Automated intelligent optimization method for active thermal control simulation of mechanical refrigerators

Abstract

The application relates to the technical field of space mechanical refrigerator control, and provides an automatic intelligent optimization method for active temperature control simulation of a mechanical refrigerator. The electromagnetic simulation software, the mathematical simulation software and the multidisciplinary optimization software are organically combined to construct a multi-physical-domain joint simulation accelerated operation and automatic control optimization method suitable for an active temperature control system of a mechanical refrigerator. The method forms a complete set of intelligent workflow of multidisciplinary integration, has the characteristics of one-time deployment and reusability, and effectively improves the efficiency and convenience of system design, verification and optimization. Through the automatic optimization process, the application can obtain key optimal control parameters, provides a quick optimization and correction direction for subsequent software design of a control algorithm, and thus significantly improves the overall performance and engineering application value of the temperature control system of the mechanical refrigerator.

Description

Technical Field

[0001] This application relates to the field of space mechanical refrigeration control technology, and in particular to an automated intelligent optimization method for active temperature control simulation of mechanical refrigeration machines. Background Technology

[0002] High-performance space infrared detector technology and cryogenic cooling technology often advance each other, with space mechanical refrigerators equipped with active temperature control systems being one of the key cryogenic cooling methods. In recent years, as space missions have become increasingly sophisticated in astronomical observation, deep space exploration, and high-precision scientific experiments, the requirements for suppressing temperature fluctuations in the operating environments of core components of payloads such as infrared detectors have become increasingly stringent, correspondingly placing higher standards on the temperature stability of space refrigerator outputs. Therefore, it is necessary to promote end-to-end simulation optimization of the hardware and software design and integration technologies in the active temperature control system of mechanical refrigerators.

[0003] The simulation of temperature control systems for mechanical refrigeration units requires a multidisciplinary simulation platform capable of characterizing thermodynamic changes, compressor electromagnetic drive characteristics, control circuits, and control strategy logic. Existing simulation technologies largely focus on separate modeling research within each discipline, and interdisciplinary collaborative simulations often suffer from difficulties in data communication, inconvenient model parameter adjustment, and low simulation efficiency, failing to meet the demands for refined simulation of active temperature control systems for refrigeration units.

[0004] Therefore, there is an urgent need for an efficient automated intelligent optimization method for active temperature control simulation systems of mechanical refrigeration machines. Summary of the Invention

[0005] In view of this, embodiments of this application provide an automated intelligent optimization method for active temperature control simulation of mechanical refrigeration machines, in order to solve the problems of low efficiency and poor convenience in the fine simulation of active temperature control systems for refrigeration machines in the prior art.

[0006] A first aspect of this application provides an automated intelligent optimization method for active temperature control simulation of a mechanical refrigeration machine, comprising:

[0007] Construct an electronic model of the mechanical refrigeration machine in an electromagnetic simulation software platform; the electronic model includes at least an electromagnetic calculation model of the compressor and a drive circuit model;

[0008] The accelerated calculation workflow for building the drive circuit model on the optimized software platform is used to obtain the accelerated calculation file; the accelerated calculation file should at least include the mapping relationship between the compressor input signal amplitude and the output power.

[0009] A closed-loop temperature control strategy model is built in mathematical simulation software; the closed-loop temperature control strategy model includes at least the transfer function model of the mechanical refrigeration machine, and the functional fitting relationship between the amplitude of the compressor input signal and the average output power.

[0010] The optimization software platform constructs a parameter optimization workflow, runs the parameter optimization workflow to determine the target performance indicators of the mechanical refrigeration machine, and determines the evaluation indicators based on the target performance indicators.

[0011] A second aspect of this application provides an automated intelligent optimization device for active temperature control simulation of a mechanical refrigeration unit, comprising:

[0012] The simulation module is configured to build an electronic model of the mechanical refrigeration machine in an electromagnetic simulation software platform; the electronic model includes at least an electromagnetic calculation model of the compressor and a drive circuit model;

[0013] The optimization module is configured to build an accelerated calculation workflow for the drive circuit model on the optimization software platform, resulting in an accelerated calculation file; the accelerated calculation file includes at least the mapping relationship between the compressor input signal amplitude and the output power.

[0014] The calculation module is configured to build a closed-loop temperature control strategy model in mathematical simulation software; the closed-loop temperature control strategy model includes at least the transfer function model of the mechanical refrigeration machine, and the functional fitting relationship of the mapping relationship between the amplitude of the compressor input signal and the average output power;

[0015] The optimization module is also configured to build a parameter optimization workflow on the optimization software platform, run the parameter optimization workflow to determine the target performance indicators of the mechanical refrigeration unit, and determine the evaluation indicators based on the target performance indicators.

[0016] 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 computer program to implement the steps of the above-described method.

[0017] The beneficial effects of the embodiments in this application compared with the prior art are:

[0018] This application integrates electromagnetic simulation software, mathematical simulation software, and multidisciplinary optimization software to construct a multi-physics domain co-simulation accelerated operation and automated control optimization method suitable for active temperature control systems of mechanical refrigeration machines. This method forms a complete intelligent workflow integrating multiple disciplines, featuring one-time deployment and reusability, effectively improving the efficiency and convenience of system design, verification, and optimization. Through an automated optimization process, this application can obtain key optimal control parameters, providing rapid optimization and correction directions for subsequent software design of control algorithms, thereby significantly improving the overall performance and engineering application value of the refrigeration machine temperature control system. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating an automated intelligent optimization method for active temperature control simulation of a mechanical refrigeration unit, provided in an embodiment of this application.

[0021] Figure 2 This is a flowchart illustrating the method for accelerating computation workflow in building a driver circuit model on an optimized software platform, as provided in an embodiment of this application.

[0022] Figure 3 This is a schematic diagram of a method for using optimization software to run an automated optimization workflow.

[0023] Figure 4 This is a flowchart illustrating another automated intelligent optimization method for active temperature control simulation of a mechanical refrigeration unit provided in this application embodiment.

[0024] Figure 5 This is a schematic diagram of an automated intelligent optimization device for active temperature control simulation of a mechanical refrigeration unit, provided in an embodiment of this application.

[0025] Figure 6 This is a schematic diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0026] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0027] The following will describe in detail, with reference to the accompanying drawings, an automated intelligent optimization method and apparatus for active temperature control simulation of a mechanical refrigeration unit according to an embodiment of this application.

[0028] As mentioned above, the simulation of a mechanical refrigeration unit's temperature control system requires a multidisciplinary simulation platform capable of characterizing thermodynamic changes, compressor electromagnetic drive characteristics, control circuits, and control strategy logic. Existing simulation technologies mostly focus on separate modeling research within each discipline, and interdisciplinary collaborative simulations often suffer from difficulties in data communication, inconvenient model parameter adjustment, and low simulation efficiency, failing to meet the demands for refined simulation of active temperature control systems for refrigeration units.

[0029] Therefore, there is an urgent need for an efficient automated intelligent optimization method for active temperature control simulation systems of mechanical refrigeration machines.

[0030] In view of this, this application provides an automated intelligent optimization method for active temperature control simulation of mechanical refrigeration machines. It organically combines electromagnetic simulation software, mathematical simulation software, and multidisciplinary optimization software to construct a multi-physics domain co-simulation accelerated operation and automated control optimization method suitable for active temperature control systems of mechanical refrigeration machines. This method forms a complete set of multidisciplinary integrated intelligent workflows, featuring one-time deployment and reusability, effectively improving the efficiency and convenience of system design, verification, and optimization. Through the automated optimization process, this application can obtain key optimal control parameters, providing rapid optimization and correction directions for subsequent software design of control algorithms, thereby significantly improving the overall performance and engineering application value of the refrigeration machine temperature control system.

[0031] Figure 1 This is a flowchart illustrating an automated intelligent optimization method for active temperature control simulation of a mechanical refrigeration unit, provided in an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0032] In step S101, an electronic model of the mechanical refrigeration machine is constructed in the electromagnetic simulation software platform.

[0033] The electronic model includes at least a compressor electromagnetic calculation model and a drive circuit model.

[0034] In step S102, an accelerated computation workflow for the driving circuit model is built on the optimization software platform to obtain the accelerated computation file.

[0035] The accelerated calculation file includes at least the mapping relationship between the compressor input signal amplitude and the output power.

[0036] In step S103, a closed-loop temperature control strategy model is built in a mathematical simulation software platform.

[0037] The closed-loop temperature control strategy model includes at least the transfer function model of the mechanical refrigeration machine and the function fitting relationship between the amplitude of the compressor input signal and the average output power.

[0038] In step S104, a parameter optimization workflow is constructed on the optimization software platform, the parameter optimization workflow is run to determine the target performance index of the mechanical refrigeration machine, and the evaluation index is determined based on the target performance index.

[0039] In some embodiments of this application, the method may be executed by a server or by a terminal device with certain processing capabilities.

[0040] In some embodiments of this application, an electronic model of a mechanical refrigeration machine can be constructed in an electromagnetic simulation software platform. This electronic model includes at least a compressor electromagnetic calculation model and a drive circuit model.

[0041] Meanwhile, an accelerated calculation workflow for the drive circuit model can be built on the optimized software platform to obtain an accelerated calculation file, which at least includes the mapping relationship between the compressor input signal amplitude and the output power.

[0042] In some embodiments of this application, a closed-loop temperature control strategy model can also be built in a mathematical simulation software platform. The closed-loop temperature control strategy model includes at least the transfer function model of the mechanical refrigeration machine and the functional fitting formula of the mapping relationship between the amplitude of the compressor input signal and the average output power.

[0043] Finally, a parameter optimization workflow can be built on the optimization software platform, the parameter optimization workflow can be run to determine the target performance indicators of the mechanical refrigeration machine, and the evaluation indicators can be determined based on the target performance indicators.

[0044] According to the technical solution provided in this application, electromagnetic simulation software, mathematical simulation software, and multidisciplinary optimization software are organically combined to construct a multi-physics domain co-simulation accelerated operation and automated control optimization method suitable for active temperature control systems of mechanical refrigeration machines. This method forms a complete set of multidisciplinary integrated intelligent workflows, featuring one-time deployment and reusability, effectively improving the efficiency and convenience of system design, verification, and optimization. Through an automated optimization process, this application embodiment can obtain key optimal control parameters, providing rapid optimization and correction directions for subsequent software design of control algorithms, thereby significantly improving the overall performance and engineering application value of the refrigeration machine temperature control system.

[0045] The construction of the electronic model of the mechanical refrigeration machine in the electromagnetic simulation software platform may include: completing the construction and parameter setting of the mechanical refrigeration machine circuit components in the electromagnetic simulation software platform; the mechanical refrigeration machine circuit components include at least a sinusoidal pulse width modulation (SPWM) waveform generator and a full-bridge inverter; importing the compressor electromagnetic calculation model into the electromagnetic simulation software platform; setting simulation parameters; the simulation parameters include at least simulation solution compensation and total simulation time; performing a single simulation operation to obtain the settling time; the settling time is the time it takes for the compressor output power to reach a steady state.

[0046] In other words, the electronic model of the mechanical refrigeration machine can be constructed in an electromagnetic simulation software platform (ANSYS ElectronicsDesktop can be used in this embodiment).

[0047] Taking a pulse-type refrigerator as an example, the electronic model of a mechanical refrigerator can include the electronic model of the drive circuit. This electronic model can include a 60 Hz sinusoidal signal with adjustable amplitude and a sinusoidal pulse width modulation (SPWM) wave generator constructed by the pwm24eu1 module. In order to achieve real-time amplitude control, the amplitude of this module needs to be set to be controlled by an external signal.

[0048] The circuit structure of the inverter section can adopt a full-bridge inverter built based on the driver chip, while setting the physical parameters of each component.

[0049] In some embodiments of this application, the electronic model of the mechanical refrigeration machine may also include an electromagnetic calculation model of the compressor. After constructing the electromagnetic calculation model of the compressor, the compressor load characteristics under different driving voltages can be calculated first. The calculation results are saved as an equivalent circuit data file that can be called by the drive circuit module, and connected to the output terminal of the drive circuit in the form of a simulation model (SimulatorModel, SML) module file that reflects the load characteristics. During the simulation, the compressor operating state is controlled by changing the excitation voltage.

[0050] In some embodiments of this application, parameters such as the maximum step size, minimum step size, and total simulation time can be set before the calculation.

[0051] Because the drive circuit contains high-frequency metal-oxide-semiconductor (MOSFET) switching devices, and a high carrier frequency is typically required to obtain low-harmonic, high-quality load-side output voltage waveforms, the system cannot accelerate calculations by increasing the simulation step size during simulation. Forcibly increasing the step size will result in significant distortion of the output waveform, compromising the accuracy of the simulation.

[0052] Therefore, embodiments of this application can use an automated workflow to accelerate simulation. Prior to this, a single simulation operation is required to obtain the time it takes for the compressor output power to reach steady state, because significant transient disturbances exist in the circuit during the initial startup phase of the compressor load. After the simulation is complete, the compressor's current and voltage curves can be saved.

[0053] In some embodiments of this application, the accelerated computation workflow for building a driving circuit model on an optimized software platform to obtain an accelerated computation file may include: setting the scanning range and parameter distribution form of the input signal amplitude on the optimized software platform; recording the steps of starting the electromagnetic simulation software, modifying the input signal amplitude, and saving the output results on the electromagnetic simulation software platform, and saving the recorded results as a single simulation script file; importing a pre-written steady-state power extraction program into the accelerated computation workflow, and combining it with the single simulation script file to obtain the accelerated computation file; the accelerated computation file includes the mapping relationship between the corresponding parameters of the input signal amplitude and the calculated output average power under a single simulation; setting automatic optimization experimental parameters; the automatic optimization experimental parameters include at least the iteration step size and the point-scattering method.

[0054] Figure 2 This is a flowchart illustrating a method for accelerating computational workflows in building driver circuit models on an optimized software platform, as provided in an embodiment of this application. Figure 2As shown, an accelerated computational workflow for driving circuit models can be built on an optimization software platform. In one example, the optimization software could be Optimus.

[0055] When optimizing the software platform to build an accelerated computation workflow for driving circuit models, it is first necessary to set the scanning range of the input signal amplitude parameters and the parameter distribution form.

[0056] Next, a simulation script file for electromagnetic simulation software needs to be recorded. This script file includes starting the AEDT simulation software, modifying the amplitude parameters of the input sinusoidal signal, starting the transient simulation analysis, and saving the output compressor current and voltage curves. This simulation script file can be imported as an input file into the workflow.

[0057] Simultaneously, a power extraction program for the compressor's steady-state operation phase needs to be developed. This program runs on mathematical simulation software, and its main function is to calculate the average power from the compressor's current and voltage curve data at the steady-state time point, and write the calculation results into an accelerated calculation file, which can be a .txt file.

[0058] Finally, the accelerated calculation file can be imported into the accelerated calculation workflow of the driver circuit model, and the self-start and self-shutdown commands after the extraction program are written. The mapping relationship between the corresponding parameters of the input signal amplitude and the output average power calculation results under a single simulation is established.

[0059] In some embodiments of this application, automatic experimental design can also be performed before starting the automated running program in the optimized software platform. The iteration step size is set and the calculation of a specific scattering method within the input parameter scanning range is performed. The result of each iteration calculation is exported as a .csv format file. The accelerated calculation workflow of the driving circuit model automatically ends after traversing all parameters.

[0060] The specific point-scattering method can be any one of Latin hypercube, adaptive design, or random design. Alternatively, since the technical solution of this application embodiment only needs to traverse the values ​​within the range of input parameters, the specific point-scattering method can also be selected as step-by-step.

[0061] In some embodiments of this application, building a closed-loop temperature control strategy in mathematical simulation software may include: obtaining a transfer function model of the mechanical refrigeration machine based on a step test; fitting a function relationship between the amplitude of the compressor input signal and the average output power to obtain a function fitting expression; and building the closed-loop control algorithm logic. In one example, the mathematical simulation software may be Simulink in Matlab.

[0062] The closed-loop control algorithm logic may include: taking the transfer function model as the controlled object, and obtaining the output power value of the current calculation by fitting the algorithm's calculation result again through the function fitting relationship; determining the cooling amount of the current calculation based on the output power value; subtracting the cooling amount of the current calculation from the initial temperature value to obtain the current temperature value; and using the difference between the current temperature value and the set temperature control point value as the logical input of the control algorithm to perform the next iteration calculation.

[0063] Furthermore, building a closed-loop temperature control strategy model in mathematical simulation software may also include: setting the control parameters of the closed-loop control algorithm to a modifiable mode; and setting an evaluation index output model.

[0064] In other words, a complete closed-loop temperature control strategy model can be built in Matlab / Simulink.

[0065] First, it is necessary to design a system identification experiment, such as a step test, to obtain the transfer function model of the mechanical refrigeration machine, thereby reflecting the mathematical relationship between the input power and the cooling amount of the mechanical refrigeration machine, and to use this model as the core controlled object in the control system.

[0066] Next, the input amplitude parameters and corresponding output power mapping relationships obtained from the aforementioned workflow need to be fitted using a functional relationship. The fitted relationship can be written using S-Function and inserted into the Simulink module.

[0067] Next, the closed-loop control algorithm logic is built, with the transfer function model as the controlled object of the algorithm. Each calculation result of the algorithm needs to be fed into the transfer function model through the fitting relation, with the output power value. Then, the transfer function displays the system response (cooling amount). The cooling amount is subtracted from the initial value to form the current temperature value. This temperature value is compared with the set temperature control point value, and the difference is used as the input of the control algorithm logic again, so that the algorithm can perform the next iteration calculation.

[0068] In some embodiments of this application, determining the target performance index of a mechanical refrigeration machine by running a parameter optimization workflow may include: writing an executable file for the parameter optimization workflow; the executable file includes at least the following: reading an accelerated calculation file, setting the model running time and step size, reading the temperature output curve, and calculating the target performance index; setting a control parameter list in the optimization software platform, and creating a new output parameter file in the project file directory of the optimization software platform; the output parameter file can be read by the optimization software and mapped and connected with the parameters in the control parameter list; importing the executable file of the parameter optimization workflow and the closed-loop temperature control strategy model into the parameter optimization workflow, and running the parameter optimization workflow to determine the target performance index of the mechanical refrigeration machine.

[0069] The process of determining evaluation indicators based on target performance indicators may include: determining pre-evaluation indicators based on the target performance indicators of the mechanical refrigeration unit; establishing a single-objective optimization algorithm to optimize the control parameters by targeting the minimum value among the pre-evaluation indicators; inputting the optimized control parameters into the closed-loop temperature control strategy model and determining the optimization result based on the running results of the closed-loop temperature control strategy model.

[0070] In some embodiments of this application, since there are many types of closed-loop temperature control algorithms, but all of them require the debugging of control parameters, it is necessary to set the necessary control parameters of the algorithm to a modifiable mode in advance. Their values ​​are set as the names of the control parameters in the module, so that .m format files can be written externally to directly assign and modify the control parameters in the model. For subsequent optimization work, the cooling curve results from the simulation run need to be output. The .m format file is used to calculate relevant performance parameters such as overshoot, response speed, and steady-state error, and output as a .txt format file, which automatically closes upon completion.

[0071] Figure 3 This is a schematic diagram illustrating a method for using optimization software to run an automated optimization workflow. For example... Figure 3 As shown, the control parameter list can be set first in the workflow software, including the range and distribution of the parameters; then, a control parameter .txt file can be created in the project file directory, which is read by the workflow software and mapped to the parameter names in the control parameter list; then, the external operation .m file and the closed-loop control Simulink model file are put into the workflow, and commands to start and close the Matlab software window after the simulation ends are inserted. The various performance parameters after the simulation ends are also mapped to the .txt file, and the workflow reads the various performance indicators and calculates the final evaluation index according to the weight ratio of the interests.

[0072] Once the entire optimization workflow is established, a single-objective optimization algorithm with the goal of minimizing the final evaluation index is created. Key algorithm parameters such as the maximum total number of iterations, termination conditions, and iteration step size are set, and the algorithm can start running automatically. After the optimization reaches the termination condition or the maximum number of iterations, the optimal control parameter values ​​obtained from the optimization are substituted into the original control model, and the optimization results are run and observed.

[0073] The termination conditions can be set in Optimus according to the different types of single-objective optimization algorithms selected. For example, reaching the maximum number of iterations, or setting a termination threshold in a single-objective particle swarm optimization algorithm.

[0074] In other words, in Optimus's automated workflow, the input parameter list is the control parameter list of the temperature control algorithm, which is continuously and automatically updated by Optimus (based on different single-objective optimization algorithms). Then, Matlab is started, the "Simulation External Operations.m file" is run, the updated control parameters are assigned to the control parameter module in "Closed-Loop Temperature Control.slx", and the Simulink model is run. The "Simulation External Operations.m file" captures the cooling curve and calculates the performance parameters, which are then imported back into Optimus. Optimus then summarizes and calculates the evaluation indicators.

[0075] In the workflow, the .txt format files are all intermediaries for storing parameters. In fact, Optimus has an internal interface that can connect with many software such as Ansys and Matlab to obtain simulation data, but using the .txt format file here is simpler, more convenient, and more customizable.

[0076] Figure 4 This is a flowchart illustrating another automated intelligent optimization method for active temperature control simulation of a mechanical refrigeration unit provided in this application embodiment. Figure 4 As shown, the method may include the following steps:

[0077] Step 1: Modeling the electronic control system of the mechanical refrigeration unit;

[0078] Step 2: Setting up the workflow for accelerating computation of the driving model;

[0079] Step 3: Building a closed-loop temperature control strategy model;

[0080] Step 4: Construct a workflow for optimizing control parameters.

[0081] Preferably, in step 1, the modeling of the mechanical refrigeration machine control electronics system includes the following steps:

[0082] S11. Complete the construction and parameter setting of related circuit devices such as SPWM waveform generator and full-bridge inverter in the modeling software;

[0083] S12. Import the compressor electromagnetic calculation model into the modeling software;

[0084] S13. Set parameters such as simulation solution step size and total time;

[0085] S14. Perform a single simulation to obtain the information on the time point when the output power reaches steady state.

[0086] Preferably, in step 2, the workflow for accelerating computation of the driving model includes the following steps:

[0087] S21. Set the scanning range and parameter distribution form of the input amplitude parameters;

[0088] S22. Record a script file in the driving circuit modeling software to perform steps such as starting the simulation software, modifying the input amplitude parameters, and saving the output results, and import it as an input file in the workflow.

[0089] S23. Write a power extraction program for the steady-state stage, import it into the workflow, write the self-start and self-shutdown commands for the extraction program after it finishes running, and establish a mapping relationship between the simulation results and the corresponding parameters in the power extraction program.

[0090] S24. Perform automatic experimental design, set the iteration step size and calculate the specific scattering method within the input parameter scanning range, and export the results of each iteration calculation as a .csv format file.

[0091] Preferably, in step 3, the closed-loop temperature control strategy model is built, including the following steps:

[0092] S31. Obtain the transfer function model of the controlled object of the refrigerator based on step test;

[0093] S32. Fit the mapping relationship between the input amplitude parameter and the corresponding output power using a functional relationship;

[0094] S33. Build the closed-loop control algorithm module logic, take the transfer function mathematical model as the controlled object of the algorithm, and the calculation result of each algorithm needs to be fed into the transfer function model with the output power value through S32.

[0095] S34. The control parameters of the control algorithm must be set to a modifiable mode, that is, the control parameters in the model can be directly modified by the externally written MATLAB file.

[0096] S35. Set the evaluation index output module.

[0097] Preferably, in step 4, the workflow for optimizing control parameters is constructed, including the following steps:

[0098] S41. Write an .m format file containing the following: read the output parameters generated by the workflow from the .txt format file, assign the numerical amplitude of the output parameters to the corresponding control parameters, set the model running time and step size, read the temperature output curve, calculate various performance indicators and output them to the .txt format file, and automatically close the file upon completion of the run.

[0099] S42. Set the control parameter list in the workflow software, and create a new output parameter.txt file in the project file directory. The workflow software will read the output parameter.txt file and map and connect it with the parameters in the control parameter list.

[0100] S43. Put the .m format file written in S41 and the closed-loop control model in step 3 into the workflow;

[0101] S44. Read various performance indicators from the workflow and calculate the final evaluation indicators according to their proportions;

[0102] S45. Establish a single-objective optimization algorithm, and execute the optimization algorithm with the minimum value of the final evaluation index in S44 as the objective.

[0103] S46. Substitute the control parameter values ​​after the optimization process into the original control model from step 3, run the model, and observe the optimization effect.

[0104] The technical solution provided in this application combines multidisciplinary optimization software to achieve automated control parameter optimization, thereby obtaining the optimal simulation model. This solves the problems of difficult data communication, low simulation efficiency, and poor simulation results in current interdisciplinary simulations.

[0105] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0106] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0107] Figure 5 This is a schematic diagram of an automated intelligent optimization device for active temperature control simulation of a mechanical refrigeration unit, provided in an embodiment of this application. Figure 5 As shown, the device includes:

[0108] Simulation module 501 is configured to construct an electronic model of a mechanical refrigeration machine in an electromagnetic simulation software platform; the electronic model includes at least an electromagnetic calculation model of the compressor and a drive circuit model.

[0109] The optimization module 502 is configured to build an accelerated calculation workflow for the drive circuit model on the optimization software platform to obtain an accelerated calculation file; the accelerated calculation file includes at least the mapping relationship between the compressor input signal amplitude and the output power.

[0110] The calculation module 503 is configured to build a closed-loop temperature control strategy model in mathematical simulation software; the closed-loop temperature control strategy model includes at least the transfer function model of the mechanical refrigeration machine and the function fitting relationship of the mapping relationship between the amplitude of the compressor input signal and the average output power.

[0111] The optimization module 502 is also configured to build a parameter optimization workflow on the optimization software platform, run the parameter optimization workflow to determine the target performance index of the mechanical refrigeration unit, and determine the evaluation index based on the target performance index.

[0112] According to the technical solution provided in this application, electromagnetic simulation software, mathematical simulation software, and multidisciplinary optimization software are organically combined to construct a multi-physics domain co-simulation accelerated operation and automated control optimization method suitable for active temperature control systems of mechanical refrigeration machines. This method forms a complete set of multidisciplinary integrated intelligent workflows, featuring one-time deployment and reusability, effectively improving the efficiency and convenience of system design, verification, and optimization. Through an automated optimization process, this application embodiment can obtain key optimal control parameters, providing rapid optimization and correction directions for subsequent software design of control algorithms, thereby significantly improving the overall performance and engineering application value of the refrigeration machine temperature control system.

[0113] In some implementations, the electronic model of the mechanical refrigerator is constructed in an electromagnetic simulation software platform, including: constructing and setting the parameters of the mechanical refrigerator circuit components in the electromagnetic simulation software platform; the mechanical refrigerator circuit components include at least a sinusoidal pulse width modulation (SPWM) waveform generator and a full-bridge inverter; importing the compressor electromagnetic calculation model into the electromagnetic simulation software platform; setting simulation parameters; the simulation parameters include at least simulation solution compensation and total simulation time; performing a single simulation operation to obtain the settling time; the settling time is the time it takes for the compressor output power to reach a steady state.

[0114] In some implementations, an accelerated computation workflow for building a driver circuit model on an optimized software platform is used to obtain an accelerated computation file. This includes: setting the scanning range and parameter distribution of the input signal amplitude on the optimized software platform; recording the steps of starting the electromagnetic simulation software, modifying the input signal amplitude, and saving the output results on the electromagnetic simulation software platform, and saving the recorded results as a single simulation script file; importing a pre-written steady-state power extraction program into the accelerated computation workflow, and combining it with the single simulation script file to obtain the accelerated computation file; the accelerated computation file includes the mapping relationship between the corresponding parameters of the input signal amplitude and the calculated output average power under a single simulation; setting automatic optimization experimental parameters; the automatic optimization experimental parameters include at least the iteration step size and the point-scattering method.

[0115] In some implementations, an accelerated computation workflow for building a driver circuit model on an optimized software platform is used to obtain an accelerated computation file. This includes: setting the scanning range and parameter distribution of the input signal amplitude on the optimized software platform; recording the steps of starting the electromagnetic simulation software, modifying the input signal amplitude, and saving the output results on the electromagnetic simulation software platform, and saving the recorded results as a single simulation script file; importing a pre-written steady-state power extraction program into the accelerated computation workflow, and combining it with the single simulation script file to obtain the accelerated computation file; the accelerated computation file includes the mapping relationship between the corresponding parameters of the input signal amplitude and the calculated output average power under a single simulation; setting automatic optimization experimental parameters; the automatic optimization experimental parameters include at least the iteration step size and the point-scattering method.

[0116] In some implementations, the closed-loop control algorithm logic includes: taking the transfer function model as the controlled object, and obtaining the output power value of the current calculation by fitting the algorithm's calculation result again with the function fitting relationship; determining the cooling amount of the current calculation based on the output power value; subtracting the cooling amount of the current calculation from the initial temperature value to obtain the current temperature value; and using the difference between the current temperature value and the set temperature control point value as the logical input of the control algorithm to perform the next iteration calculation.

[0117] In some implementations, building a closed-loop temperature control strategy model in mathematical simulation software also includes: setting the control parameters of the closed-loop control algorithm to a modifiable mode; and setting an evaluation index output model.

[0118] In some implementations, the parameter optimization workflow determines the target performance indicators of the mechanical chiller, including: writing an executable file for the parameter optimization workflow; the executable file includes at least the following: reading the accelerated calculation file, setting the model running time and step size, reading the temperature output curve, and calculating the target performance indicators; setting a control parameter list in the optimization software platform, and creating a new output parameter file in the project file directory of the optimization software platform; the output parameter file can be read by the optimization software and mapped and connected to the parameters in the control parameter list; importing the executable file of the parameter optimization workflow and the closed-loop temperature control strategy model into the parameter optimization workflow, and running the parameter optimization workflow to determine the target performance indicators of the mechanical chiller.

[0119] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0120] Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of this application. Figure 6 As shown, the electronic device 6 of this embodiment includes a processor 601, a memory 602, and a computer program 603 stored in the memory 602 and executable on the processor 601. When the processor 601 executes the computer program 603, it implements the steps in the various method embodiments described above. Alternatively, when the processor 601 executes the computer program 603, it implements the functions of each module / unit in the various device embodiments described above.

[0121] Electronic device 6 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 6 may include, but is not limited to, processor 601 and memory 602. Those skilled in the art will understand that... Figure 6 This is merely an example of electronic device 6 and does not constitute a limitation on electronic device 6. It may include more or fewer components than shown, or different components.

[0122] The processor 601 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0123] The memory 602 can be an internal storage unit of the electronic device 6, such as a hard disk or RAM of the electronic device 6. The memory 602 can also be an external storage device of the electronic device 6, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., equipped on the electronic device 6. The memory 602 can also include both internal and external storage units of the electronic device 6. The memory 602 is used to store computer programs and other programs and data required by the electronic device.

[0124] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0125] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0126] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An automated intelligent optimization method for active temperature control simulation of mechanical refrigeration machines, characterized in that, include: An electronic model of a mechanical refrigeration machine was constructed in an electromagnetic simulation software platform; The electronic model includes at least a compressor electromagnetic calculation model and a drive circuit model; An accelerated computation workflow for the drive circuit model is built on an optimized software platform to obtain an accelerated computation file; the accelerated computation file includes at least the mapping relationship between the compressor input signal amplitude and the average output power. A closed-loop temperature control strategy model is built in a mathematical simulation software platform; the closed-loop temperature control strategy model includes at least the transfer function model of the mechanical refrigeration machine, and the function fitting relationship of the mapping relationship between the amplitude of the compressor input signal and the average output power; A parameter optimization workflow is constructed on the optimization software platform, the parameter optimization workflow is run to determine the target performance index of the mechanical refrigeration machine, and the evaluation index is determined based on the target performance index. The accelerated computation workflow for building the driving circuit model on the optimized software platform yields accelerated computation files, including: The scanning range and parameter distribution of the input signal amplitude are set in the optimization software platform; Record the steps of starting the electromagnetic simulation software, modifying the input signal amplitude, and saving the output results on the electromagnetic simulation software platform, and save the recording results as a single simulation script file; The pre-written steady-state power extraction program is imported into the accelerated computation workflow, and combined with the single simulation script file to obtain the accelerated computation file; the accelerated computation file includes the mapping relationship between the corresponding parameters of the input signal amplitude and the output average power calculation results under a single simulation; the steady-state power extraction program runs on mathematical simulation software; Set the automatic optimization experiment parameters; the automatic optimization experiment parameters include at least the iteration step size and the point-scattering method; The parameter optimization workflow is run to determine the target performance indicators of the mechanical refrigeration unit, including: Write an executable file for the parameter optimization workflow; the executable file shall include at least the following contents: reading the accelerated calculation file, setting the model running time and step size, reading the temperature output curve, and calculating the target performance index; A list of control parameters is set in the optimization software platform, and an output parameter file is created in the project file directory of the optimization software platform; the output parameter file is read by the optimization software and mapped and connected to the parameters in the control parameter list. Import the executable file of the parameter optimization workflow and the closed-loop temperature control strategy model into the parameter optimization workflow, and run the parameter optimization workflow to determine the target performance indicators of the mechanical refrigeration machine. Evaluation indicators are determined based on the target performance indicators, including: Pre-evaluation indicators are determined based on the target performance indicators of the mechanical refrigeration machine; A single-objective optimization algorithm is established to optimize the control parameters by targeting the minimum value among the pre-evaluation indicators. The optimized control parameters are input into the closed-loop temperature control strategy model, and the optimization result is determined based on the running results of the closed-loop temperature control strategy model.

2. The method according to claim 1, characterized in that, The electronic model of the mechanical refrigeration machine was constructed in the electromagnetic simulation software platform, including: The mechanical refrigeration machine circuit components are constructed and their parameters are set in an electromagnetic simulation software platform; the mechanical refrigeration machine circuit components include at least a sinusoidal pulse width modulation (SPWM) waveform generator and a full-bridge inverter. Import the electromagnetic calculation model of the compressor into the electromagnetic simulation software platform; Set simulation parameters; the simulation parameters include at least simulation solution compensation and total simulation time; A single simulation operation is performed to obtain the settling time; the settling time is the time it takes for the compressor output power to reach a steady state.

3. The method according to claim 1, characterized in that, A closed-loop temperature control strategy model is built in mathematical simulation software, including: The transfer function model of the mechanical refrigeration machine was obtained based on the step test. The mapping relationship between the amplitude of the compressor input signal and the average output power is fitted with a function to obtain the function fitting formula; Build the closed-loop control algorithm logic.

4. The method according to claim 3, characterized in that, The closed-loop control algorithm logic includes: Using the transfer function model as the controlled object, the output power value of the current calculation is obtained by refitting the algorithm's calculation result with the function fitting relationship. The cooling amount calculated in this case is determined based on the output power value. Subtract the calculated cooling amount from the initial temperature value to obtain the current temperature value; The difference between the current temperature value and the set temperature control point value is used as the logical input of the control algorithm, and the next iteration calculation is performed.

5. The method according to claim 4, characterized in that, Building a closed-loop temperature control strategy model in mathematical simulation software also includes: The control parameters of the closed-loop control algorithm are set to be modifiable. And set up the evaluation index output model.

6. An automated intelligent optimization device for active temperature control simulation of mechanical refrigeration machines, characterized in that, include: The simulation module is configured to build an electronic model of the mechanical refrigeration unit in an electromagnetic simulation software platform; The electronic model includes at least a compressor electromagnetic calculation model and a drive circuit model; The optimization module is configured to build an accelerated calculation workflow for the drive circuit model on the optimization software platform to obtain an accelerated calculation file; the accelerated calculation file includes at least the mapping relationship between the compressor input signal amplitude and the average output power. The calculation module is configured to build a closed-loop temperature control strategy model in mathematical simulation software; the closed-loop temperature control strategy model includes at least the transfer function model of the mechanical refrigeration machine, and a function fitting formula for the mapping relationship between the amplitude of the compressor input signal and the average output power; The optimization module is also configured to build a parameter optimization workflow on the optimization software platform, run the parameter optimization workflow to determine the target performance index of the mechanical refrigeration machine, and determine the evaluation index based on the target performance index. The accelerated computation workflow for building the driving circuit model on the optimized software platform yields accelerated computation files, including: The scanning range and parameter distribution of the input signal amplitude are set in the optimization software platform; Record the steps of starting the electromagnetic simulation software, modifying the input signal amplitude, and saving the output results on the electromagnetic simulation software platform, and save the recording results as a single simulation script file; The pre-written steady-state power extraction program is imported into the accelerated computation workflow, and combined with the single simulation script file to obtain the accelerated computation file; the accelerated computation file includes the mapping relationship between the corresponding parameters of the input signal amplitude and the output average power calculation results under a single simulation; the steady-state power extraction program runs on mathematical simulation software; Set the automatic optimization experiment parameters; the automatic optimization experiment parameters include at least the iteration step size and the point-scattering method; The parameter optimization workflow is run to determine the target performance indicators of the mechanical refrigeration unit, including: Write an executable file for the parameter optimization workflow; the executable file shall include at least the following contents: reading the accelerated calculation file, setting the model running time and step size, reading the temperature output curve, and calculating the target performance index; A list of control parameters is set in the optimization software platform, and an output parameter file is created in the project file directory of the optimization software platform; the output parameter file is read by the optimization software and mapped and connected to the parameters in the control parameter list. Import the executable file of the parameter optimization workflow and the closed-loop temperature control strategy model into the parameter optimization workflow, and run the parameter optimization workflow to determine the target performance indicators of the mechanical refrigeration machine. Evaluation indicators are determined based on the target performance indicators, including: Pre-evaluation indicators are determined based on the target performance indicators of the mechanical refrigeration machine; A single-objective optimization algorithm is established to optimize the control parameters by targeting the minimum value among the pre-evaluation indicators. The optimized control parameters are input into the closed-loop temperature control strategy model, and the optimization result is determined based on the running results of the closed-loop temperature control strategy model.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5.

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