A Multi-Objective Optimization Method for Environmental Control Mode of Thermal Management Combined Power Unit

Abstract

This invention discloses a multi-objective optimization method for the environmental control mode of a thermally managed combined power unit, comprising: Step 1, constructing a simulation model of the environmental control mode of the thermally managed combined power unit; Step 2, formulating a multi-objective optimization evaluation strategy for the thermally managed combined power unit under environmental control mode based on its requirements; Step 3, using the multi-objective optimization evaluation strategy to perform multi-objective optimization of the thermally managed combined power unit under environmental control mode. This invention addresses the problem that existing parameter configuration methods for thermally managed combined power units, which rely on expert experience to select several sets of typical design parameters, lead to high difficulty in designing parameters, and are characterized by high subjectivity and low accuracy, thus affecting the overall optimization effect of the unit.

Description

Technical Field

[0001] This invention relates to, but is not limited to, the field of aviation electromechanical technology, and specifically to a multi-objective optimization method for the environmental control mode of a thermal management type combined power unit. Background Technology

[0002] In the integrated technology of aviation electromechanical systems, by integrating the auxiliary power system, emergency power system and thermal management system of an aircraft into a thermal management-type combined power unit, the number of redundant parts in the aviation electromechanical system is greatly reduced, the overall size and weight of the aviation electromechanical system are reduced, and the system efficiency is improved. This is a key focus of research on the next generation of aviation electromechanical technology.

[0003] Existing thermal management combined power units have multiple operating modes under different operational scenarios, including engine start-up mode, ground maintenance mode, environmental control cooling mode, and emergency power mode. Among these, the environmental control cooling mode, which provides cooling air to the aircraft, is the operating mode with the longest operating time and the highest operational complexity for thermal management combined power units. This environmental control cooling mode requires multi-objective optimization and has device boundary constraints. Researching multi-objective optimization methods for the environmental control cooling mode can help improve the energy utilization rate and system stability of thermal management combined power units.

[0004] Currently, the commonly used parameter configuration method for the environmental control cooling mode of thermal management combined power units is to select several sets of typical design parameters based on expert experience, with the goal of meeting functional requirements and unit constraints, and then compare and select the most suitable parameter configuration results. However, the following problems exist:

[0005] 1. The design parameters based on expert experience are usually aimed at meeting functional and performance indicators, without considering the optimization of the combined power unit as a key factor. As a result, the overall efficiency of the combined power unit is often low, and there is room for further improvement.

[0006] 2. When the combined power unit is operating in the environmental control cooling mode, the unit also has the need for multi-objective control and multiple constraints. It is difficult for experts to give design parameters based on experience, and the parameter design method is characterized by high subjectivity and low accuracy, which affects the overall optimization effect of the unit.

[0007] 3. Currently, exhaustive methods can be used to calculate a large number of design parameters, which can yield parameter design results with better energy efficiency. However, this device has a high degree of nonlinearity and strong coupling under the environmental control cooling mode. Exhaustive methods will greatly increase the calculation cost and have low efficiency. Summary of the Invention

[0008] The purpose of this invention is to provide a multi-objective optimization method for the environmental control mode of a thermal management type combined power unit, in order to solve the problem that the existing parameter configuration of thermal management type combined power units, which selects several typical design parameters by means of expert experience, is difficult to design parameters and has the characteristics of high subjectivity and low accuracy, thus affecting the overall optimization effect of the unit.

[0009] The technical solution of the present invention: Embodiments of the present invention provide a multi-objective optimization method for the environmental control mode of a thermal management type combined power unit, comprising:

[0010] Step 1: Construct a simulation model of the environmental control mode for a thermal management type combined power unit;

[0011] Step 2: Based on the requirements of the thermal management type combined power unit under environmental control mode, formulate a multi-objective optimization evaluation strategy for the thermal management type combined power unit under environmental control mode; the methods for formulating the multi-objective optimization evaluation strategy include:

[0012] S21, Determine the control target parameters of the thermal management type combined power unit under environmental control mode;

[0013] S22, determine the optimization evaluation target of the thermal management type combined power unit under the environmental control mode, and establish the fitness function between the optimization evaluation target and the control target parameters in combination with the environmental control mode simulation model;

[0014] Step 3: A multi-objective optimization evaluation strategy is adopted to perform multi-objective optimization on the thermal management type combined power unit under environmental control mode.

[0015] Optionally, in the multi-objective optimization method for the environmental control mode of the thermal management type combined power unit as described above, step 1 includes:

[0016] Step 11: Model each component based on the parameters of each component of the thermal management type combined power unit to form a model of each individual component;

[0017] Step 12: Based on the models of individual components, establish the equilibrium equations of the thermal management type combined power unit under the environmental control mode, including: obtaining the interface continuity conditions of each component according to the hardware structure and connection form of the device, calculating the convergence of the air circulation loop in the device, and constructing the matching relationship of coaxial components.

[0018] Optionally, in the multi-objective optimization method for the environmental control mode of the thermal management type combined power unit as described above, step 3 includes:

[0019] Taking the environmental control mode simulation model as the object, the optimal solution set of the optimization evaluation objective is obtained by using the Pareto-based multi-objective particle swarm optimization algorithm with an external archiving mechanism.

[0020] Optionally, in the multi-objective optimization method for the environmental control mode of the thermal management type combined power unit as described above,

[0021] By employing a multi-objective particle swarm intelligence algorithm for parameter configuration optimization, and combining it with chaotic initialization, adaptive adjustment of inertial weights, external archiving strategy updates, and mutation strategies, the algorithm is improved to obtain the optimal solution set for the improved optimization evaluation objective.

[0022] Optionally, in the multi-objective optimization method for the environmental control mode of the thermal management type combined power unit as described above, the specific process of step 3 includes:

[0023] Step 31: Initialize the computational parameters in the multi-objective particle swarm optimization algorithm, and set the constraint range of the control target parameters and boundary parameters of the device to be optimized;

[0024] Step 32: Initialize the control target parameters of the thermal management type combined power unit in the environmental control mode to form the initial population S(i);

[0025] Step 33: Calculate the fitness value of each individual particle in the population S(i) according to the fitness function, and initially set the individual extreme value Pbest and the population extreme value Gbest for the fitness value of each individual particle.

[0026] Step 34: Adjust the inertia weights of the multi-objective particle swarm optimization algorithm, update the population velocity and position based on the adjusted inertia weights, and use a mutation strategy to generate the next generation population S(i+1).

[0027] Step 35: Calculate the fitness value of each individual particle in the updated population S(i+1) according to the fitness function, and construct the Pareto optimal frontier;

[0028] Step 36: Use the Pareto optimal frontier obtained in this round to update the original Pareto optimal frontier in the external archive, and adjust the archive size to make the Pareto frontier distribution evenly dispersed.

[0029] Step 37: Update the individual extreme value Pbest and the group extreme value Gbest in S(i) by comparing the current individual extreme value Pbest and the group extreme value Gbest in S(i+1).

[0030] Step 38: If the maximum number of iterations M has been reached, the algorithm ends and outputs the last updated Pareto optimal frontier; otherwise, return to step 34.

[0031] Optionally, in the multi-objective optimization method for the environmental control mode of the thermal management type combined power unit as described above, step 32 includes:

[0032] A chaotic initialization strategy is adopted to randomly generate an N-dimensional vector Z, which consists of N particles. Each particle includes three control target parameters, i.e., an N*3 vector. The value of each individual particle in vector Z is a random value within a specified range of the corresponding control target parameter, which is used to initialize the particle swarm position and randomly initialize the particle velocity to obtain the initial population S(i), and the initial iteration number i = 0 is set; where each individual particle in vector Z is each target parameter in vector Z.

[0033] Optionally, in the multi-objective optimization method for the environmental control mode of the thermal management type combined power unit as described above, the method for constructing the Pareto optimal front in step 35 is as follows:

[0034] If there are no other particles Ni in population S(i+1) whose fitness values ​​are all better than those of particle N1, then particle N1 is called the Pareto optimal solution, and the set of all Pareto optimal solutions in population S(i+1) is called the Pareto optimal frontier; among them, if the fitness values ​​of particle N1 are all better than those of particle N2, then particle N1 is said to be better than particle N2.

[0035] Optionally, in the multi-objective optimization method for the environmental control mode of the thermal management type combined power unit as described above, the update method in step 37 includes:

[0036] The method for updating the population extremum is to randomly select a particle from the Pareto optimal frontier as the population extremum Gbest;

[0037] The method for updating the individual extreme value is that if a particle in S(i+1) is better than the corresponding particle in S(i), then the better particle in S(i+1) is taken as the individual extreme value Pbest of that particle.

[0038] The beneficial effects of the present invention: The embodiments of the present invention provide a multi-objective optimization method for the environmental control mode of a thermal management type combined power unit, which has the following beneficial effects:

[0039] 1) In view of the characteristics of the thermal management type combined power unit's environmental control mode operation state having many design parameters and complex boundary constraints, a simulation model suitable for this mode is established, which can realize rapid simulation;

[0040] 2) Establish a multi-objective optimization evaluation method with energy utilization and system stability as evaluation indicators and system constraints as boundary conditions. A multi-objective particle swarm optimization algorithm is used to optimize the parameter configuration of the environmental control mode simulation model of a thermally managed combined power unit, obtaining a multi-objective optimal parameter solution set that satisfies the optimization evaluation method. Users can select the parameter results within the optimal solution set according to different needs. Compared with traditional environmental control mode parameter configuration methods, this invention can improve energy utilization and system stability.

[0041] 3) The multi-objective particle swarm optimization algorithm balances computational efficiency and optimization effect, saving computational costs. Attached Figure Description

[0042] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of the present invention and do not constitute a limitation on the technical solutions of the present invention.

[0043] Figure 1 This is a schematic diagram of the architecture of the thermal management type combined power unit environmental control mode used in the multi-objective optimization method for the environmental control mode of the thermal management type combined power unit provided in the embodiments of the present invention;

[0044] Figure 2 A flowchart of the multi-objective particle swarm optimization algorithm in the environmental control mode multi-objective optimization method for thermal management combined power devices is provided for embodiments of the present invention.

[0045] Figure 3 This is a schematic diagram of the Pareto front optimization solution set in the multi-objective optimization method for the environmental control mode of the thermal management type combined power unit provided in the embodiments of the present invention.

[0046] Explanation of reference numerals in the attached figures:

[0047] 1. Compressor; 2. Power turbine; 3. Refrigeration turbine; 4. Combustion chamber; 5. Generator; 6. Air cooling heat exchanger; 7. Air regeneration heat exchanger; 8. Liquid cooling heat exchanger. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

[0049] As explained in the background section above, the commonly used parameter configuration method for the environmental control cooling mode of thermal management combined power units is to select several sets of typical design parameters based on expert experience, with the goal of meeting functional requirements and device constraints, and then compare and select the most suitable parameter configuration results, which has various problems.

[0050] The present invention provides the following specific embodiments, which can be combined with each other. For the same or similar concepts or processes, they may not be described again in some embodiments.

[0051] To address the aforementioned problems with the commonly used parameter configurations in the environmental control cooling mode of existing thermal management combined power units, this invention proposes a multi-objective optimization method for the environmental control mode of airflow thermal management combined power units. Considering the characteristics of multiple control objectives and multiple evaluation indicators in the environmental control mode, a multi-optimization objective evaluation method for the environmental control mode is established. This method applies a multi-objective particle swarm optimization algorithm to select the optimal parameter solution set, thereby configuring system parameters according to different user needs while balancing computational efficiency and optimization effect.

[0052] The multi-objective optimization method for the environmental control mode of the airflow thermal management combined power unit proposed in this invention mainly includes the following three parts: constructing a simulation model of the environmental control mode of the thermal management combined power unit, a multi-objective optimization evaluation strategy for the environmental control mode of the unit, and a multi-objective particle swarm optimization algorithm optimization process applicable to the environmental control mode of the unit.

[0053] The implementation methods of the above content are explained below:

[0054] Step 1: Construct a simulation model of the environmental control mode for a thermal management type combined power unit;

[0055] Figure 1 This is a schematic diagram of the architecture of the thermal management combined power unit's environmental control mode, used in the multi-objective optimization method for the environmental control mode of the thermal management combined power unit provided in this embodiment of the invention. Figure 1 As shown, the components of a thermal management type combined power unit include: compressor 1, power turbine 2, refrigeration turbine 3, combustion chamber 4, generator 5, air-cooled heat exchanger 6, air-regenerated heat exchanger 7, and liquid-cooled heat exchanger 8. The input and output parameters of each component in the thermal management type combined power unit are determined as follows:

[0056] The input and output parameters of compressor 1, power turbine 2 and refrigeration turbine 3 include gas flow rate, pressure, temperature and shaft speed;

[0057] The input and output parameters of combustion chamber 4 include gas flow rate, pressure, temperature, and fuel flow rate;

[0058] The input and output parameters of generator 5 include shaft speed, input shaft power and power generation;

[0059] The input and output parameters of the heat exchanger include cold / hot end flow rate, pressure and temperature. The heat exchanger consists of three heat exchangers: air-cooled heat exchanger 6, air-regenerated heat exchanger 7, and liquid-cooled heat exchanger 8.

[0060] The method for constructing the environmental control mode simulation model in this section is as follows:

[0061] S1, Model each component based on the parameters of each component of the thermal management type combined power unit to form a model of each individual component;

[0062] S2. Based on the models of individual components, establish the equilibrium equations of the thermal management type combined power unit under the environmental control mode, including: obtaining the interface continuity conditions of each component according to the hardware structure and connection form of the device, calculating the convergence of the air circulation loop in the device, and constructing the matching relationship of coaxial components to establish the equilibrium equations of the thermal management type combined power unit under the environmental control mode; wherein, compressor 1, power turbine 2, refrigeration turbine 3 and generator 5 are coaxial components.

[0063] In S2 above, the continuity conditions of the interfaces of each component include:

[0064] 1) The temperature and pressure parameters at the compressor outlet and the hot end inlet of the air cooling heat exchanger are equal, and the compressor outlet flow rate is equal to the sum of the hot end inlet flow rate of the air cooling heat exchanger and the engine bleed air / make-up air flow rate.

[0065] 2) The temperature, flow rate, and pressure parameters at the hot end outlet of the air-cooled heat exchanger and the hot end inlet of the air-regenerated heat exchanger are equal;

[0066] 3) The temperature, flow rate, and pressure parameters of the hot end outlet of the air regeneration heat exchanger and the inlet of the cooling turbine are equal;

[0067] 4) The temperature and pressure parameters at the outlet of the cooling turbine and the inlet of the hot end of the liquid-cooled heat exchanger are equal, and the outlet flow rate of the cooling turbine is equal to the sum of the inlet flow rate of the hot end of the liquid-cooled heat exchanger and the cabin cooling gas flow rate.

[0068] 5) The temperature, flow rate, and pressure parameters at the hot end outlet of the liquid-cooled heat exchanger and the cold end inlet of the air regeneration heat exchanger are equal;

[0069] 6) The temperature, flow rate, and pressure parameters at the cold end outlet of the air regeneration heat exchanger and the inlet of the compressor are equal;

[0070] 7) The temperature, flow rate, and pressure parameters at the combustion chamber outlet and the power turbine inlet are equal.

[0071] It should be noted that a balance equation is established for each of the above conditions.

[0072] (2) The convergence of the air circulation loop means that the inlet and outlet parameters of compressor 1, heat exchangers 6-8 and cooling turbine 3 reach stable convergence after iterative calculation, and the convergence error of the parameters of the above components is less than 0.001.

[0073] (3) The matching relationship of the coaxial components of the device is established as follows: the rotor system consisting of compressor 1, power turbine 2, cooling turbine 3 and generator 5 has balanced shaft power and no residual torque in the rotor system.

[0074] Step two: Based on the requirements of the thermal management type combined power unit under environmental control mode, formulate a multi-objective optimization evaluation strategy for the thermal management type combined power unit under environmental control mode; the methods for formulating the multi-objective optimization evaluation strategy include:

[0075] S21, determine the control target parameters of the thermal management type combined power unit under the environmental control mode. The control target parameters include: shaft speed, compressor inlet pressure and air cooling heat exchanger heat exchange capacity; each parameter has a preset range.

[0076] S22, the optimization evaluation objectives of the thermal management type combined power unit under the environmental control mode are determined to be the generator bleed air flow rate and the compressor stability margin. Based on the above simulation model, the functional relationship between the optimization evaluation objective and the control objective parameters is established, namely the fitness function.

[0077] Step 3: Optimize strategy execution.

[0078] In this step, a multi-objective optimization evaluation strategy is specifically adopted to perform multi-objective optimization on the thermal management type combined power unit under environmental control mode. During execution, using the environmental control mode simulation model as the object, a Pareto-based multi-objective particle swarm optimization algorithm with an external archiving mechanism is employed to obtain the optimal solution set for the optimization evaluation objectives.

[0079] Specifically, in the process of parameter configuration optimization using the multi-objective particle swarm intelligence algorithm, the algorithm is improved by combining chaotic initialization, adaptive adjustment of inertial weight, external archiving strategy update, and mutation strategy, so as to obtain the optimal solution set of the optimization evaluation objective of the algorithm improvement.

[0080] like Figure 2 The diagram shows a flowchart of the multi-objective particle swarm optimization algorithm in the environmental control mode multi-objective optimization method for a thermal management type combined power unit provided in an embodiment of the present invention. The optimization process using the multi-objective particle swarm optimization algorithm includes the following steps:

[0081] S31, Initialize the computational parameters in the multi-objective particle swarm optimization algorithm. These parameters include the population size N, learning factors c1 and c2, inertia weight ranges Wmax and Wmin, external archive size Q, mutation rate u, and maximum number of iterations M; set the constraint range of the control target parameters and boundary parameters of the device to be optimized.

[0082] S32, Initialize the control target parameters of the thermal management type combined power unit in environmental control mode;

[0083] In this step, a chaotic initialization strategy is adopted to randomly generate an N-dimensional vector Z (the vector Z consists of N particles, each particle includes 3 control target parameters, i.e., an N*3 vector). The value of each individual particle (i.e. each target parameter) in the vector Z is a random value within the specified range of the corresponding control target parameter, which is used to initialize the position of the particle swarm and randomly initialize the velocity of the particles to obtain the initial population S(i), and set the initial iteration number i = 0.

[0084] S33, calculate the fitness value of each individual particle in the population S(i) according to the fitness function, and initially set the individual extreme value Pbest and the population extreme value Gbest of the fitness value of each individual particle.

[0085] S34, adjust the inertia weights of the multi-objective particle swarm algorithm, update the population velocity and position based on the adjusted inertia weights, and use a mutation strategy to generate the next generation population S(i+1), and update the counter i = i+1.

[0086] S35. Calculate the fitness value of each individual particle in the updated population S(i+1) based on the fitness function, and construct the Pareto optimal frontier.

[0087] In this step, the method for constructing the Pareto optimal front is as follows: if there are no other particles Ni in population S(i+1) whose fitness values ​​are all better than those of particle N1, then particle N1 is called the Pareto optimal solution, and the set of all Pareto optimal solutions in population S(i+1) is called the Pareto optimal front; wherein, if all fitness values ​​of particle N1 are better than those of particle N2, then particle N1 is said to be better than particle N2. Figure 3 The diagram shown is a schematic of the Pareto front optimization solution set in the multi-objective optimization method for the environmental control mode of the thermal management type combined power unit provided in the embodiment of the present invention.

[0088] S36. The Pareto optimal frontier obtained in this round is used to update the original Pareto optimal frontier in the external archive, and the archive size is adjusted (the archive size has an upper limit, and the updated selection of some optimal solutions is used) to make the Pareto frontier evenly distributed and avoid excessive concentration in a certain local area.

[0089] S37. Update the individual extreme value Pbest and the group extreme value Gbest in S(i) by comparing the current individual extreme value Pbest and the group extreme value Gbest in S(i+1).

[0090] The update method in this step is as follows: the method to update the population extreme value is to randomly select a particle from the Pareto optimal frontier as the population extreme value Gbest; the method to update the individual extreme value is that if the particle in S(i+1) is better than the corresponding particle in S(i), then the better particle in S(i+1) is taken as the individual extreme value Pbest of that particle.

[0091] S38. If the maximum number of iterations M has been reached, the algorithm ends and outputs the Pareto optimal frontier of the last update; otherwise, it returns to execute S34.

[0092] Using the multi-objective optimization method provided in this embodiment of the invention, the output Pareto optimal frontier is the optimal solution set of the thermal management type combined power unit in environmental control mode. Users can select the parameter results in the optimal solution set according to different needs.

[0093] The multi-objective optimization method for the environmental control mode of a thermal management type combined power unit provided in this invention has the following beneficial effects:

[0094] 1) In view of the characteristics of the thermal management type combined power unit's environmental control mode operation state having many design parameters and complex boundary constraints, a simulation model suitable for this mode is established, which can realize rapid simulation;

[0095] 2) Establish a multi-objective optimization evaluation method with energy utilization and system stability as evaluation indicators and system constraints as boundary conditions. A multi-objective particle swarm optimization algorithm is used to optimize the parameter configuration of the environmental control mode simulation model of a thermally managed combined power unit, obtaining a multi-objective optimal parameter solution set that satisfies the optimization evaluation method. Users can select the parameter results within the optimal solution set according to different needs. Compared with traditional environmental control mode parameter configuration methods, this invention can improve energy utilization and system stability.

[0096] 3) The multi-objective particle swarm optimization algorithm balances computational efficiency and optimization effect, saving computational costs.

[0097] The following example illustrates the specific implementation of the multi-objective optimization method for the environmental control mode of the thermal management type combined power unit provided in this invention.

[0098] Implementation Example

[0099] Please see Figures 1 to 3 As shown,

[0100] This implementation example is for Figure 1 The thermal management type combined power unit environmental control mode architecture shown includes: compressor 1, power turbine 2, refrigeration turbine 3, combustion chamber 4, generator 5, air cooling heat exchanger 6, air regeneration heat exchanger 7, and liquid cooling heat exchanger 8.

[0101] Mathematical models are created based on the characteristics of the selected components, such as compressors, combustion chambers, power turbines, cooling turbines, generators, and heat exchangers.

[0102] After establishing the models of each component, the steady-state common working equation of the environmental control mode is established. Based on the continuity conditions of the interfaces of each component and the convergence and coaxial matching relationship of the air circulation system, the equilibrium equations are established.

[0103] The continuity conditions of the interfaces of each component include:

[0104] 1) The temperature and pressure parameters at the compressor outlet and the hot end inlet of the air cooling heat exchanger are equal, and the compressor outlet flow rate is equal to the sum of the hot end inlet flow rate of the air cooling heat exchanger and the engine bleed air / make-up air flow rate.

[0105] 2) The temperature, flow rate, and pressure parameters at the hot end outlet of the air-cooled heat exchanger and the hot end inlet of the air-regenerated heat exchanger are equal;

[0106] 3) The temperature, flow rate, and pressure parameters of the hot end outlet of the air regeneration heat exchanger and the inlet of the cooling turbine are equal;

[0107] 4) The temperature and pressure parameters at the outlet of the cooling turbine and the inlet of the hot end of the liquid-cooled heat exchanger are equal, and the outlet flow rate of the cooling turbine is equal to the sum of the inlet flow rate of the hot end of the liquid-cooled heat exchanger and the cabin cooling gas flow rate.

[0108] 5) The temperature, flow rate, and pressure parameters at the hot end outlet of the liquid-cooled heat exchanger and the cold end inlet of the air regeneration heat exchanger are equal;

[0109] 6) The temperature, flow rate, and pressure parameters at the cold end outlet of the air regeneration heat exchanger and the inlet of the compressor are equal;

[0110] 7) The temperature, flow rate, and pressure parameters at the combustion chamber outlet and the power turbine inlet are equal.

[0111] The calculation convergence of the air circulation system refers to the stable convergence of the inlet and outlet parameters of the compressor, heat exchanger, and cooling turbine after iterative calculation, with the convergence error of each parameter being less than 0.001.

[0112] The method for establishing the coaxial matching equation of this device is as follows: the rotor system consisting of the compressor, power turbine, cooling turbine and generator has shaft power balance and no residual torque in the rotor system.

[0113] The following steps are taken to establish a multi-objective optimization evaluation method under the environmental control model:

[0114] 1) The control targets for the thermal management type combined power unit under the environmental control mode are determined to be shaft speed, compressor inlet pressure, and heat exchange capacity of the air cooling heat exchanger;

[0115] 2) The optimization evaluation objectives of the thermal management type combined power unit under the environmental control mode are determined to be the generator bleed air flow rate and the compressor stability margin. Based on the above simulation model, the functional relationship between the optimization objective and the control parameters is established, namely the fitness function.

[0116] In this implementation example, the fitness function is constructed as shown below:

[0117]

[0118] In the formula: x1, x2, and x3 represent the control target parameters of the model: shaft speed, compressor inlet pressure, and air cooling heat exchanger heat transfer; c represents other input parameters of the model, such as ambient temperature and pressure; W represents the overall optimization evaluation objective of the thermal management type combined power unit in environmental control mode; F represents the objective function of engine bleed air flow rate; H represents the objective function of compressor stability margin; G represents the boundary constraint conditions of the constraint parameters determined according to the unit requirements, such as cabin cooling capacity and power generation; x i _min and x i _max represents the search space range that controls the target parameters.

[0119] Based on the multi-objective particle swarm optimization process, the optimization steps under the environmental control mode of the thermal management type combined power unit can be derived as follows, and the corresponding process is as follows: Figure 2 As shown.

[0120] Step 1: Set the population size (i.e., initial particle count of 200), maximum number of iterations of 100; learning factor c1 is 1.5, c2 is 1.5; specify the inertia weight variation range as 0.2–0.8; external archive size Q is 40; mutation rate is 0.5; determine the search space range of the device's control target parameters as follows: shaft speed 0.73–0.8, compressor inlet pressure 120–300 kPa, air cooling heat exchanger heat exchange capacity 100–200 kW; set the constraint range of boundary parameters corresponding to normal temperature operating conditions.

[0121] Step 2: Using a chaotic initialization strategy, an n-dimensional vector Z is randomly generated. The value of each individual particle in vector Z is a random value within a specified range of the corresponding control target parameter. This value is used to initialize the position of the particle swarm and randomly initialize the velocity of the particles to obtain the initial population S(i). The initial iteration number i = 0 is set.

[0122] Step 3: Calculate the fitness value of each individual particle in the population S(i) according to the fitness function, and initially set the individual extreme value Pbest and the population extreme value Gbest for the fitness value of each individual particle.

[0123] Step 4: Adjust the inertia weights according to the adaptive strategy, then update the population velocity and position, and use the mutation strategy to generate the next generation population S(i+1), and update the counter i = i+1.

[0124] Step 5: Calculate the fitness value of each individual particle in the updated population S(i+1) based on the fitness function. If the constraint parameters of a particle exceed the boundary constraints, a constraint penalty strategy is applied to the non-compliant individual particle according to the constraints, setting its fitness value to the worst value. Then, construct the Pareto optimal front. The method for constructing the Pareto optimal front is as follows: If there are no other particles Ni in population S(i+1) whose fitness values ​​are all better than particle N1, then particle N1 is called the Pareto optimal solution, and the set of all Pareto optimal solutions in population S(i+1) is called the Pareto optimal front; if all fitness values ​​of particle N1 are better than particle N2, then particle N1 is said to be better than particle N2, such as... Figure 3 As shown.

[0125] Step 6: Update the external archive and adjust its size to ensure a more even distribution of the Pareto front, avoiding excessive concentration in any particular local area.

[0126] Step 7: Update the individual extreme value Pbest and the group extreme value Gbest by comparing the individual optimal value with the global optimal value.

[0127] Step 8: If the iteration condition is met, the algorithm ends and outputs the Pareto optimal frontier. See the diagram for a schematic of the Pareto optimal frontier. Figure 3 Otherwise, return to step four.

[0128] Finally, the optimization results regarding engine bleed air volume and compressor stability margin were obtained, yielding the Pareto optimal front, which represents the optimal solution set under the current operating conditions of the environmental control mode. The optimization results are shown in Table 1. Users can select the parameter results within the optimal solution set according to different needs.

[0129] Table 1 Multi-objective optimization table for environmental control mode

[0130]

[0131] While the embodiments disclosed in this invention are as described above, they are merely illustrative of the embodiments to facilitate understanding of the invention and are not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in the form and details of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A multi-objective optimization method for the environmental control mode of a thermal management type combined power unit, characterized in that, The method, based on the requirement of multi-objective optimization and the existence of device boundary constraints in the environmentally controlled cooling mode, includes: Step 1: Construct a simulation model of the thermal management type combined power unit under environmental control mode. The thermal management type combined power unit under environmental control cooling mode includes: compressor, power turbine, refrigeration turbine, combustion chamber, generator, air cooling heat exchanger, air regeneration heat exchanger, and liquid cooling heat exchanger. Step 2: Based on the requirements of the thermal management type combined power unit under the environmental control cooling mode, formulate a multi-objective optimization evaluation strategy for the thermal management type combined power unit under the environmental control cooling mode; the methods for formulating the multi-objective optimization evaluation strategy include: S21, Determine the control target parameters of the thermal management type combined power unit under the environmental control cooling mode; S22, determine the optimization evaluation target of the thermal management type combined power unit under the environmental control cooling mode, and establish the fitness function between the optimization evaluation target and the control target parameters in combination with the environmental control mode simulation model; the optimization evaluation target includes the generator bleed air flow rate and the compressor stability margin. Step 3: A multi-objective optimization evaluation strategy is adopted to perform multi-objective optimization on the thermal management type combined power unit under the environmental control cooling mode.

2. The multi-objective optimization method for the environmental control mode of the thermal management type combined power unit according to claim 1, characterized in that, Step 1 includes: Step 11: Model each component based on the parameters of each component of the thermal management type combined power unit to form a model of each individual component; Step 12: Based on the models of individual components, establish the equilibrium equations of the thermal management type combined power unit under the environmental control cooling mode, including: obtaining the interface continuity conditions of each component according to the hardware structure and connection form of the device, calculating the convergence of the air circulation loop in the device, and constructing the matching relationship of coaxial components.

3. The multi-objective optimization method for the environmental control mode of the thermal management type combined power unit according to claim 2, characterized in that, Step 3 includes: Taking the environmental control mode simulation model as the object, the optimal solution set of the optimization evaluation objective is obtained by using the Pareto-based multi-objective particle swarm optimization algorithm with an external archiving mechanism.

4. The multi-objective optimization method for the environmental control mode of the thermal management type combined power unit according to claim 3, characterized in that, By employing a multi-objective particle swarm intelligence algorithm for parameter configuration optimization, and combining it with chaotic initialization, adaptive adjustment of inertial weights, external archiving strategy updates, and mutation strategies, the algorithm is improved to obtain the optimal solution set for the improved optimization evaluation objective.

5. The multi-objective optimization method for the environmental control mode of the thermal management type combined power unit according to claim 4, characterized in that, The specific process of step 3 includes: Step 31: Initialize the computational parameters in the multi-objective particle swarm optimization algorithm, and set the constraint range of the control target parameters and boundary parameters of the device to be optimized; Step 32: Initialize the control target parameters of the thermal management type combined power unit in the environmental control cooling mode to form the initial population S(i); Step 33: Calculate the fitness value of each individual particle in the population S(i) according to the fitness function, and initially set the individual extreme value Pbest and the population extreme value Gbest for the fitness value of each individual particle. Step 34: Adjust the inertia weights of the multi-objective particle swarm optimization algorithm, update the population velocity and position based on the adjusted inertia weights, and use a mutation strategy to generate the next generation population S(i+1). Step 35: Calculate the fitness value of each individual particle in the updated population S(i+1) according to the fitness function, and construct the Pareto optimal frontier; Step 36: Use the Pareto optimal frontier obtained in this round to update the original Pareto optimal frontier in the external archive, and adjust the archive size to make the Pareto frontier distribution evenly dispersed. Step 37: Update the individual extreme value Pbest and the group extreme value Gbest in S(i) by comparing the current individual extreme value Pbest and the group extreme value Gbest in S(i+1); Step 38: If the maximum number of iterations M has been reached, the algorithm ends and outputs the last updated Pareto optimal frontier; otherwise, return to step 34.

6. The multi-objective optimization method for the environmental control mode of the thermal management type combined power unit according to claim 5, characterized in that, Step 32 includes: A chaotic initialization strategy is adopted to randomly generate an N-dimensional vector Z, which consists of N particles. Each particle includes three control target parameters, i.e., an N*3 vector. The value of each individual particle in vector Z is a random value within a specified range of the corresponding control target parameter, which is used to initialize the particle swarm position and randomly initialize the particle velocity to obtain the initial population S(i), and set the initial iteration number i=0; where each individual particle in vector Z is each target parameter in vector Z.

7. The multi-objective optimization method for the environmental control mode of the thermal management type combined power unit according to claim 5, characterized in that, The method for constructing the Pareto optimal front in step 35 is as follows: If there are no other particles Ni in population S(i+1) whose fitness values ​​are all better than those of particle N1, then particle N1 is called the Pareto optimal solution, and the set of all Pareto optimal solutions in population S(i+1) is called the Pareto optimal frontier; among them, if the fitness values ​​of particle N1 are all better than those of particle N2, then particle N1 is said to be better than particle N2.

8. The multi-objective optimization method for the environmental control mode of the thermal management type combined power unit according to claim 5, characterized in that, The update method in step 37 includes: The method for updating the population extremum is to randomly select a particle from the Pareto optimal frontier as the population extremum Gbest; The method for updating the individual extreme value is that if a particle in S(i+1) is better than the corresponding particle in S(i), then the better particle in S(i+1) is taken as the individual extreme value Pbest of that particle.

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