts off part of the heat exchange unit from the flow path, and then the fluid outlet is determined by the fluid manifold.

[0065] In addition, such as Figure 3 As shown, in the design phase of the heat exchanger body and piping system, this application does not pursue the absolute minimum flow resistance. Instead, it strategically reserves a reasonable system pressure drop margin through a control-oriented design. This ensures that when the flow rate is changed by regulating valves to adapt to new operating conditions, the variation in the total system pressure drop always remains within the stable operating range of the pump or fan. This provides the necessary operating margin for the intervention of core regulation methods and effectively avoids the risk of overload of the drive equipment.

[0066] In some embodiments, the present application embodiments can determine the thermal stress data in the performance coefficient data based on the thermal fatigue performance data, material data, and fatigue data in the operating data.

[0067] It is understood that, in the embodiments of this application, the frequent start-ups and shutdowns and drastic temperature changes inherent in variable load operation pose a severe challenge to the integrity and long-term reliability of the heat exchanger structure. Therefore, as... Figure 3 As shown, the embodiments of this application incorporate a co-design of materials and safety, the main contents of which are as follows: (1) Material selection criteria based on thermal fatigue resistance: In order to ensure the long-term durability of the heat exchanger under a wide range of operating conditions, the material selection of its core components follows the basic principle of prioritizing thermal fatigue resistance. The selected materials must have the characteristics of maintaining microstructure stability and crack initiation resistance under high-frequency temperature fluctuations, so as to provide a basic guarantee for the safe and reliable operation of the heat exchanger through the intrinsic durability of the materials.

[0068] (2) Thermal stress and fatigue management: Given that frequent changes in operating conditions will cause uneven thermal expansion and contraction between components and generate alternating thermal stress, it is necessary to carry out thermal-mechanical coupling transient simulation in the design stage, so as to predict the stress distribution of key pressure and load-bearing components under temperature cycling in a forward-looking manner, thereby optimizing the corresponding structure to reduce stress concentration, avoiding the risk of cracking and leakage caused by low-cycle fatigue from the design source, and ensuring its structural safety throughout the entire life cycle.

[0069] Furthermore, the flow-adaptive design, the coordinated strategy of heat transfer temperature difference and structural dimensions, the control-oriented reserved design, and the coordinated design of materials and safety in the embodiments of this application constitute the design input and physical basis of the high-performance heat exchanger. To ensure that these designs can achieve optimal performance in complex wide-condition environments, the embodiments of this application establish a verification system based on dynamic modeling and full-condition performance simulation. This system is not an independent design step, but a core verification and optimization engine that runs through all design stages. Its workflow and core functions are as follows: (1) Core functions: As an integrated verification and collaborative optimization platform, and with a high-fidelity dynamic model containing all the above design parameters as the core, through multi-objective collaborative optimization and full-condition performance verification, it quantitatively evaluates and drives the design schemes of flow adaptability design, heat transfer temperature difference and structural size collaborative strategy, control-oriented reserved design and material and safety collaborative design.

[0070] Multi-objective collaborative optimization: Driven by the performance coefficient data generated by the aforementioned simulation, an intelligent optimization algorithm is introduced. With the goal of maximizing the "unit temperature difference heat-to-gravity ratio", key design variables in the flow adaptive design, heat transfer temperature difference and structural size collaborative strategy, control-oriented reserved design and material and safety collaborative design, such as flow channel configuration, process layout, core size, etc. are adjusted and optimized in reverse. This application does not impose specific restrictions, thereby finding the global optimal solution among multiple objectives such as heat transfer, flow, lightweighting and control quality.

[0071] Full-condition performance verification: Based on the initial design scheme, a systematic simulation scan of the heat exchanger's steady-state and transient performance throughout the entire expected operating range is performed to quantitatively verify whether the output performance coefficient data of the flow adaptive design, the heat transfer temperature difference and structural size coordination strategy, the control-oriented reserved design and the material and safety coordinated design meet the preset performance conditions.

[0072] (2) System output: closed-loop iteration and scheme solidification. In this application embodiment, the automated iterative closed loop of "design-simulation-optimization-re-verification" continuously provides data feedback for the front-end structural design. However, the final output is not an isolated simulation report, but a solidified final design scheme that has been verified under full operating conditions and optimized by multiple objectives. This ensures that all design efforts in flow adaptability design, heat transfer temperature difference and structural size coordination strategy, control-oriented reserved design and material and safety coordination design are systematically integrated and directed to a physical entity with optimal performance and safety in a wide operating range, thereby outputting performance coefficient data that meets the preset performance conditions.

[0073] For example, in the transient model of the microchannel heat exchanger in this application embodiment, due to its small flow channel, high flow velocity, and strong heat conduction and convection effects, relatively complex heat transfer phenomena usually occur in the transient state. Therefore, this application embodiment needs to consider the heat exchange process between the fluid and the solid (usually a metal material or other solid medium). The governing equations of the transient model of the heat exchanger include the fluid side (two-phase fluid) and the solid side (wall).

[0074] In microchannel heat exchangers, there are typically hot and cold fluids (or two different working fluids). The transient heat transfer of each fluid can be described by the energy conservation equation, thus revealing the transient temperature distribution and heat transfer process of the hot and cold fluids in the microchannel.

[0075] The expression for the thermal fluid (fluid 1) can be, but is not limited to, as follows: , in, Density of a thermal fluid, in units of ; Specific heat capacity of a hot fluid, expressed in J / (kg·K); The temperature of a hot fluid is expressed in Kelvin (K). Indicates the flow velocity of a hot fluid, in m / s; Thermal conductivity of a fluid, expressed in W / (m·K); The heat source term represents the heat fluid, in units of... .

[0076] The expression for the cold fluid (fluid 2) can be, but is not limited to, as follows: , in, The density of a cold fluid is expressed in units of... ; This indicates the specific heat capacity of a cold fluid, expressed in J / (kg·K). This indicates the temperature of a cold fluid, expressed in Kelvin (K). This indicates the flow velocity of the cold fluid, measured in m / s. This represents the thermal conductivity of a cold fluid, measured in W / (m·K). The heat source term for the cold fluid, in units of .

[0077] Furthermore, in this embodiment of the microchannel heat exchanger, heat is exchanged with the fluid through conduction and convection on the solid side (wall). The temperature change of the solid portion needs to consider the heat conduction equation, wherein the expression for the heat conduction equation of the solid wall (solid 1) can be, but is not limited to, the following: , in, The density of the wall surface, in units of ; This indicates the specific heat capacity of the wall surface, expressed in J / (kg·K). This indicates the temperature of the wall surface, in Kelvin (K). This represents the thermal conductivity of the wall surface, measured in W / (m·K). Indicates the heat source generated by the wall, in units of .

[0078] Furthermore, in the embodiments of this application, there is strong heat exchange between the fluid and the wall inside the microchannel, and the fluid may be in a turbulent or laminar flow state; therefore, the choice of heat transfer coefficient may vary over time. The overall heat transfer can be described by a combination of convection and conduction.

[0079] In this application embodiment, the heat exchange between the fluid and the wall is commonly described using a convective heat transfer model, the expression of which may be, but is not limited to: , in, It is the convective heat transfer coefficient between the fluid and the wall. and These are the temperatures of the fluid and the wall, respectively.

[0080] For microchannel flow, the heat transfer coefficient is typically a function of flow velocity and flow state (laminar or turbulent) and varies with time.

[0081] Furthermore, the expression for the boundary condition of the inlet temperature of the hot fluid in the embodiments of this application may be, but is not limited to, as follows: , The expression for the boundary condition of the inlet temperature of the cold fluid can be, but is not limited to, as follows: , Among them, the boundary conditions change over time, usually the time-varying temperature input, reflecting the changes in actual operation.

[0082] The expression for the boundary condition of the outlet temperature of the hot fluid can be, but is not limited to, as follows: , The expression for the boundary condition of the outlet temperature of the cold fluid can be, but is not limited to, as follows: , in, and These represent the flow lengths of the hot and cold fluids in the microchannel, respectively.

[0083] Heat exchange between the fluid and the wall is modeled through convective heat transfer. The expression for the heat transfer boundary conditions on the solid side can be, but is not limited to, as follows: , , in, and It is the convective heat transfer coefficient between the hot fluid and the cold fluid and the wall. It is the temperature of the wall surface.

[0084] For the wall, assuming the initial temperature distribution is uniform, the expression for the initial conditions on the solid side can be, but is not limited to, as follows: , in, It is the initial temperature of the wall surface.

[0085] In addition, in the embodiments of this application, the transient effect of the microchannel heat exchanger is related to the following factors: (1) the heat exchange efficiency between the hot fluid and the cold fluid: due to the high flow velocity of the microchannel fluid, the influence of the flow state and turbulence is more significant, therefore the heat transfer coefficient is higher. and It may be a function of time and flow rate; (2) The effect of wall temperature change: The change of wall temperature affects the heat exchange efficiency of the fluid, and thus affects the overall heat exchange performance of the system; (3) Multiphase flow: In some applications, the fluid may be a two-phase flow, which makes the heat exchange more complex and requires consideration of the heat transfer and phase change effect between the gas and liquid phases.

[0086] In summary, the transient model of the heat exchanger in this embodiment encompasses the heat transfer processes between the fluid, the solid, and between the fluid and the solid. By combining the fluid's energy equation and the solid wall's conduction equation with appropriate boundary conditions, the transient heat exchange process between the hot and cold fluids in the microchannel heat exchanger can be described. Considering transient effects is crucial for designing and optimizing the performance of microchannel heat exchangers, especially under time-varying and dynamic operating conditions.

[0087] For example, embodiments of this application are used for thermal management of hypersonic vehicles. The main design process of the fuel cooler design is shown in Tables 2-5, and will not be elaborated further here. Table 2 is a schematic table illustrating the design basis and core indicators provided according to one embodiment of this application; Table 3 is a schematic table illustrating the design schemes and parameters at each level provided according to one embodiment of this application; Table 4 is a schematic table illustrating the dynamic modeling and optimization verification process provided according to one embodiment of this application; and Table 5 is a schematic table illustrating the final design results and performance indicators provided according to one embodiment of this application.

[0088] Table 2

[0089] Table 3

[0090] Table 4

[0091] Table 5

[0092] The embodiments of this application fully verify that, under the premise of ensuring safety, efficient operation is achieved across the entire operating range through modular and flow-adaptive design; the optimization engine based on dynamic modeling systematically solves the multi-objective conflict problem and significantly improves power density; combined with the collaborative design of materials and structure, the reliability of the system is ensured under extreme conditions, thus providing a practical and feasible technical path for the thermal management system of hypersonic vehicles, demonstrating the effectiveness and advancement of the embodiments of this application in solving the design challenges of heat exchangers under extreme operating conditions.

[0093] In step S202, after controlling the target heat exchanger to operate according to the performance coefficient data, the flow rate, pressure and temperature values ​​of the target heat exchanger in the current state are collected, and the adjustment commands of the bypass valve and / or frequency converter valve in the target heat exchanger are determined based on the flow rate, pressure and temperature values.

[0094] In some embodiments, after the target heat exchanger is controlled to operate according to the performance coefficient data, the flow rate, pressure and temperature values ​​of the target heat exchanger in the current state are collected, and then the adjustment commands of the bypass valve and the frequency converter valve in the target heat exchanger are determined based on the flow rate, pressure and temperature values.

[0095] In some embodiments, after the target heat exchanger is controlled to operate according to the performance coefficient data, the flow rate, pressure and temperature values ​​of the target heat exchanger in the current state are collected, and then the adjustment command of the bypass valve in the target heat exchanger is determined based on the flow rate, pressure and temperature values.

[0096] In some embodiments, after the target heat exchanger is controlled to operate according to the performance coefficient data, the flow rate, pressure and temperature values ​​of the target heat exchanger in the current state are collected, and then the adjustment command of the variable frequency valve in the target heat exchanger is determined based on the flow rate, pressure and temperature values.

[0097] Optionally, in one embodiment of this application, determining the adjustment command for the bypass valve and / or variable frequency valve in the target heat exchanger based on the flow rate, pressure, and temperature values ​​includes: inputting the flow rate, pressure, and temperature values ​​into the target model predictive controller to output the load change rate of the target heat exchanger; in response to the load change rate of the target heat exchanger being greater than a preset threshold, determining the adjustment command as a bypass adjustment command for the bypass valve, and determining the opening adjustment value of the bypass valve according to the bypass adjustment command; in response to the load change rate being less than or equal to a preset threshold, determining the adjustment command as a variable frequency adjustment command for the variable frequency valve, and determining the variable speed pump speed value of the variable frequency valve according to the variable frequency adjustment command.

[0098] In some embodiments, the flow rate, pressure, and temperature values ​​can be input into the target model predictive controller to output the load change rate of the target heat exchanger.

[0099] For example, embodiments of this application may install a sensor group, an actuator group, and a controller on the heat exchanger. The sensor group is used to monitor the inlet and outlet flow rates, inlet and outlet pressures, and inlet and outlet temperatures of the fluids on both sides of the heat exchanger in real time. It may include, but is not limited to, temperature sensors, pressure sensors, and flow sensors, etc., and this application does not impose specific limitations. The actuator group includes at least a variable frequency pump or variable frequency fan (variable frequency valve) installed in the fluid passage of the heat exchanger, and a bypass pipeline and regulating valve (bypass valve) installed on at least one side of the heat exchanger. The controller is used to coordinate and control the bypass valve and the variable frequency valve based on the data collected by the sensor group and the adjustment commands, so as to maintain the optimal outlet flow rate and outlet temperature inside the heat exchanger under varying loads.

[0100] It should be noted that the embodiments of this application integrate at least two types of adjustment commands to form a multi-command collaborative adjustment strategy with complementary advantages. The adjustment commands may include, but are not limited to, bypass adjustment commands and frequency converter adjustment commands; this application does not impose specific limitations.

[0101] The bypass adjustment command is used to quickly and accurately control downstream key parameters, such as temperature, by changing the flow path without directly affecting the operating point of the upstream system. This application does not impose specific limitations on this.

[0102] Variable frequency control commands are used to configure variable frequency devices at the drive end of system pumps or fans. By continuously adjusting the speed, the volumetric flow rate of the working fluid is changed, thereby achieving a smooth shift of the heat exchanger's operating point. It is the most energy-efficient control method and is especially suitable for dealing with gradual, small-amplitude load changes.

[0103] In addition, in this embodiment, the bypass adjustment command and the frequency conversion adjustment command follow the principle of "small-range fine adjustment relies on frequency conversion, and large-range step change relies on bypass". The central controller makes intelligent judgments and coordinates outputs based on the load change amplitude and rate to achieve the best balance between energy efficiency and dynamic response speed.

[0104] In some embodiments, when the load change rate is greater than a preset threshold, the adjustment command can be determined as a bypass adjustment command for a bypass valve, thereby determining the opening adjustment value of the bypass valve; when the load change rate is less than or equal to a preset threshold, the adjustment command can be determined as a frequency conversion adjustment command for a frequency converter valve, thereby determining the variable speed pump speed value of the frequency converter valve. The preset threshold can be set by those skilled in the art according to actual conditions, and this application does not impose specific limitations.

[0105] For example, to achieve rapid response and precise control of the heat exchanger under a wide range of operating conditions, this application embodiment constructs a highly efficient and reliable active control and regulation system after controlling the target heat exchanger to operate according to the performance coefficient data. Through a multi-command coordinated regulation strategy, it ensures that the heat exchanger can operate stably and accurately under various dynamic loads. A schematic diagram is shown below. Figure 6 As shown, the main content is as follows: Step S601: Obtain the system settings of the target heat exchanger.

[0106] Step S602: Obtain the load change feedforward signal.

[0107] Step S603: Internal prediction model.

[0108] In this embodiment, the internal prediction model is based on the heat exchanger transient model and uses a rolling optimization algorithm to achieve accurate dynamic performance prediction, thereby completing the update and correction of the target model predictive controller.

[0109] Step S604: Target model predictive controller.

[0110] The target model predictive controller uses a high-precision sensor array to monitor the inlet and outlet temperatures, pressures, and flow rates of the fluids on both sides of the heat exchanger in real time. It then uses a transient model of the heat exchanger to predict the system's operating state in the near future. Based on this prediction, a rolling optimization algorithm is used to calculate the opening adjustment value of the bypass valve and the speed value of the variable-speed pump of the frequency converter valve, and then drives the actuator assembly. This feedforward-feedback composite control mode enables the system to not only quickly eliminate existing control deviations but also proactively compensate for dynamic disturbances caused by load changes, thereby significantly improving control accuracy and system stability.

[0111] Step S605: Determine whether the load change rate is greater than the preset threshold.

[0112] In this embodiment of the application, the target model can be used to predict the load change rate of the target heat exchanger output by the controller, and if the load change rate is greater than a preset threshold, step S606 is executed; otherwise, step S608 is executed.

[0113] Step S606: Determine that the adjustment command is a bypass adjustment command.

[0114] Step S607: Determine the opening adjustment value of the bypass valve.

[0115] Step S608: Determine that the adjustment command is a frequency converter adjustment command.

[0116] Step S609: Determine the variable speed pump speed value of the variable frequency valve.

[0117] Step S610: Actuator group.

[0118] In this embodiment, the actuator group can adjust the corresponding bypass valve or frequency converter valve according to the corresponding opening adjustment value or the speed value of the variable speed pump.

[0119] Step S611: Sensor group.

[0120] The sensor group in this application embodiment may include, but is not limited to, a temperature sensor, a pressure sensor, and a flow sensor.

[0121] For example, embodiments of this application in... In power generation systems, maintaining the working fluid in a specific phase state is crucial for ensuring cycle safety. The physical properties (such as specific heat capacity and density) of the fluid undergo drastic changes in the quasi-critical region. If the cooler outlet temperature is too low and the fluid enters this region, it will lead to a sharp increase in system pressure drop, flow instability, and may even cause compressor surge. To solve this problem, embodiments of this application... A bypass line is installed on the hot side of the cooling water heat exchanger. Its core control objective is to ensure... The outlet temperature of the cooler is always higher than its quasi-critical temperature. For example, at a pressure of 10 MPa, the temperature needs to be maintained above 320 K.

[0122] The specific adjustment process is as follows: When the system load decreases, Reduced flow rate poses a risk of overcooling. In this situation, the controller will open the hot-side bypass valve to divert some of the high-temperature air from the turbine outlet. The flow is diverted, bypassing the cooler and directly contacting the low-temperature water at the cooler outlet. By mixing the components and precisely controlling the bypass flow rate, the temperature of the mixed working fluid can be accurately maintained within a safe range slightly above the quasi-critical point, thereby ensuring the stable operation of the entire system.

[0123] This application embodiment combines bypass adjustment commands and frequency converter adjustment commands to achieve precise control over a wide range of operating conditions. The bypass adjustment command is used to deal with large-scale and rapid load changes, i.e., when the load change rate is greater than a preset threshold, such as aircraft maneuvering. The frequency converter adjustment focuses on fine-tuning the flow within a small range, i.e. when the load change rate is less than or equal to the preset threshold, thereby improving energy efficiency. For example, when the load change rate is detected to be greater than the preset threshold, the bypass adjustment command is activated first to quickly stabilize the system. When the load change rate is less than or equal to the preset threshold, the frequency converter adjustment command is used as the main command, thus balancing dynamic response speed and system operating economy.

[0124] In step S203, based on the flow rate, pressure, temperature, regulation command, and the digital twin corresponding to the target heat exchanger, the performance degradation data and / or fault diagnosis results of the target heat exchanger are determined. Based on the performance degradation data and / or fault diagnosis results, optimization commands for the target heat exchanger are generated, and the target heat exchanger is controlled according to the optimization commands.

[0125] It is understood that the embodiments of this application can construct and maintain a digital twin that is synchronously updated with the target heat exchanger. The digital twin is a high-fidelity dynamic virtual model integrating physical mechanisms and real-time operational data. It continuously receives real-time operational data from the target heat exchanger through a data interface, such as the temperature, pressure, flow rate of the fluids on both sides, and actuator status. This application does not impose specific limitations on these data. This allows for real-time calibration and status synchronization of key parameters within the digital twin, such as heat transfer coefficient and fouling thermal resistance, thereby ensuring that the digital twin accurately maps the current state and performance evolution of the target heat exchanger.

[0126] In summary, the digital twin in this application serves as a bridge connecting the physical world and the information space, transforming the target heat exchanger from a passive execution component into an intelligent agent with self-sensing, self-predicting, and self-optimizing capabilities. This can be understood as using the digital twin as a "sandbox" to perform advanced simulations of system performance in the short term. By comparing performance degradation data and fault diagnosis results under different control strategies, optimization instructions for the target heat exchanger are generated and sent to the target heat exchanger for execution, thus forming a closed-loop optimization from "sensing" to "decision-making" to "execution."

[0127] In some embodiments, this application can determine the performance degradation data and fault diagnosis results of the target heat exchanger based on the flow rate value, pressure value, temperature value, adjustment command and the digital twin corresponding to the target heat exchanger, thereby generating optimization commands for the target heat exchanger and controlling the target heat exchanger according to the optimization commands.

[0128] For example, the process of generating target heat exchanger optimization instructions in this application embodiment is as follows: Figure 7 As shown, the main content is as follows: Step S701: Digital twin.

[0129] In this embodiment, the digital twin is a high-fidelity dynamic virtual model that integrates physical mechanisms and real-time operating data, and can update and synchronize the real-time operating data of the target heat exchanger in real time.

[0130] Step S702: Intelligent Operation and Maintenance Layer.

[0131] In this embodiment, the target machine learning model can be used in the intelligent operation and maintenance layer to generate and determine performance degradation data and fault diagnosis results, and generate corresponding optimization instructions.

[0132] Step S703: Target heat exchanger.

[0133] In this embodiment, the target heat exchanger can be controlled according to the optimization instructions.

[0134] Step S704: Operation and maintenance decision support.

[0135] Optionally, in one embodiment of this application, determining the performance degradation data and / or fault diagnosis results of the target heat exchanger based on the flow rate value, pressure value, temperature value, adjustment command, and the digital twin corresponding to the target heat exchanger includes: determining the initial performance degradation data and / or initial fault diagnosis results of the digital twin based on the flow rate value, pressure value, temperature value, and adjustment command; and obtaining the performance degradation data and / or fault diagnosis results based on the initial performance degradation data and / or initial fault diagnosis results.

[0136] In some embodiments, the present application embodiments can determine the initial performance degradation data and initial fault diagnosis results of the digital twin based on flow rate value, pressure value, temperature value and adjustment command, and then obtain performance degradation data and / or fault diagnosis results.

[0137] For example, in the embodiments of this application, the flow rate, pressure and temperature values ​​of the target heat exchanger can be obtained in real time through a sensor group, and the adjustment command of the target heat exchanger can be received. Then, a digital twin constructed using digital twin technology is used to simulate the operation of the target heat exchanger. By analyzing the digital twin, the initial performance degradation data and initial fault diagnosis results of the target heat exchanger can be obtained.

[0138] Furthermore, to improve the accuracy of the results, the initial performance degradation data and initial fault diagnosis results can be further optimized through processes such as data smoothing, error correction, and multi-source data fusion, thereby obtaining more accurate and reliable performance degradation data and fault diagnosis results, providing strong support for system maintenance, optimization, and decision-making.

[0139] Optionally, in one embodiment of this application, determining the initial performance degradation data and / or initial fault diagnosis results of the digital twin based on flow rate, pressure, temperature, and adjustment instructions includes: selecting a target machine learning model suitable for the digital twin based on historical and real-time data of the digital twin; and inputting the flow rate, pressure, temperature, and adjustment instructions into the target machine learning model to output the initial performance degradation data and / or initial fault diagnosis results.

[0140] It is understood that, in the embodiments of this application, the target machine learning model may include, but is not limited to, support vector machines, long short-term memory networks, deep learning classification networks, etc., and this application does not impose specific limitations.

[0141] In some embodiments, this application can analyze historical and real-time data of the digital twin, select a target machine learning model suitable for the digital twin, and then use the target machine learning model to output the corresponding initial performance degradation data and initial fault diagnosis results.

[0142] The initial performance degradation data may include, but is not limited to, the degradation trend of the heat transfer coefficient and the remaining service life of the equipment; this application does not impose specific limitations on this. The initial fault diagnosis results may include, but are not limited to, micro-leakage, local blockage, scaling, etc.; this application does not impose specific limitations on this.

[0143] For example, embodiments of this application can output the decay trend of the heat transfer coefficient and the remaining service life of the target heat exchanger through a long short-term memory network, and use a deep learning classification network to detect whether the target heat exchanger has micro-leakage, local blockage, scaling, etc., thereby realizing the location and diagnosis of early faults of the target heat exchanger.

[0144] According to the collaborative design and control method for heat exchangers proposed in this application, the collected operating data of the target heat exchanger under at least one operating condition can be input into a pre-constructed multi-objective collaborative optimization model, thereby outputting performance coefficient data that meets preset performance conditions. After controlling the target heat exchanger to operate according to the performance coefficient data, the corresponding adjustment command is determined based on the flow rate, pressure, and temperature values ​​of the target heat exchanger in the current state. Then, combined with the digital twin, the performance degradation data and / or fault diagnosis results of the target heat exchanger are determined to generate optimization commands for the target heat exchanger and control the target heat exchanger. This ensures the dynamic adaptability of the heat exchanger throughout its entire life cycle from its inception to operation, ensuring that the heat exchanger can maintain high heat transfer efficiency throughout the entire range from low load to overload, greatly improving equipment reliability and lifespan, significantly improving the system's annual average energy efficiency, and realizing a leap from "passive maintenance" to "proactive operation and maintenance". This solves the problems in related technologies, such as the tendency for fluid flow rate and velocity to change and thus deteriorate heat transfer and efficiency when the load deviates from the design point under variable load conditions, and the vibration caused by uneven flow. In addition, the temperature control accuracy is poor, and the material properties and temperature pinch points are also prone to occur in supercritical working fluids. Furthermore, the post-adjustment does not take into account the adaptability of variable load, making it difficult to achieve optimal performance, resulting in slow response and safety hazards.

[0145] Next, with reference to the accompanying drawings, a collaborative design and control device for a heat exchanger according to an embodiment of this application is described.

[0146] Figure 8 This is a block diagram of a collaborative design and control device for a heat exchanger provided according to an embodiment of this application.

[0147] like Figure 8 As shown, the collaborative design and control device 10 for the heat exchanger includes: an output module 100, a first determination module 200, and a control module 300.

[0148] The output module 100 is used to collect the operating data of the target heat exchanger under at least one operating condition and input the operating data into a pre-built multi-objective collaborative optimization model to output the performance coefficient data of the target heat exchanger to meet the preset performance conditions.

[0149] The first determining module 200 is used to control the target heat exchanger to operate according to the performance coefficient data, collect the flow rate, pressure value and temperature value of the target heat exchanger in the current state, and determine the adjustment command of the bypass valve and / or frequency converter valve in the target heat exchanger based on the flow rate, pressure value and temperature value.

[0150] The control module 300 is used to determine the performance degradation data and / or fault diagnosis results of the target heat exchanger based on the flow rate value, pressure value, temperature value, adjustment command and the digital twin corresponding to the target heat exchanger, so as to generate optimization commands for the target heat exchanger according to the performance degradation data and / or fault diagnosis results, and control the target heat exchanger according to the optimization commands.

[0151] Optionally, in one embodiment of this application, it further includes: a calculation module, a second determination module, a third determination module, and a construction module.

[0152] The calculation module is used to calculate the heat-to-gravity ratio per unit temperature difference of the target heat exchanger based on the heat-to-gravity ratio and heat transfer temperature difference of the target heat exchanger before inputting the running data into the pre-built multi-objective collaborative optimization model.

[0153] The second determination module is used to determine the objective function of the multi-objective collaborative optimization model based on the unit temperature difference thermogravimetric ratio.

[0154] The third determination module is used to determine the constraints of the multi-objective collaborative optimization model based on the heat transfer temperature difference and pressure drop.

[0155] The building module is used to construct multi-objective collaborative optimization models based on objective functions and constraints.

[0156] Optionally, in one embodiment of this application, the output module 100 includes: a first determination unit, a second determination unit, a third determination unit, and a fourth determination unit.

[0157] The first determining unit is used to determine the flow data in the performance coefficient data based on the process data, flow path data and flow channel configuration data in the operating data.

[0158] The second determining unit is used to determine the heat transfer temperature difference data in the performance coefficient data based on the structural dimension data in the operating data.

[0159] The third determining unit is used to determine the pressure drop data in the performance coefficient data based on the fluid data and heat exchange unit data in the operating data.

[0160] The fourth determining unit is used to determine the thermal stress data in the performance coefficient data based on the thermal fatigue performance data, material data, and fatigue data in the operating data.

[0161] Optionally, in one embodiment of this application, the first determining module 200 includes: an output unit, a fifth determining unit, and a sixth determining unit.

[0162] The output unit is used to input the flow rate, pressure, and temperature values ​​into the target model predictive controller to output the load change rate of the target heat exchanger.

[0163] The fifth determining unit is used to determine the bypass regulating command of the bypass valve in response to the load change rate of the target heat exchanger being greater than a preset threshold, so as to determine the opening adjustment value of the bypass valve according to the bypass regulating command.

[0164] The sixth determining unit is used to determine the adjustment command as the frequency conversion adjustment command of the frequency converter valve in response to the load change rate being less than or equal to a preset threshold, so as to determine the speed value of the variable pump of the frequency converter valve according to the frequency conversion adjustment command.

[0165] Optionally, in one embodiment of this application, the control module 300 includes a seventh determining unit and a generating unit.

[0166] The seventh determining unit is used to determine the initial performance degradation data and / or initial fault diagnosis results of the digital twin based on the flow rate, pressure, temperature and adjustment commands.

[0167] The generation unit is used to obtain performance degradation data and / or fault diagnosis results based on the initial performance degradation data and / or initial fault diagnosis results.

[0168] Optionally, in one embodiment of this application, the generation unit includes: a selection subunit and an output subunit.

[0169] The selection sub-unit is used to select a target machine learning model suitable for the digital twin based on the digital twin's historical and real-time data.

[0170] The output subunit is used to input flow rate, pressure, temperature and regulation commands into the target machine learning model to output initial performance degradation data and / or initial fault diagnosis results.

[0171] It should be noted that the foregoing explanation of the embodiment of the collaborative design and control method for heat exchangers also applies to the collaborative design and control device for heat exchangers in this embodiment, and will not be repeated here.

[0172] According to the collaborative design and control device for heat exchangers proposed in the embodiments of this application, the collected operating data of the target heat exchanger under at least one operating condition can be input into a pre-constructed multi-objective collaborative optimization model, thereby outputting performance coefficient data that meets preset performance conditions. After controlling the target heat exchanger to operate according to the performance coefficient data, the corresponding adjustment command is determined based on the flow rate, pressure, and temperature values ​​of the target heat exchanger in the current state. Then, combined with the digital twin, the performance degradation data and / or fault diagnosis results of the target heat exchanger are determined to generate optimization commands for the target heat exchanger and control the target heat exchanger, ensuring the dynamic adaptability of the heat exchanger throughout its entire life cycle from birth to operation. This ensures that the heat exchanger can maintain high heat transfer efficiency throughout the entire range from low load to overload, greatly improving equipment reliability and lifespan, significantly improving the system's annual average energy efficiency, and realizing a leap from "passive maintenance" to "proactive operation and maintenance". This solves the problems in related technologies, such as the tendency for fluid flow rate and velocity to change and thus deteriorate heat transfer and efficiency when the load deviates from the design point under variable load conditions, and the vibration caused by uneven flow. In addition, the temperature control accuracy is poor, and the material properties and temperature pinch points are also prone to occur in supercritical working fluids. Furthermore, the post-adjustment does not take into account the adaptability of variable load, making it difficult to achieve optimal performance, resulting in slow response and safety hazards.

[0173] Figure 9 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. The electronic device may include: The memory 901, the processor 902, and the computer program stored on the memory 901 and capable of running on the processor 902.

[0174] When the processor 902 executes the program, it implements the collaborative design and control method for heat exchangers provided in the above embodiments.

[0175] Furthermore, electronic devices also include: Communication interface 903 is used for communication between memory 901 and processor 902.

[0176] The memory 901 is used to store computer programs that can run on the processor 902.

[0177] The memory 901 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0178] If the memory 901, processor 902, and communication interface 903 are implemented independently, then the communication interface 903, memory 901, and processor 902 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0179] Optionally, in a specific implementation, if the memory 901, processor 902, and communication interface 903 are integrated on a single chip, then the memory 901, processor 902, and communication interface 903 can communicate with each other through an internal interface.

[0180] The processor 902 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0181] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described co-design and control method for heat exchangers.

[0182] This application also provides a computer program product, including a computer program that, when executed, implements the above-described collaborative design and control method for heat exchangers.

[0183] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0184] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0185] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0186] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting or otherwise processing as necessary, and then stored in computer memory.

[0187] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0188] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0189] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0190] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A collaborative design and control method for a heat exchanger, characterized in that, Includes the following steps: The operating data of the target heat exchanger under at least one operating condition is collected, and the operating data is input into a pre-constructed multi-objective collaborative optimization model to output the performance coefficient data of the target heat exchanger to meet the preset performance conditions. After controlling the target heat exchanger to operate according to the performance coefficient data, the flow rate, pressure and temperature values ​​of the target heat exchanger in the current state are collected, and the adjustment commands of the bypass valve and / or frequency converter valve in the target heat exchanger are determined based on the flow rate, pressure and temperature values. Based on the flow rate, pressure, temperature, adjustment command, and the digital twin corresponding to the target heat exchanger, determine the performance degradation data and / or fault diagnosis results of the target heat exchanger, generate optimization commands for the target heat exchanger according to the performance degradation data and / or fault diagnosis results, and control the target heat exchanger according to the optimization commands.

2. The method according to claim 1, characterized in that, Before inputting the runtime data into the pre-built multi-objective collaborative optimization model, the following steps are also included: Calculate the heat-to-gravity ratio per unit temperature difference of the target heat exchanger based on the heat-to-gravity ratio and heat transfer temperature difference of the target heat exchanger; Based on the unit temperature difference thermogravimetric ratio, the objective function of the multi-objective collaborative optimization model is determined; Based on the heat transfer temperature difference and pressure drop, the constraints of the multi-objective collaborative optimization model are determined. Based on the objective function and the constraints, the multi-objective collaborative optimization model is constructed.

3. The method according to claim 2, characterized in that, The step of inputting the operating data into a pre-constructed multi-objective collaborative optimization model to output performance coefficient data of the target heat exchanger satisfying preset performance conditions includes: Based on the process data, flow path data, and flow channel configuration data in the operational data, determine the flow rate data in the performance coefficient data; Based on the structural dimension data in the operational data, determine the heat transfer temperature difference data in the performance coefficient data; Based on the fluid data and heat exchange unit data in the operating data, the pressure drop data in the performance coefficient data is determined; Based on the thermal fatigue performance data, material data, and fatigue data in the operational data, the thermal stress data in the performance coefficient data is determined.

4. The method according to claim 1, characterized in that, The step of determining the adjustment command for the bypass valve and / or frequency converter valve in the target heat exchanger based on the flow rate value, the pressure value, and the temperature value includes: The flow rate, pressure, and temperature values ​​are input into the target model predictive controller to output the load change rate of the target heat exchanger; In response to the load change rate of the target heat exchanger being greater than a preset threshold, the adjustment command is determined to be a bypass adjustment command for the bypass valve, so as to determine the opening adjustment value of the bypass valve according to the bypass adjustment command; In response to the load change rate being less than or equal to the preset threshold, the adjustment command is determined to be the frequency conversion adjustment command of the frequency converter valve, so as to determine the speed value of the variable speed pump of the frequency converter valve according to the frequency conversion adjustment command.

5. The method according to claim 1, characterized in that, The determination of performance degradation data and / or fault diagnosis results of the target heat exchanger based on the flow rate value, pressure value, temperature value, adjustment command, and the digital twin corresponding to the target heat exchanger includes: Based on the flow rate value, the pressure value, the temperature value, and the adjustment command, determine the initial performance degradation data and / or initial fault diagnosis results of the digital twin; Based on the initial performance degradation data and / or the initial fault diagnosis results, the performance degradation data and / or the fault diagnosis results are obtained.

6. The method according to claim 5, characterized in that, The process of determining the initial performance degradation data and / or initial fault diagnosis results of the digital twin based on the flow rate value, the pressure value, the temperature value, and the adjustment command includes: Based on the historical and real-time data of the digital twin, a target machine learning model suitable for the digital twin is selected; The flow rate, pressure, temperature, and adjustment command are input into the target machine learning model to output the initial performance degradation data and / or the initial fault diagnosis results.

7. A collaborative design and control device for a heat exchanger, characterized in that, include: The output module is used to collect the operating data of the target heat exchanger under at least one operating condition, and input the operating data into a pre-constructed multi-objective collaborative optimization model to output the performance coefficient data of the target heat exchanger to meet the preset performance conditions. The first determining module is used to control the target heat exchanger to operate according to the performance coefficient data, collect the flow rate, pressure value and temperature value of the target heat exchanger in the current state, and determine the adjustment command of the bypass valve and / or frequency converter valve in the target heat exchanger based on the flow rate, pressure value and temperature value. The control module is used to determine the performance degradation data and / or fault diagnosis results of the target heat exchanger based on the flow rate value, the pressure value, the temperature value, the adjustment command, and the digital twin corresponding to the target heat exchanger, so as to generate optimization commands for the target heat exchanger according to the performance degradation data and / or the fault diagnosis results, and control the target heat exchanger according to the optimization commands.

8. The apparatus according to claim 7, characterized in that, Also includes: The calculation module is used to calculate the heat-to-gravity ratio per unit temperature difference of the target heat exchanger based on the heat transfer temperature difference and the heat transfer ratio of the target heat exchanger before inputting the running data into the pre-built multi-objective collaborative optimization model. The second determining module is used to determine the objective function of the multi-objective collaborative optimization model based on the unit temperature difference thermogravimetric ratio. The third determining module is used to determine the constraints of the multi-objective collaborative optimization model based on the heat transfer temperature difference and pressure drop. A construction module is used to construct the multi-objective collaborative optimization model based on the objective function and the constraints.

9. The apparatus according to claim 8, characterized in that, The output module includes: The first determining unit is used to determine the flow data in the performance coefficient data based on the process data, flow path data and flow channel configuration data in the operation data; The second determining unit is used to determine the heat transfer temperature difference data in the performance coefficient data based on the structural dimension data in the operating data; The third determining unit is used to determine the pressure drop data in the performance coefficient data based on the fluid data and heat exchange unit data in the operating data; The fourth determining unit is used to determine the thermal stress data in the performance coefficient data based on the thermal fatigue performance data, material data, and fatigue data in the operating data.

10. The apparatus according to claim 7, characterized in that, The first determining module includes: The output unit is used to input the flow rate, pressure value and temperature value into the target model predictive controller to output the load change rate of the target heat exchanger; The fifth determining unit is used to determine the adjustment command as a bypass adjustment command of the bypass valve in response to the load change rate of the target heat exchanger being greater than a preset threshold, so as to determine the opening adjustment value of the bypass valve according to the bypass adjustment command; The sixth determining unit is used to determine the adjustment command as the frequency conversion adjustment command of the frequency converter valve in response to the load change rate being less than or equal to the preset threshold, so as to determine the speed value of the variable speed pump of the frequency converter valve according to the frequency conversion adjustment command.

11. The apparatus according to claim 7, characterized in that, The control module includes: The seventh determining unit is used to determine the initial performance degradation data and / or initial fault diagnosis results of the digital twin based on the flow rate value, the pressure value, the temperature value, and the adjustment command; A generation unit is used to obtain the performance degradation data and / or the fault diagnosis results based on the initial performance degradation data and / or the initial fault diagnosis results.

12. The apparatus according to claim 11, characterized in that, The generation unit includes: The selection subunit is used to select a target machine learning model suitable for the digital twin based on the historical and real-time data of the digital twin. The output subunit is used to input the flow rate value, the pressure value, the temperature value, and the adjustment command into the target machine learning model to output the initial performance degradation data and / or the initial fault diagnosis result.

13. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, the processor executing the program to implement the co-design and control method for a heat exchanger as described in any one of claims 1-6.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the co-design and control method for the heat exchanger as described in any one of claims 1-6.

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