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CN115859831A - Multi-objective optimization method for environment control mode of thermal management type combined power device - Google Patents

Multi-objective optimization method for environment control mode of thermal management type combined power device Download PDF

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CN115859831A
CN115859831A CN202211701978.1A CN202211701978A CN115859831A CN 115859831 A CN115859831 A CN 115859831A CN 202211701978 A CN202211701978 A CN 202211701978A CN 115859831 A CN115859831 A CN 115859831A
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combined power
control mode
particle
type combined
management type
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陈丽君
杜翔宇
朱银锋
周禹男
郭文军
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AVIC Jincheng Nanjing Engineering Institute of Aircraft Systems
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AVIC Jincheng Nanjing Engineering Institute of Aircraft Systems
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Abstract

The invention discloses a multi-objective optimization method for a thermal management type combined power device in an environment control mode, which comprises the following steps: step 1, constructing an environment control mode simulation model of a heat management type combined power device; step 2, forming a multi-objective optimization evaluation strategy of the heat management type combined power device in the environment control mode according to the requirement of the heat management type combined power device in the environment control mode; and 3, performing multi-target optimization on the heat management type combined power device in a ring control mode by adopting a multi-target optimization evaluation strategy. The embodiment of the invention solves the problems that the design parameters are difficult due to the fact that a plurality of groups of typical design parameters are selected in an expert experience mode aiming at the existing parameter configuration mode of the heat management type combined power device, and the optimization effect of the whole device is influenced due to the characteristics of high subjectivity and low precision.

Description

Multi-objective optimization method for environment control mode of thermal management type combined power device
Technical Field
The invention relates to the technical field of aviation electromechanics, in particular to a multi-objective optimization method for a thermal management type combined power device in an environment control mode.
Background
In the comprehensive technology of the aeronautical electromechanical system, an auxiliary power system, an emergency power system and a thermal management system of an airplane are integrated to form a thermal management type combined power device, so that the number of redundant parts of the aeronautical electromechanical system is greatly reduced, the overall volume and weight of the aeronautical electromechanical system are reduced, the system efficiency is improved, and the comprehensive technology is a key focus object in the research of the new generation aeronautical electromechanical technology.
The conventional heat management type combined power device has multiple working modes under different working scenes, including an engine starting mode, a ground maintenance mode, an environment-friendly cooling mode, an emergency power mode and the like. The environment-controlled cooling mode provides a cooling air source for the airplane, and is the working mode with the longest running time and the highest working complexity of the thermal management type combined power device. The environment-controlled cooling mode has the requirement of multi-objective optimization and the device boundary constraint condition, and the multi-objective optimization method research on the environment-controlled cooling mode is beneficial to improving the energy utilization rate and the system stability of the heat management type combined power device.
The commonly used parameter configuration mode of the environmental control cooling mode of the existing heat management type combined power device is to select several groups of typical design parameters in an expert experience mode, to meet the functional requirements and the device constraint conditions as the target, and to compare and screen out the most appropriate parameter configuration result, but has the following problems:
1. the parameters are designed through expert experience, the purpose of meeting the function and performance indexes is usually achieved, the optimization of the combined power device is not taken as a key consideration factor, the overall efficiency of the combined power device is often low, and the space is further improved;
2. under the operating state of the combined power device in an environment-controlled cooling mode, the device also has the requirement of multi-target control and has multi-constraint conditions, the difficulty of designing parameters given by expert experience is high, and the parameter design method has the characteristics of high subjectivity and low precision, so that the optimization effect of the whole device is influenced;
3. at present, a parameter design result with better energy efficiency can be obtained by calculating a large number of design parameters by adopting an exhaustion method, but the device has high non-linear degree and strong coupling in an environment-controlled cooling mode operation state, the calculation cost can be greatly increased by the exhaustion method, and the efficiency is lower.
Disclosure of Invention
The purpose of the invention is as follows: the embodiment of the invention provides a multi-objective optimization method for an environment control mode of a heat management type combined power device, which aims to solve the problems that the difficulty of design parameters is high, the characteristics of high subjectivity and low precision exist and the optimization effect of the whole device is influenced due to the fact that a plurality of groups of typical design parameters are selected in an expert experience mode aiming at the existing parameter configuration form of the heat management type combined power device.
The technical scheme of the invention is as follows: the embodiment of the invention provides a multi-objective optimization method for a ring control mode of a heat management type combined power device, which comprises the following steps:
step 1, constructing a ring control mode simulation model of a heat management type combined power device;
step 2, forming a multi-objective optimization evaluation strategy of the heat management type combined power device in the environment control mode according to the requirement of the heat management type combined power device in the environment control mode; the method for forming the multi-objective optimization evaluation strategy comprises the following steps:
s21, determining control target parameters of the thermal management type combined power device in the environment control mode;
s22, determining an optimization evaluation target of the thermal management type combined power device in the environment control mode, and establishing a fitness function between the optimization evaluation target and a control target parameter by combining a simulation model of the environment control mode;
and 3, performing multi-target optimization on the heat management type combined power device in a ring control mode by adopting a multi-target optimization evaluation strategy.
Optionally, in the method for loop control mode multi-objective optimization of a thermal management type combined power plant as described above, the step 1 includes:
step 11, modeling each component according to parameters of each component of the thermal management type combined power device to form a model of an individual component;
step 12, establishing a balance equation of the thermal management type combined power device in a ring control mode according to the model of the single component, wherein the balance equation comprises the following steps: and obtaining interface continuous conditions of each component according to the hardware structure and the connection form of the device, calculating the convergence of an air circulation loop in the device, and constructing the matching relation of coaxial components.
Optionally, in the method for loop control mode multi-objective optimization of a thermal management type combined power plant as described above, the step 3 includes:
and taking the environment control mode simulation model as an object, and obtaining an optimal solution set of an optimization evaluation target by adopting a Pareto-based multi-target particle swarm algorithm with an external archiving mechanism.
Alternatively, in the method for loop control mode multi-objective optimization of the thermal management type combined power plant as described above,
in the process of optimizing parameter configuration by adopting the multi-target particle cluster intelligent algorithm, the multi-target particle cluster intelligent algorithm is improved by combining chaotic initialization, inertia weight self-adaptive adjustment, external archiving strategy updating and variation strategy, so that the optimal solution set of the optimization evaluation target improved by the algorithm is obtained.
Optionally, in the method for loop control mode multi-objective optimization of a thermal management type combined power plant, a specific process in step 3 includes:
step 31, initializing calculation parameters in the multi-target particle swarm algorithm, and setting a control target parameter and a constraint range of a boundary parameter of a device to be optimized;
step 32, initializing control target parameters of the heat management type combined power device in a ring control mode to form an initial population S (i);
step 33, calculating the fitness value of each individual particle in the population S (i) according to the fitness function, and preliminarily setting an individual extreme value Pbest and a population extreme value Gbest of the fitness value of each individual particle;
step 34, adjusting the inertia weight of the multi-target particle swarm algorithm, updating the speed and the position of the population based on the adjusted inertia weight, and generating a next generation population S (i + 1) by adopting a variation strategy;
step 35, calculating the fitness value of each individual particle in the updated population S (i + 1) according to the fitness function, and constructing a Pareto optimal front edge;
step 36, updating the original Pareto optimal front edge in the external archive by using the Pareto optimal front edge obtained in the current round, and adjusting the archive scale to ensure that the Pareto front edge is uniformly distributed;
step 37, comparing and updating the individual extremum Pbest and the group extremum Gbest in the S (i) through the individual extremum Pbest and the group extremum Gbest of the current S (i + 1);
step 38, if the maximum iteration number M is reached, the algorithm is ended, and the Pareto optimal leading edge updated for the last time is output; otherwise, the process returns to step 34.
Alternatively, in the method for loop control mode multi-objective optimization of a thermal management type combined power plant as described above, the step 32 includes:
adopting a chaos initialization strategy to randomly generate an N-dimensional vector Z, wherein the vector Z consists of N particles, each particle comprises 3 control target parameters, namely N x 3 vectors, the numerical value of each individual particle in the vector Z is a random numerical value in a range interval specified by the corresponding control target parameters, the numerical value is used for initializing the position of a particle swarm, the speed of the particle is randomly initialized, an initial population S (i) is obtained, and the initial iteration number i =0 is set; wherein, each individual particle in the vector Z is each target parameter in the vector Z.
Optionally, in the method for loop control mode multi-objective optimization of the thermal management type combined power plant, the method for constructing the Pareto optimal leading edge in step 35 is as follows:
if the population S (i + 1) does not have other particles Ni with each fitness value superior to that of the particle N1, the particle N1 is called a Pareto optimal solution, and a set formed by all the Pareto optimal solutions in the population S (i + 1) is called a Pareto optimal leading edge; here, if each fitness value of the particle N1 is better than that of the particle N2, the particle N1 is said to be better than the particle N2.
Optionally, in the method for loop control mode multi-objective optimization of a thermal management type combined power plant as described above, the updating manner in step 37 includes:
the mode of updating the group extreme value is that a particle is randomly selected from the Pareto optimal front edge to serve as the group extreme value Gbest;
the method for updating the individual extremum is to take the better particle in S (i + 1) as the individual extremum Pbest of the particle if the particle in S (i + 1) is better than the corresponding particle in S (i).
The invention has the beneficial effects that: the embodiment of the invention provides a multi-objective optimization method for a ring control mode of a heat management type combined power device, which has the following beneficial effects:
1) Aiming at the characteristics of multiple design parameters and complex boundary constraint of the operation state of the environment control mode of the heat management type combined power device, a simulation model suitable for the mode is established, and rapid simulation can be realized;
2) And establishing a multi-objective optimization evaluation method taking the energy utilization rate and the system stability as evaluation indexes and taking system constraints as boundary conditions. And (3) performing parameter configuration optimization on the thermal management type combined power device environment control mode simulation model by selecting a multi-target particle swarm optimization to obtain a multi-target optimal parameter solution set meeting the optimization evaluation method. The user can select the parameter result in the optimal solution set according to different requirements. Compared with the traditional method for configuring the parameters of the environment control mode, the method can improve the energy utilization rate and the system stability;
3) The calculation efficiency and the optimization effect are considered through the multi-target particle swarm algorithm, and the calculation cost is saved.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a schematic structural diagram of an environmental control mode of a thermal management type combined power plant adopted by a multi-objective optimization method of an environmental control mode of a thermal management type combined power plant according to an embodiment of the present invention;
FIG. 2 is a flow chart of a multi-objective particle swarm optimization algorithm in the environment-controlled mode multi-objective optimization method for the thermal management type combined power plant according to the embodiment of the invention;
fig. 3 is a schematic diagram of a Pareto front edge optimization solution set in the environment-controlled mode multi-objective optimization method of the thermal management type combined power device according to the embodiment of the present invention.
Description of reference numerals:
the system comprises a compressor 1, a power turbine 2, a refrigeration turbine 3, a combustion chamber 4, a generator 5, an air cooling heat exchanger 6, an air regeneration heat exchanger 7 and a liquid cooling heat exchanger 8.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
It has been described in the above background art that the parameter configuration form commonly used in the environment-controlled cooling mode of the current thermal management type combined power plant is to select several groups of typical design parameters in an expert experience manner, so as to meet the functional requirements and the device constraint conditions as the target, and compare and screen out the most suitable parameter configuration result, which has various problems.
The present invention provides that several embodiments may be combined, and that the same or similar concepts or processes may not be described in detail in connection with certain embodiments.
Aiming at various problems existing in commonly used parameter configuration of an environment-controlled cooling mode of the existing heat management type combined power device, the embodiment of the invention provides an environment-controlled mode multi-target optimization method of an airflow heat management type combined power device.
The environment control mode multi-objective optimization method for the airflow heat management type combined power device provided by the embodiment of the invention mainly comprises the following three parts: and constructing a simulation model of a ring control mode of the heat management type combined power device, a multi-target optimization evaluation strategy of the ring control mode of the device and a multi-target particle swarm optimization flow applicable to the ring control mode of the device.
The following describes the implementation of the above contents:
step one, constructing a ring control mode simulation model of a heat management type combined power device;
fig. 1 is a schematic structural diagram of a thermal management type combined power plant environment control mode adopted by the environment control mode multi-objective optimization method of the thermal management type combined power plant according to the embodiment of the present invention. As shown in fig. 1, the constituent elements of the thermal management type combined power plant include: the system comprises a compressor 1, a power turbine 2, a refrigeration turbine 3, a combustion chamber 4, a generator 5, an air cooling heat exchanger 6, an air regeneration heat exchanger 7 and a liquid cooling heat exchanger 8. Determining input and output parameters of each component in the thermal management type combined power device as follows:
input and output parameters of the gas compressor 1, the power turbine 2 and the refrigeration turbine 3 comprise gas flow, pressure, temperature and shaft rotation speed;
the input and output parameters of the combustion chamber 4 comprise gas flow, pressure, temperature and fuel flow;
the input and output parameters of the generator 5 comprise shaft rotation speed, input shaft work and generated energy;
the input and output parameters of the heat exchanger comprise cold/hot end flow, pressure and temperature, wherein the heat exchanger comprises 3 heat exchangers including an air cooling heat exchanger 6, an air regenerating heat exchanger 7 and a liquid cooling heat exchanger 8.
The method for constructing the environment control mode simulation model in the part comprises the following steps:
s1, modeling each component according to parameters of each component of the thermal management type combined power device to form a model of an individual component;
s2, establishing a balance equation of the heat management type combined power device in a ring control mode according to the model of the single component, wherein the balance equation comprises the following steps: obtaining interface continuous conditions of each component according to a hardware structure and a connection form of the device, calculating the convergence of an air circulation loop in the device, constructing a matching relation of coaxial components, and establishing a balance equation of the heat management type combined power device in an environment control mode; the compressor 1, the power turbine 2, the refrigeration turbine 3 and the generator 5 are coaxial components.
In S2, the interface continuity conditions of the respective components include:
1) The temperature and pressure parameters of the outlet of the gas compressor and the hot end inlet of the air cooling heat exchanger are equal, and the flow of the outlet of the gas compressor is equal to the sum of the flow of the hot end inlet of the air cooling heat exchanger and the flow of the bleed air supplement of the engine;
2) The hot end outlet of the air cooling heat exchanger and the hot end inlet of the air regeneration heat exchanger have equal temperature, flow and pressure parameters;
3) The temperature, flow and pressure parameters of the hot end outlet of the air regenerative heat exchanger and the inlet of the cooling turbine are equal;
4) The temperature and pressure parameters of the outlet of the cooling turbine and the hot end inlet of the liquid cooling heat exchanger are equal, and the flow of the outlet of the cooling turbine is equal to the sum of the flow of the hot end inlet of the liquid cooling heat exchanger and the flow of the cabin cooling air;
5) The temperature, flow and pressure parameters of the hot end outlet of the liquid cooling heat exchanger and the cold end inlet of the air regenerating heat exchanger are equal;
6) The temperature, flow and pressure parameters of the cold end outlet of the air regenerative heat exchanger and the inlet of the air compressor are equal;
7) The temperature, flow and pressure parameters of the outlet of the combustion chamber and the inlet of the power turbine are equal.
It should be noted that each of the above conditions establishes an equilibrium equation.
(2) Calculating the convergence of the air circulation loop means: the parameters of the inlet and the outlet of the compressor 1, the heat exchangers 6-8 and the cooling turbine 3 reach stable convergence after iterative calculation, and the convergence error of the parameters of all the parts is less than 0.001.
(3) The coaxial component matching relationship of the device is established in the following way: the shaft power of a rotor system consisting of the compressor 1, the power turbine 2, the cooling turbine 3 and the generator 5 is balanced, and the rotor system has no residual torque.
Step two, forming a multi-objective optimization evaluation strategy of the heat management type combined power device in the environment control mode according to the requirement of the heat management type combined power device in the environment control mode; the method for forming the multi-objective optimization evaluation strategy comprises the following steps:
s21, determining control target parameters of the thermal management type combined power device in the environment control mode, wherein the control target parameters comprise: the rotating speed of the shaft, the inlet pressure of the air compressor and the heat exchange quantity of the air cooling heat exchanger; each parameter has a preset range;
s22, determining the optimal evaluation targets of the heat management type combined power device in the environment control mode to be the bleed air flow of the generator and the stability margin of the air compressor, and establishing a functional relation between the optimal evaluation targets and control target parameters by combining the simulation model, namely a fitness function.
Thirdly, adopting a strategy execution optimization mode;
in the step, a multi-objective optimization evaluation strategy is specifically adopted to carry out multi-objective optimization on the heat management type combined power device in an environment control mode. In the execution of the step, a multi-target particle swarm algorithm with an external archiving mechanism based on Pareto is adopted by taking a ring control mode simulation model as an object to obtain an optimal solution set of an optimization evaluation target.
Specifically, in the process of optimizing parameter configuration by adopting the multi-target particle cluster intelligent algorithm, the multi-target particle cluster intelligent algorithm is improved by combining chaotic initialization, inertial weight self-adaptive adjustment, external archiving strategy updating and variation strategy, so that the optimal solution set of the optimization evaluation target improved by the algorithm is obtained.
As shown in fig. 2, a flowchart of a multi-objective particle swarm optimization algorithm in the environment-controlled mode multi-objective optimization method for the thermal management type combined power device according to the embodiment of the present invention is provided. The optimization process by adopting the multi-target particle swarm algorithm comprises the following steps of:
s31, initializing calculation parameters in the multi-target particle swarm algorithm, wherein the calculation parameters comprise a population scale N, learning factors c1 and c2, inertia weight ranges Wmax and Wmin, an external archive size Q, a variation rate u and a maximum iteration number M; and setting the control target parameters and the constraint range of the boundary parameters of the device to be optimized.
S32, initializing control target parameters of the thermal management type combined power device in a ring control mode;
in the step, a chaos initialization strategy is adopted, an N-dimensional vector Z is randomly generated (the vector Z is composed of N particles, each particle comprises 3 control target parameters, namely vectors of N x 3), the numerical value of each individual particle (namely each target parameter) in the vector Z is a random numerical value in a range interval specified by the corresponding control target parameter, the numerical value is used for initializing the position of a particle swarm, the speed of the particle is randomly initialized, an initial population S (i) is obtained, and the initial iteration number i =0 is set;
s33, calculating the fitness value of each individual particle in the population S (i) according to the fitness function, and preliminarily setting the individual extreme value Pbest and the population extreme value Gbest of the fitness value of each individual particle.
And S34, adjusting the inertia weight of the multi-target particle swarm algorithm, updating the speed and the position of the swarm based on the adjusted inertia weight, generating a next-generation swarm S (i + 1) by adopting a variation strategy, and updating a counter i = i +1.
And S35, calculating the fitness value of each individual particle in the updated population S (i + 1) according to the fitness function, and constructing a Pareto optimal front edge.
In the step, the method for constructing the Pareto optimal leading edge comprises the following steps: if the population S (i + 1) does not have other particles Ni with each fitness value superior to that of the particle N1, the particle N1 is called a Pareto optimal solution, and a set formed by all the Pareto optimal solutions in the population S (i + 1) is called a Pareto optimal leading edge; here, if each fitness value of the particle N1 is better than that of the particle N2, the particle N1 is said to be better than the particle N2. Fig. 3 is a schematic diagram of a Pareto front edge optimization solution set in the environment-controlled mode multi-objective optimization method of the thermal management type combined power plant according to the embodiment of the present invention.
And S36, updating the original Pareto optimal front edge in the external archive by using the Pareto optimal front edge obtained in the round, and adjusting the archive scale (wherein the archive scale has an upper limit, and the updated screened partial optimal solution is adopted), so that the Pareto front edge is uniformly distributed, and the Pareto front edge is prevented from being excessively concentrated in a certain local area.
And S37, comparing and updating the individual extremum Pbest and the group extremum Gbest in S (i) through the individual extremum Pbest and the group extremum Gbest in the current S (i + 1).
The updating mode in the step is as follows: the mode of updating the group extreme value is that a particle is randomly selected from the Pareto optimal front edge to serve as the group extreme value Gbest; the method for updating the individual extremum is to take the better particle in S (i + 1) as the individual extremum Pbest of the particle if the particle in S (i + 1) is better than the corresponding particle in S (i).
S38, if the maximum iteration number M is reached, the algorithm is ended, and the Pareto optimal front edge updated for the last time is output; otherwise, the process returns to step S34.
By adopting the multi-objective optimization method provided by the embodiment of the invention, the output Pareto optimal front edge is the optimal solution set of the thermal management type combined power device in the environment control mode, and a user can select parameter results in the optimal solution set according to different requirements.
The environment control mode multi-objective optimization method of the heat management type combined power device provided by the embodiment of the invention has the following beneficial effects:
1) Aiming at the characteristics of multiple design parameters and complex boundary constraint of the operation state of the environment control mode of the heat management type combined power device, a simulation model suitable for the mode is established, and rapid simulation can be realized;
2) And establishing a multi-objective optimization evaluation method taking the energy utilization rate and the system stability as evaluation indexes and taking system constraints as boundary conditions. And (3) performing parameter configuration optimization on the thermal management type combined power device environment control mode simulation model by using a multi-target particle swarm optimization to obtain a multi-target optimal parameter solution set meeting the optimization evaluation method. The user can select the parameter result in the optimal solution set according to different requirements. Compared with the traditional method for configuring the parameters of the environment control mode, the method can improve the energy utilization rate and the system stability;
3) The calculation efficiency and the optimization effect are considered through the multi-target particle swarm algorithm, and the calculation cost is saved.
The following schematically illustrates a specific implementation of the method for loop control mode multi-objective optimization of the thermal management type combined power plant according to an embodiment of the present invention by using an implementation example.
Examples of the implementation
Referring to figures 1 through 3 of the drawings,
this implementation example is directed to the thermal management type combined power plant environment control mode architecture shown in fig. 1, and the plant: the system comprises a compressor 1, a power turbine 2, a refrigeration turbine 3, a combustion chamber 4, a generator 5, an air cooling heat exchanger 6, an air regeneration heat exchanger 7 and a liquid cooling heat exchanger 8.
And performing mathematical modeling according to the characteristics of the selected parts such as the air compressor, the combustion chamber, the power turbine, the cooling turbine, the generator, the heat exchanger and the like.
After building each component model, building a steady-state common working equation of an environment control mode, calculating convergence and coaxial matching relation according to each component interface continuous condition and an air circulation system, and building each balance equation;
the interface continuity conditions of each component comprise:
1) The temperature and pressure parameters of the outlet of the gas compressor and the hot end inlet of the air cooling heat exchanger are equal, and the flow of the outlet of the gas compressor is equal to the sum of the flow of the hot end inlet of the air cooling heat exchanger and the flow of the bleed air supplement of the engine;
2) The hot end outlet of the air cooling heat exchanger and the hot end inlet of the air regeneration heat exchanger have equal temperature, flow and pressure parameters;
3) The temperature, flow and pressure parameters of the hot end outlet of the air regenerative heat exchanger and the inlet of the cooling turbine are equal;
4) The temperature and pressure parameters of the outlet of the cooling turbine and the hot end inlet of the liquid cooling heat exchanger are equal, and the flow of the outlet of the cooling turbine is equal to the sum of the flow of the hot end inlet of the liquid cooling heat exchanger and the flow of the cabin cooling air;
5) The temperature, flow and pressure parameters of the hot end outlet of the liquid cooling heat exchanger and the cold end inlet of the air regenerating heat exchanger are equal;
6) The temperature, flow and pressure parameters of the cold end outlet of the air regenerative heat exchanger and the inlet of the air compressor are equal;
7) The temperature, flow and pressure parameters of the outlet of the combustion chamber and the inlet of the power turbine are equal.
The calculation convergence of the air circulation system means that the inlet and outlet parameters of the air compressor, the heat exchanger and the cooling turbine reach stable convergence after iterative calculation, and the convergence error of each parameter is less than 0.001.
The coaxial matching equation establishment method of the device comprises the following steps: the shaft power of a rotor system consisting of the gas compressor, the power turbine, the cooling turbine and the generator is balanced, and the rotor system has no residual torque.
The multi-objective optimization evaluation method under the environment control mode is established, and comprises the following steps:
1) Determining the control targets of the heat management type combined power device in the environment control mode to be the rotating speed of a shaft, the inlet pressure of a gas compressor and the heat exchange quantity of an air cooling heat exchanger;
2) And determining the optimization evaluation target of the heat management type combined power device in the environmental control mode as the bleed air flow of the generator and the stability margin of the compressor, and establishing a functional relation between the optimization target and the control parameter, namely a fitness function, by combining the simulation model.
In this embodiment, a fitness function is constructed as follows:
Figure BDA0004024839470000131
in the formula: x is the number of 1 、x 2 、x 3 Control target parameters representing the model: the rotating speed of the shaft, the inlet pressure of the air compressor and the heat exchange quantity of the air cooling heat exchanger; c representing other input parameters of the model, e.g.The method comprises the following steps: ambient temperature, pressure; w represents the overall optimization evaluation objective of the thermal management type combined power plant in the environmental control mode, F represents an objective function of engine bleed air flow, H represents an objective function of compressor stability margin, and G represents boundary constraints of constraint parameters determined according to plant requirements, including, for example: refrigerating capacity and generating capacity of cabin, x i Min and x i Max denotes a search space range of the control target parameter.
The optimization steps in the thermal management type combined power device environment control mode can be deduced by combining the multi-target particle swarm optimization process, and the corresponding flow is shown in fig. 2.
The method comprises the following steps: setting the population size (namely the initial particle number is 200) and the maximum iteration number to be 100; the learning factor c1 is 1.5, c2 is 1.5; the variation range of the specified inertia weight is 0.2-0.8; an external archive size Q of 40; the variation rate is 0.5; the control target parameter search space range of the determination device is: the rotating speed of the shaft is 0.73-0.8, the inlet pressure of the air compressor is 120-300 kPa, and the heat exchange capacity of the air cooling heat exchanger is 100-200 kW; setting a constraint range of boundary parameters corresponding to the working condition of the normal-temperature day;
step two: randomly generating an n-dimensional vector Z by adopting a chaotic initialization strategy, wherein each individual particle value of the vector Z is a random value in a range interval corresponding to a control target parameter, and is used for initializing the position of a particle swarm, randomly initializing the speed of the particles to obtain an initial population S (i), and setting the initial iteration number i =0;
step three: and (3) calculating the fitness value of each individual particle in the population S (i) according to the fitness function, and initially setting the individual extreme value Pbest and the population extreme value Gbest of the fitness value of each individual particle.
Step four: and adjusting inertial weight according to an adaptive strategy, then updating the population speed and position, adopting a variation strategy to generate a next generation population S (i + 1), and updating a counter i = i +1.
Step five: calculating the fitness value of each individual particle in the updated population S (i + 1) according to the fitness function, when the constraint parameter of a certain particle exceeds the boundary constraint condition, executing a constraint punishment strategy on the individual particles which do not meet the condition according to the constraint condition to enable the fitness value to be the worst value, and then constructing a Pareto optimal front edge. The method for constructing the Pareto optimal leading edge comprises the following steps: if the population S (i + 1) does not have other particles Ni with each fitness value superior to that of the particle N1, the particle N1 is called a Pareto optimal solution, and a set formed by all the Pareto optimal solutions in the population S (i + 1) is called a Pareto optimal leading edge; among them, if each fitness value of the particle N1 is better than that of the particle N2, the particle N1 is said to be better than the particle N2, as shown in fig. 3.
Step six: and updating the external file, and adjusting the file scale to uniformly disperse the Pareto front edge distribution and avoid excessive concentration in a certain local area.
Step seven: and updating the individual extremum Pbest and the group extremum Gbest through comparing the individual optima with the global optima.
Step eight: if the iteration condition is met, the algorithm is ended, and a Pareto optimal leading edge is output, wherein a schematic diagram of the Pareto optimal leading edge is shown in FIG. 3; otherwise, returning to the step four.
And finally, obtaining an optimization result about the bleed air quantity of the engine and the stability margin of the air compressor, and obtaining the Pareto optimal front edge which is an optimal solution set under the current working condition of the environment control mode, wherein the optimization result is shown in the table 1. The user can select the parameter result in the optimal solution set according to different requirements.
TABLE 1 environment control mode multi-objective optimization table
Figure BDA0004024839470000151
Although the embodiments of the present invention have been described above, the present invention is not limited to the embodiments described above. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (8)

1. A multi-objective optimization method for a thermal management type combined power device in an environment control mode is characterized by comprising the following steps:
step 1, constructing a ring control mode simulation model of a heat management type combined power device;
step 2, forming a multi-objective optimization evaluation strategy of the heat management type combined power device in the environment control mode according to the requirement of the heat management type combined power device in the environment control mode; the method for forming the multi-objective optimization evaluation strategy comprises the following steps:
s21, determining control target parameters of the thermal management type combined power device in the environment control mode;
s22, determining an optimization evaluation target of the heat management type combined power device in the environment control mode, and establishing a fitness function between the optimization evaluation target and a control target parameter by combining a simulation model of the environment control mode;
and 3, performing multi-target optimization on the heat management type combined power device in a ring control mode by adopting a multi-target optimization evaluation strategy.
2. The method for multi-objective optimization in a loop control mode of a thermal management type combined power plant according to claim 1, wherein the step 1 comprises:
step 11, modeling each component according to parameters of each component of the thermal management type combined power device to form a model of an individual component;
step 12, establishing a balance equation of the thermal management type combined power device in a ring control mode according to the model of the single component, wherein the balance equation comprises the following steps: and obtaining interface continuous conditions of each component according to the hardware structure and the connection form of the device, calculating the convergence of an air circulation loop in the device, and constructing the matching relation of coaxial components.
3. The multi-objective optimization method for the environmental control mode of the thermal management type combined power plant according to claim 2, wherein the step 3 comprises the following steps:
and taking the environment control mode simulation model as an object, and obtaining an optimal solution set of an optimization evaluation target by adopting a Pareto-based multi-target particle swarm algorithm with an external archiving mechanism.
4. The multi-objective optimization method for environmental control mode of a thermal management type combined power plant according to claim 3,
in the process of optimizing parameter configuration by adopting the multi-target particle cluster intelligent algorithm, the multi-target particle cluster intelligent algorithm is improved by combining chaotic initialization, inertia weight self-adaptive adjustment, external archiving strategy updating and variation strategy, so that the optimal solution set of the optimization evaluation target improved by the algorithm is obtained.
5. The environment-control mode multi-objective optimization method of the thermal management type combined power plant according to claim 4, wherein the specific process of the step 3 comprises the following steps:
step 31, initializing calculation parameters in the multi-target particle swarm algorithm, and setting constraint ranges of control target parameters and boundary parameters of the device to be optimized;
step 32, initializing control target parameters of the heat management type combined power device in a ring control mode to form an initial population S (i);
step 33, calculating the fitness value of each individual particle in the population S (i) according to the fitness function, and preliminarily setting an individual extreme value Pbest and a population extreme value Gbest of the fitness value of each individual particle;
step 34, adjusting the inertia weight of the multi-target particle swarm algorithm, updating the speed and the position of the population based on the adjusted inertia weight, and generating a next generation population S (i + 1) by adopting a variation strategy;
step 35, calculating the fitness value of each individual particle in the updated population S (i + 1) according to the fitness function, and constructing a Pareto optimal front edge;
step 36, updating the original Pareto optimal front edge in the external archive by using the Pareto optimal front edge obtained in the current round, and adjusting the archive scale to ensure that the Pareto front edge is uniformly distributed;
step 37, comparing and updating the individual extremum Pbest and the group extremum Gbest in the S (i) through the individual extremum Pbest and the group extremum Gbest of the current S (i + 1);
step 38, if the maximum iteration number M is reached, the algorithm is ended, and the Pareto optimal leading edge updated for the last time is output; otherwise, the process returns to step 34.
6. The multi-objective optimization method for the environmental control mode of the thermal management type combined power plant according to claim 5, wherein the step 32 comprises:
adopting a chaos initialization strategy to randomly generate an N-dimensional vector Z, wherein the vector Z consists of N particles, each particle comprises 3 control target parameters, namely N x 3 vectors, the numerical value of each individual particle in the vector Z is a random numerical value in a range interval specified by the corresponding control target parameters, the numerical value is used for initializing the position of a particle swarm, the speed of the particle is randomly initialized, an initial population S (i) is obtained, and the initial iteration number i =0 is set; wherein, each individual particle in the vector Z is each target parameter in the vector Z.
7. The multi-objective optimization method for the environmental control mode of the thermal management type combined power plant according to claim 5, wherein the method for constructing the Pareto optimal leading edge in the step 35 is as follows:
if the population S (i + 1) does not have other particles Ni with each fitness value superior to that of the particle N1, the particle N1 is called a Pareto optimal solution, and a set formed by all the Pareto optimal solutions in the population S (i + 1) is called a Pareto optimal leading edge; among them, if each fitness value of the particle N1 is better than that of the particle N2, the particle N1 is said to be better than the particle N2.
8. The multi-objective optimization method for the environmental control mode of the thermal management type combined power plant according to claim 5, wherein the updating manner in the step 37 comprises the following steps:
the mode of updating the group extreme value is that a particle is randomly selected from the Pareto optimal front edge to serve as the group extreme value Gbest;
the method for updating the individual extremum is to take the better particle in S (i + 1) as the individual extremum Pbest of the particle if the particle in S (i + 1) is better than the corresponding particle in S (i).
CN202211701978.1A 2022-12-29 2022-12-29 Multi-objective optimization method for environment control mode of thermal management type combined power device Pending CN115859831A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114815600A (en) * 2022-03-25 2022-07-29 中国航空工业集团公司金城南京机电液压工程研究中心 Intelligent parameter optimization method for heat management type combined power device

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114815600A (en) * 2022-03-25 2022-07-29 中国航空工业集团公司金城南京机电液压工程研究中心 Intelligent parameter optimization method for heat management type combined power device
CN114815600B (en) * 2022-03-25 2024-12-24 中国航空工业集团公司金城南京机电液压工程研究中心 An intelligent parameter optimization method for a thermal management combined power unit

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