CN116859756A - Aviation comprehensive electromechanical system optimization model construction method and device - Google Patents
Aviation comprehensive electromechanical system optimization model construction method and device Download PDFInfo
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Abstract
The application provides a method and a device for constructing an optimization model of an aviation comprehensive electromechanical system, wherein an electromechanical system mathematical model can be constructed through a system architecture of the aviation comprehensive electromechanical system, a calculation formula of a system fuel compensation value is constructed, a sample is optimized through a preset optimal solution algorithm, a neural network model is constructed, and the optimized sample is used for training, so that the optimization model of the aviation comprehensive electromechanical system is obtained. Compared with the prior art, the optimization model of the aviation comprehensive electromechanical system obtained through training can realize the optimization of the aviation comprehensive electromechanical system by combining the optimal solution algorithm and the machine learning algorithm, the accuracy is high, and the problem of overlong calculation time of the conventional algorithm is solved.
Description
Technical Field
The application relates to the technical field of aviation electromechanics, in particular to a method and a device for constructing an optimization model of an aviation comprehensive electromechanical system.
Background
With the continuous improvement of the comprehensive performance of the aircraft and the continuous improvement of the power of electronic equipment, the optimization of the aircraft energy and the realization of the real-time management of the energy are the current research emphasis. The comprehensive electromechanical system is used as an important component of the aircraft, and the systems such as environmental control, auxiliary power, fuel oil and the like are designed in an integrated manner, so that the problem of large coupling degree of the systems is also brought, and the comprehensive electromechanical system is difficult to optimize and difficult to ensure the timeliness of the optimization.
The optimization methods commonly used in the existing aircraft electromechanical systems are divided into optimization methods based on expert knowledge and intelligent algorithms. The former relies on the knowledge and experience of expert, and has low accuracy; the latter is difficult to optimize in real time due to the long calculation time of the algorithm.
Disclosure of Invention
In view of the above, the technical problem solved by the embodiments of the present application is to provide a method and a device for constructing an optimization model of an avionics system, which are used to solve the problem that the optimization method of an avionics system in the prior art depends on knowledge and experience to cause serious accident, or the problem that the algorithm used has a long calculation period to cause the timeliness of the optimization method cannot be guaranteed.
The first aspect of the embodiment of the application discloses a method for constructing an optimization model of an aviation comprehensive electromechanical system, which comprises the following steps: constructing an electromechanical system mathematical model according to the system architecture of the aviation comprehensive electromechanical system; the electromechanical system mathematical model is used for calculating and obtaining an engine air entraining value, a fuel consumption value, an evaporation circulation electricity consumption value and a motor electricity consumption value according to flight state data, control data of the aviation comprehensive electromechanical system, power source driving form data of a power device and cooling mode data of the aviation comprehensive electromechanical system;
Constructing a calculation formula of a system fuel compensation value according to the engine bleed air value, the fuel consumption value, the evaporation cycle power consumption value and the motor power consumption value;
according to the flight state data corresponding to a plurality of working points to be optimized in the full flight envelope working condition and the electromechanical system mathematical model, taking the minimum value of the system fuel compensation value as an optimization target, and calculating and obtaining control data of the aviation comprehensive electromechanical system, power source driving form data of the power device and cooling mode data of the aviation comprehensive electromechanical system corresponding to each working point to be optimized by using a preset optimal solution algorithm;
constructing a neural network model, wherein the input of the neural network model comprises the flight state data, and the output of the neural network model comprises control data of the aviation comprehensive electromechanical system, power source driving form data of the power device and cooling mode data of the aviation comprehensive electromechanical system;
and training the neural network model by using the flight state data, the control data of the aviation comprehensive electromechanical system, the power source driving form data of the power device and the cooling form data of the aviation comprehensive electromechanical system corresponding to all the working points to be optimized to obtain an optimization model of the aviation comprehensive electromechanical system.
The second aspect of the embodiment of the application discloses an aviation comprehensive electromechanical system optimization model construction device, which comprises: the first model building module is used for building an electromechanical system mathematical model according to the system architecture of the aviation comprehensive electromechanical system; the electromechanical system mathematical model is used for calculating and obtaining an engine air entraining value, a fuel consumption value, an evaporation circulation electricity consumption value and a motor electricity consumption value according to flight state data, control data of the aviation comprehensive electromechanical system, power source driving form data of a power device and cooling mode data of the aviation comprehensive electromechanical system;
the formula construction module is used for constructing a calculation formula of a system fuel compensation value according to the engine bleed air value, the fuel consumption value, the evaporation cycle power consumption value and the motor power consumption value;
the optimal solution calculation module is used for calculating and obtaining control data of the aviation comprehensive electromechanical system, power source driving form data of the power device and cooling mode data of the aviation comprehensive electromechanical system corresponding to each working condition point to be optimized by using a preset optimal solution algorithm according to the flight state data corresponding to a plurality of working condition points to be optimized in a full flight envelope working condition and the electromechanical system mathematical model, wherein the minimum value of the system fuel oil compensation value is taken as an optimization target;
The second model building module is used for building a neural network model, the input of the neural network model comprises the flight state data, and the output of the neural network model comprises control data of the aviation comprehensive electromechanical system, power source driving form data of the power device and cooling mode data of the aviation comprehensive electromechanical system;
and the machine learning module is used for training the neural network model by utilizing the flight state data, the control data of the aviation comprehensive electromechanical system, the power source driving form data of the power device and the cooling mode data of the aviation comprehensive electromechanical system corresponding to all the working points to be optimized to obtain an optimization model of the aviation comprehensive electromechanical system.
According to the embodiment of the invention, firstly, an electromechanical system mathematical model is constructed through a system architecture of the aviation comprehensive electromechanical system, a calculation formula of a system fuel compensation value is constructed, a sample is sampled and optimized through a preset optimal solution algorithm, a neural network model is constructed, and the optimized sample is used for training, so that an optimization model of the aviation comprehensive electromechanical system is obtained. Compared with the prior art, the optimization model of the aviation comprehensive electromechanical system obtained through training can realize the optimization of the aviation comprehensive electromechanical system by combining the optimal solution algorithm and the machine learning algorithm, the accuracy is high, and the problem of overlong calculation time of the conventional algorithm is solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of a method for constructing an optimization model of an avionics integrated system according to an example of the present application;
FIG. 2 is a schematic flow chart of a method for constructing an optimization model of an avionics integrated system according to example II of the present application;
fig. 3 is a schematic block diagram of an optimization model construction device of an avionics integrated system according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "first," "second," and "third," etc. in the description and claims of the present application are used for distinguishing between different objects and not for describing a particular sequential order. The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
Example one
FIG. 1 is a schematic flow chart of an optimization model construction method for an avionics integrated system according to an embodiment of the present application, which includes:
step S101, constructing an electromechanical system mathematical model according to the system architecture of the aviation comprehensive electromechanical system.
In this embodiment, the electromechanical system mathematical model is used to calculate and obtain the engine bleed air value, the fuel consumption value, the evaporation cycle power consumption value and the motor power consumption value according to the flight state data, the control data of the aviation comprehensive electromechanical system, the power source driving form data of the power device, and the cooling mode data of the aviation comprehensive electromechanical system.
The flight state data are used for representing state related parameter values of the aircraft loaded with the aviation comprehensive electromechanical system at a specific time point, the specific parameter types are not limited, and reasonable selection can be carried out according to practical application requirements. For example, the flight status data may be used to characterize at least one of a flight altitude value, an engine bleed air temperature value, an engine bleed air pressure value, and a cooling device heat dissipation power value.
The control data of the avionics system is used to characterize the control-related parameter values of the avionics system at a particular point in time. The specific parameters are not limited in variety and can be reasonably selected according to actual application requirements. For example, control data for an avionics integrated system may characterize at least one of a system power plant rotational speed value, a pre-compressor pressure value.
The driving form data of the power source of the power device is used for representing a specific time point, the driving form of the power source of the aviation comprehensive electromechanical system is not limited, and the driving form is reasonably selected according to actual application requirements. For example, the drive profile data of the avionics power source may characterize at least one of electrical energy drive, engine bleed air drive, and bleed air combustion drive.
The cooling mode data of the aviation comprehensive electromechanical system is used for representing a specific time point, the specific cooling mode of the aviation comprehensive electromechanical system is not limited, and the cooling mode data can be reasonably selected according to actual application requirements. For example, cooling pattern data of the avionics system may characterize at least one of a fan bypass heat exchange method, a fuel heat sink heat exchange method.
Step S102, a calculation formula of the fuel compensation value of the system is constructed according to the engine bleed air value, the fuel consumption value, the evaporation cycle power consumption value and the motor power consumption value.
In this embodiment, the system fuel compensation value refers to the additional loss of the performance of the aircraft caused by the adverse effect of installing and using the integrated electromechanical system of the aircraft on the performance of the aircraft, and can be used as a main index for evaluating the advantages and disadvantages of the integrated electromechanical system of the aircraft.
Alternatively, for simple and accurate calculation of the system fuel compensation value, it may be preferable that the calculation formula of the system fuel compensation value may be:
，
wherein, the liquid crystal display device comprises a liquid crystal display device,the bleed air amount of the engine; />A value of fuel consumption per bleed air amount for the selected engine; / >Is fuel consumption; />The power consumption for the evaporation cycle; />The electric quantity is used for the motor;the value of fuel consumption per unit amount of electricity used for the selected engine.
And step S103, calculating and obtaining control data of the aviation comprehensive electromechanical system, power source driving form data of the power device and cooling mode data of the aviation comprehensive electromechanical system corresponding to each working point to be optimized by using a preset optimal solution algorithm according to flight state data corresponding to a plurality of working points to be optimized in the full flight envelope working condition and an electromechanical system mathematical model, wherein the minimum value of the system fuel compensation value is taken as an optimization target.
In this embodiment, since it is difficult to collect the flight state data of all the working points, and meanwhile, efficiency and practical application requirements are considered, a plurality of working points to be optimized need to be selected from the full flight envelope working conditions of the aircraft to collect the corresponding flight state data, and the specific number is not limited.
In this embodiment, a sampling method is used to select flight state data corresponding to a plurality of to-be-optimized operating points in the full-flight envelope operating mode, and the specific sampling method is not limited, for example, the sampling method may be selected to be at least one of a random sampling method and a latin super-vertical method according to actual application requirements.
In this embodiment, since one main index for evaluating the avionics integrated system is the system fuel compensation value, and the lower the system fuel compensation value is, the better the system performance is, so the minimum value of the system fuel compensation value is selected as the optimization target of the optimal solution algorithm. The specific optimization objective function formula is not limited, and for example, the optimization objective function may be:
，
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the optimization objective of minimum fuel compensation value of the system, +.>Inputting information for flight status->、/>、/>、/>Four control parameters of the integrated electromechanical system, in particular +.>Is the rotation speed of the power device>Is the front pressure of the compressor, < >>Opening degree of bypass valve of bypass heat exchanger for fan and/or the like>Is the opening degree of a bypass valve of the liquid cooling heat exchanger, +.>For the currently selected power source drive form, +.>Is the currently selected cooling mode.
After the acquisition of the flight state data is completed, the electromechanical system mathematical model is obtained, and the optimization target is determined, a preset optimal solution algorithm can be used for solving, so that the engine bleed air value, the fuel consumption value, the evaporation cycle power consumption and the motor power consumption value corresponding to each working condition point to be optimized are obtained. The output values are main parameters for calculating the fuel compensation value of the system, so that control data of the aviation comprehensive electromechanical system, power source driving form data of the power device and cooling mode data of the aviation comprehensive electromechanical system can be realized when the fuel compensation value of the system corresponding to each working point to be optimized is minimum.
The specific preset optimal solution algorithm may be selected according to actual application requirements, for example: genetic algorithms, simulated annealing algorithms, ant colony algorithms, and the like.
And S104, constructing a neural network model.
In this embodiment, the input of the neural network model includes flight state data, and the output of the neural network model includes control data of the avionics integrated system, power source driving form data of the power device, and cooling form data of the avionics integrated system.
The specific method for constructing the neural network model is not limited, and may be constructed based on at least one of a pyrerch frame and a TensorFlow frame, for example.
Step S105, training the neural network model by using flight state data corresponding to all working points to be optimized, control data of the aviation comprehensive electromechanical system, power source driving form data of the power device and cooling mode data of the aviation comprehensive electromechanical system to obtain an optimization model of the aviation comprehensive electromechanical system.
In this embodiment, the data set including the flight state data, the control data of the avionics system, the power source driving form data of the power device, and the cooling mode data of the avionics system is divided into a training data set and a testing data set, and the training data set and the testing data set are used for training the neural network model and performing the functional test, so as to obtain the trained optimization model of the avionics system. For example, the representation mode of the trained optimization model of the avionics integrated system may be:
，
Wherein the method comprises the steps of、/>、/>、/>Respectively representing four control parameters of the integrated electromechanical system,in particular +.>Indicating the rotation speed of the power device, < >>Representing the compressor front pressure, & lt & gt>Represents the opening degree of a bypass valve of the fan bypass heat exchanger and the +.>Indicating the opening of the bypass valve of the liquid-cooled heat exchanger, +.>Representing the currently selected power source drive form, +.>Representing the currently selected cooling mode, wherein ∈>All represent flight status data, in particular +.>Representing fly height values, ">Representing the bleed air temperature value of the engine,/->Representing the bleed air pressure value of the engine,/->Indicating the cooling device heat dissipation power value.
The specific proportion of the data set split into the training data set and the test data set is not limited, and the data set can be reasonably selected according to actual application requirements. For example, a training dataset may be 7 members and a test dataset 3 members; the training data set may be 9 times, the test data set may be 1 time, etc.
As can be seen from the above embodiments of the present invention, in the embodiments of the present invention, firstly, a mathematical model of an avionics system is constructed through a system architecture of the avionics system, an engine bleed air value, a fuel consumption value, an evaporation cycle power consumption value and a motor power consumption value are calculated, a calculation formula of a system fuel compensation value is constructed, flight state data corresponding to a to-be-optimized operating point, which can be used for training, corresponding control data of the avionics system, power source driving form data of a power device, and cooling mode data of the avionics system are obtained by sampling and optimizing a sample through a preset optimal solution algorithm, a neural network model is constructed, and training is performed, so as to obtain an optimized model of the avionics system. Compared with the prior art, the optimization model of the aviation comprehensive electromechanical system obtained through training can be used for optimizing the aviation comprehensive electromechanical system by combining the optimal solution algorithm and the machine learning algorithm, the accuracy is high, and meanwhile the problem that the calculation time of a conventional algorithm is too long is solved.
Example two
Fig. 2 is a schematic flow chart of a method for constructing an optimization model of an avionics integrated system according to a second embodiment of the present application, where the method includes:
step S201, constructing an electromechanical system mathematical model according to the system architecture of the aviation comprehensive electromechanical system.
In this embodiment, the electromechanical system mathematical model is used to calculate and obtain the engine bleed air value, the fuel consumption value, the evaporation cycle power consumption value and the motor power consumption value according to the flight state data, the control data of the aviation comprehensive electromechanical system, the power source driving form data of the power device, and the cooling mode data of the aviation comprehensive electromechanical system.
Optionally, the avionics system includes a power plant, a semi-closed air circulation system, a liquid cooling system, an evaporative circulation system, a fuel system, and a cooling device.
In addition, the cooling mode of the aviation comprehensive electromechanical system comprises fan duct heat exchange and fuel oil heat sink heat exchange, the fan duct heat exchanger is provided with a first bypass valve, and the liquid cooling heat exchanger is provided with a second bypass valve.
In addition, the control parameters of the aviation comprehensive electromechanical system comprise the rotating speed of the power device, the front pressure of the compressor, the opening degree of the first bypass valve and the opening degree of the second bypass valve.
The selection is to select the components of the aviation comprehensive electromechanical system according to the actual application requirements of realizing the optimization model of the aviation comprehensive electromechanical system, and only select the relevant parameters of the components to construct the mathematical model of the electromechanical system, so that the calculation amount of a subsequent optimal solution algorithm is reduced.
Optionally, the flight status data includes a flight altitude value, an engine bleed air temperature value, an engine bleed air pressure value, and a cooling device heat dissipation power value.
In order to obtain the optimization model of the aviation comprehensive electromechanical system, the flight state data need to cover a plurality of important parameters when the aircraft runs, and the necessary data are selected to form the flight state data, so that an important reference basis is provided for the optimization of the aviation comprehensive electromechanical system, and meanwhile, the calculation amount of a follow-up optimal solution algorithm is reduced.
Step S202, a calculation formula of the fuel compensation value of the system is constructed according to the engine bleed air value, the fuel consumption value, the evaporation cycle power consumption value and the motor power consumption value.
In this embodiment, the step S202 is substantially the same as or similar to the step S102 in the first embodiment, and will not be described herein.
And step S203, according to flight state data corresponding to a plurality of working points to be optimized in the full flight envelope working condition and an electromechanical system mathematical model, taking the minimum value of the system fuel compensation value as an optimization target, and calculating and obtaining control data of the aviation comprehensive electromechanical system, power source driving form data of the power device and cooling mode data of the aviation comprehensive electromechanical system corresponding to each working point to be optimized by utilizing a preset optimal solution algorithm.
Optionally, according to flight state data corresponding to a plurality of working points to be optimized in the full flight envelope working condition and an electromechanical system mathematical model, determining the minimum value of the system fuel compensation value as an optimization target, determining the mode function or performance requirement of the aviation comprehensive electromechanical system as a constraint condition, and calculating and obtaining control data of the aviation comprehensive electromechanical system, power source driving form data of a power device and cooling mode data of the aviation comprehensive electromechanical system corresponding to each working point to be optimized by using a preset optimal solution algorithm.
The method comprises the steps of determining the optimization constraint condition of the aviation comprehensive electromechanical system to meet the function or performance requirement of a current mode, ensuring that the aviation comprehensive electromechanical system can normally operate and meet the function requirement, and simultaneously enabling the calculation result of an optimal solution algorithm to be more accurate and reliable.
Further, the constraint condition further comprises that the heat exchange temperature of the fuel heat sink does not exceed a first limit value, the exhaust temperature of the power turbine does not exceed a second limit value, and the bleed air amount of the engine does not exceed a third limit value.
When the aircraft electromechanical system is ensured to meet the function or performance requirement of the current mode, the specific optimization objective function and the constraint condition thereof are not limited, for example:
，
Wherein, the liquid crystal display device comprises a liquid crystal display device,the minimum fuel compensation value of the system is used as an optimization target.
、/>、/>Respectively represents the heat exchange temperature of the fuel heat sink>Is set to a first limit value for power turbine exhaust temperatureSecond limit value of (2) and engine bleed air quantity +.>Is set to a third limit value of (c). According to practical application requirements, the first limit value may preferably be 80 ℃, the second limit value may preferably be 700 ℃, and the third limit value may preferably be 1kg/s. />And->The upper limit and the lower limit of each optimized measurement value are indicated, and the specific values of the upper limit and the lower limit are set according to actual conditions, which is not limited in this embodiment.
The constraint conditions enable the operation of the aviation comprehensive electromechanical system to be better guaranteed, and the accuracy of the calculation result of the optimal solution algorithm is further enhanced.
Optionally, when the flight state data are acquired, the Latin hypercube method is utilized to acquire the flight state data corresponding to a plurality of working condition points to be optimized in the full flight envelope working condition.
The Latin hypercube sampling method is adopted to obtain the flight state data when the flight state information is obtained, and the Latin hypercube sampling method is different from the traditional random sampling method, can obtain a better optimizing effect by using fewer sampling points, obtains a working condition set of points to be optimized, and ensures the reliability of the calculation result of an optimal solution algorithm.
Further, the density of the working condition points to be optimized selected in the stages of taking off, fight and landing of the aircraft is higher than that of the other stages.
During the whole working process of the aircraft, the running condition of the comprehensive electromechanical system of the aircraft needs to be focused when the aircraft is in the take-off, fight and landing stage, so that more working condition points to be optimized need to be selected in the take-off, fight and landing stage of the aircraft, and the reliability of the calculation result of the optimal solution algorithm is improved.
Optionally, the preset optimal solution algorithm is a particle swarm optimization algorithm.
The practical application requirement of the system requires that the selection of the preset optimal solution algorithm is to consider factors such as calculation cost, calculation time length and the like, and the particle swarm optimization algorithm is adopted, so that the problems of high calculation cost, long time and the like of a common algorithm can be solved, and the algorithm is simple, convenient to calculate and high in solving speed.
Further, step S203 may include:
selecting one working point of a plurality of working points to be optimized, and optimizing by adopting a particle swarm intelligent algorithm.
Firstly, selecting optimized particles, initializing the particles, and obtaining an initialized particle swarm by adopting a chaotic initialization strategy.
The optimized particles may be classified into continuous optimized particles and discrete optimized particles, specifically, a selection result of a cooling mode and a selection result of a power source driving mode may be set as the discrete optimized particles, and a rotation speed value of the power device, a front pressure value of the compressor, a bypass valve opening value, and the like may be set as the continuous optimized particles. In addition, the selection result of the cooling mode and the selection result of the power source driving mode can be represented by letters, numbers or the like, and the specific representation mode is not limited, for example A, B and the like, and for example 1, 2 and the like. The bypass valve opening value is used to characterize the value of the first bypass valve opening data and the value of the second bypass valve opening data.
And substituting the obtained initialized particle swarm into an optimization objective function to obtain the fitness value of the particles, screening out the particles which do not meet the constraint conditions according to the optimization objective function, and selecting global optimal particles and local optimal particles from the rest particles which can meet the constraint conditions according to the fitness value corresponding to the particles. And then, the result is brought into a particle swarm motion equation, the inertia weight is adjusted according to the self-adaptive strategy, the speed and the position of the population are updated, and the variation strategy is adopted to generate the next generation population.
And finally, setting the maximum iteration times as a standard for judging whether the algorithm is ended, and outputting global optimal particles after the algorithm is ended to obtain an optimization result of the current working condition. If the iteration times of the judgment algorithm do not reach the set numerical value, repeating the previous steps to continuously obtain a mutated population; if the iteration number of the algorithm is judged to reach the maximum iteration number, the algorithm is ended, and the optimization flow is ended.
And S204, constructing a neural network model.
In this embodiment, the input of the neural network model includes flight state data, and the output of the neural network model includes control data of the avionics integrated system, power source driving form data of the power device, and cooling form data of the avionics integrated system.
In this embodiment, the step S204 is substantially the same as or similar to the step S104 in the first embodiment, and will not be described herein.
Step S205, training the neural network model by using flight state data corresponding to all working points to be optimized, control data of the aviation comprehensive electromechanical system, power source driving form data of the power device and cooling mode data of the aviation comprehensive electromechanical system to obtain an optimization model of the aviation comprehensive electromechanical system.
In this embodiment, the step S205 is substantially the same as or similar to the step S105 in the first embodiment, and will not be described herein.
Step S206, acquiring real-time flight state data according to a preset time interval value.
In this embodiment, because the flight status data of the aircraft is continuously updated during the working process, in order to optimize the energy of the avionics system in real time, the flight status data may be periodically collected according to a preset time interval value.
The preset time interval value is not particularly limited, and can be reasonably selected according to actual application requirements. For example, it may be 1s, 2s, 3s, etc.
Step S207, according to the real-time flight state data, the control data optimization target value, the power source driving form data optimization target value and the cooling mode data optimization target value are obtained by utilizing an optimization model of the aviation comprehensive electromechanical system.
In this embodiment, the obtained real-time flight state data is used as input, and the optimization model of the avionics integrated system gives corresponding calculation results, including a control data optimization target value, a power source driving form data optimization target value, and a cooling mode data optimization target value, where the above values are optimization target parameter values of the avionics integrated system, and can be used as reference data in subsequent system optimization.
And step S208, optimizing the aviation comprehensive electromechanical system in real time according to the control data optimization target value, the power source driving form data optimization target value and the cooling mode data optimization target value.
In this embodiment, after the optimized target value is obtained, an optimized scheme may be obtained, that is, the system may adjust the state of the avionics system according to the control data optimized target value, the power source driving form data optimized target value, and the cooling form data optimized target value.
As can be seen from the above embodiments of the present invention, after the optimization model of the avionics integrated system is obtained, the embodiment of the present invention can obtain real-time flight state data according to a preset time interval value, and obtain the optimization target values of each parameter by using the optimization model of the avionics integrated system and perform optimization, so as to perform real-time optimization adjustment on the working state of the avionics integrated system.
Example three
As shown in fig. 3, fig. 3 is a schematic block diagram of a device for constructing an optimization model of an avionics integrated system according to a third embodiment of the present application, where the device includes:
the first model building module 301 is configured to build a mathematical model of an electromechanical system according to a system architecture of the avionics integrated system.
In this embodiment, the electromechanical system mathematical model is used to calculate and obtain the engine bleed air value, the fuel consumption value, the evaporation cycle power consumption value and the motor power consumption value according to the flight state data, the control data of the aviation comprehensive electromechanical system, the power source driving form data of the power device, and the cooling mode data of the aviation comprehensive electromechanical system.
Wherein the flight status data is used to characterize the status-related parameter values of the aircraft carrying the avionics system at a particular point in time. The first model building module 301 is configured to obtain a flight altitude value, an engine bleed air temperature value, an engine bleed air pressure value, and a cooling device heat dissipation power value.
Wherein the control data of the avionics system is used to characterize the control-related parameter values of the avionics system at a particular point in time. The first model building module 301 is configured to obtain control parameters of an avionics integrated system, including a power unit rotation speed, a compressor front pressure, a first bypass valve opening, and a second bypass valve opening.
The power source driving form data of the power device are used for representing the driving form of the power source of the aviation comprehensive electromechanical system at a specific time point. For example, the drive profile data of the avionics power source may characterize at least one of electrical energy drive, engine bleed air drive, and bleed air combustion drive. The first model building module 301 is configured to obtain power source driving form data of a power device of the avionics system.
The cooling mode data of the aviation comprehensive electromechanical system are used for representing the cooling mode of the aviation comprehensive electromechanical system at a specific time point. For example, cooling pattern data of the avionics system may characterize at least one of a fan bypass heat exchange method, a fuel heat sink heat exchange method. The first model building module 301 is configured to obtain cooling mode data of the avionics integrated system.
The first model construction module 301 builds an electromechanical system mathematical model for calculating the obtained engine bleed air value, fuel consumption value, evaporation cycle power consumption value and motor power consumption value based on the obtained above relevant parameter values.
The formula construction module 302 is configured to construct a calculation formula of the system fuel compensation value according to the engine bleed air value, the fuel consumption value, the evaporation cycle power consumption value and the motor power consumption value.
In this embodiment, the system fuel compensation value refers to the additional loss of the performance of the aircraft caused by the adverse effect of installing and using the integrated electromechanical system of the aircraft, and can be used as a main index for evaluating the advantages and disadvantages of the integrated electromechanical system of the aircraft. The formula construction module 302 selects appropriate parameters based on the actual application scenario, and can construct a corresponding system fuel compensation value formula.
The optimal solution calculation module 303 is configured to calculate, according to flight state data corresponding to a plurality of to-be-optimized operating points in the full flight envelope operating condition and an electromechanical system mathematical model, a minimum value of a system fuel compensation value as an optimization target, and obtain control data of an aviation comprehensive electromechanical system, power source driving form data of a power device, and cooling form data of the aviation comprehensive electromechanical system corresponding to each to-be-optimized operating point by using a preset optimal solution algorithm.
In this embodiment, when acquiring the flight status data, the optimal solution calculation module 303 is configured to determine the selection densities of the to-be-optimized operating points at different stages in the full flight stage according to the actual requirements, complete the selection of a plurality of to-be-optimized operating points in the full flight envelope operating mode, complete the sampling, and finally obtain the flight status data.
The density of the selected working condition points to be optimized in the stage of taking off, fighting and landing of the aircraft is higher than that in other stages.
The sampling method uses Latin hypercube sampling method to obtain flight state data corresponding to a plurality of working points to be optimized in the full flight envelope working conditions.
In this embodiment, the optimal solution calculation module 303 is configured to determine an objective function of an optimal solution algorithm, obtain a system fuel compensation value formula, and use a minimum fuel compensation value of the system as an optimization target.
In this embodiment, the optimal solution calculation module 303 is configured to determine a constraint condition of an optimal solution algorithm, and use a value of a related parameter to ensure that an aircraft electromechanical system can meet a function or performance requirement of a current mode as a standard for constructing the constraint condition.
In this embodiment, the optimal solution calculation module 303 is configured to determine that a specific optimal solution algorithm is a particle swarm optimization algorithm, and perform optimal solution calculation to obtain control data of an avionics system, power source driving form data of a power device, and cooling mode data of the avionics system corresponding to each working point to be optimized.
The second model building module 304 is configured to build a neural network model, where an input of the neural network model includes flight state data, and an output of the neural network model includes control data of the avionics system, power source driving form data of the power device, and cooling mode data of the avionics system.
In this embodiment, the second model building module 304 is configured to determine an input amount and an output amount of the neural network model; the specific method for constructing the neural network model is determined, and for example, the neural network model can be constructed based on at least one of a PyTorch framework and a TensorFlow framework.
The machine learning module 305 is configured to train the neural network model by using flight state data corresponding to all working points to be optimized, control data of the avionics system, power source driving form data of the power device, and cooling form data of the avionics system, so as to obtain an optimized model of the avionics system.
In this embodiment, the machine learning module 305 is configured to reasonably split the data set available for training learning into a training data set and a test data set according to a corresponding proportion, train the neural network model already constructed by using the training data set, obtain a trained neural network model, and then test the model by using the test data set, so as to finally obtain the available optimization model of the avionics comprehensive electromechanical system.
Optionally, the optimal solution calculation module 303 is further configured to determine, according to flight state data corresponding to a plurality of to-be-optimized operating points in the full flight envelope operating condition and an electromechanical system mathematical model, that a minimum value of a system fuel oil compensation value is determined as an optimization target, determine a mode function or performance requirement of the avionics system as a constraint condition, and calculate and obtain control data of the avionics system, power source driving form data of the power device, and cooling mode data of the avionics system corresponding to each to-be-optimized operating point by using a preset optimal solution algorithm.
Further, the optimal solution calculation module 303 is configured to determine the constraint condition further includes that the fuel heat sink heat exchange temperature value does not exceed the first limit value, the power turbine exhaust temperature value does not exceed the second limit value, and the engine bleed air value does not exceed the third limit value.
Optionally, the optimal solution calculation module 303 is further configured to obtain flight state data corresponding to a plurality of to-be-optimized operating points in the full flight envelope operating mode by using latin hypercube method.
Further, the density of the working condition points to be optimized selected in the stages of taking off, fight and landing of the aircraft is higher than that of the other stages.
Optionally, the flight status data includes a flight altitude value, an engine bleed air temperature value, an engine bleed air pressure value, and a cooling device heat dissipation power value.
Optionally, the avionics system includes a power plant, a semi-closed air circulation system, a liquid cooling system, an evaporative circulation system, a fuel system, and a cooling device.
The power source drive forms of the power plant include electric power drive, engine bleed air drive and bleed air combustion drive.
The cooling mode of the aviation comprehensive electromechanical system comprises fan duct heat exchange and fuel oil heat sink heat exchange, the fan duct heat exchanger is provided with a first bypass valve, and the liquid cooling heat exchanger is provided with a second bypass valve.
Correspondingly, the control data of the aviation comprehensive electromechanical system comprises power plant rotating speed data, compressor front pressure data, first bypass valve opening data and second bypass valve opening data.
Optionally, the optimal solution calculation module 303 is further configured to preset an optimal solution algorithm to be a particle swarm optimization algorithm.
Optionally, the device further comprises a timing acquisition module, which is used for acquiring real-time flight state data according to a preset time interval value.
And according to the real-time flight state data, obtaining a control data optimization target value, a power source driving form data optimization target value and a cooling mode data optimization target value by utilizing an optimization model of the aviation comprehensive electromechanical system.
And optimizing the energy of the aviation comprehensive electromechanical system in real time according to the control data optimizing target value, the power source driving form data optimizing target value and the cooling mode data optimizing target value.
Through the device for constructing the optimization model of the aviation comprehensive electromechanical system, the method for constructing the optimization model of the aviation comprehensive electromechanical system in the method embodiments can be realized, and the device has the beneficial effects of the corresponding method embodiments, and specific beneficial effects are not repeated here.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of the present application, should be included in the scope of the claims of the present application.
Claims (10)
1. The method for constructing the optimization model of the aviation comprehensive electromechanical system is characterized by comprising the following steps of:
constructing an electromechanical system mathematical model according to the system architecture of the aviation comprehensive electromechanical system; the electromechanical system mathematical model is used for calculating and obtaining an engine air entraining value, a fuel consumption value, an evaporation circulation electricity consumption value and a motor electricity consumption value according to flight state data, control data of the aviation comprehensive electromechanical system, power source driving form data of a power device and cooling mode data of the aviation comprehensive electromechanical system;
constructing a calculation formula of a system fuel compensation value according to the engine bleed air value, the fuel consumption value, the evaporation cycle power consumption value and the motor power consumption value;
according to the flight state data corresponding to a plurality of working points to be optimized in the full flight envelope working condition and the electromechanical system mathematical model, taking the minimum value of the system fuel compensation value as an optimization target, and calculating and obtaining control data of the aviation comprehensive electromechanical system, power source driving form data of the power device and cooling mode data of the aviation comprehensive electromechanical system corresponding to each working point to be optimized by using a preset optimal solution algorithm;
Constructing a neural network model, wherein the input of the neural network model comprises the flight state data, and the output of the neural network model comprises control data of the aviation comprehensive electromechanical system, power source driving form data of the power device and cooling mode data of the aviation comprehensive electromechanical system;
and training the neural network model by using the flight state data, the control data of the aviation comprehensive electromechanical system, the power source driving form data of the power device and the cooling form data of the aviation comprehensive electromechanical system corresponding to all the working points to be optimized to obtain an optimization model of the aviation comprehensive electromechanical system.
2. The method according to claim 1, wherein the calculating, according to the flight state data and the electromechanical system mathematical model corresponding to the plurality of operating points to be optimized in the full flight envelope operating mode, the minimum value of the system fuel compensation value as an optimization target, using a preset optimal solution algorithm to obtain the control data of the avionics system, the power source driving form data of the power device, and the cooling mode data of the avionics system corresponding to each operating point to be optimized includes:
And according to the flight state data corresponding to the working points to be optimized in the full flight envelope working condition and the electromechanical system mathematical model, determining the minimum value of the system fuel compensation value as an optimization target, determining the mode function or performance requirement of the aviation comprehensive electromechanical system as a constraint condition, and calculating by using a preset optimal solution algorithm to obtain control data of the aviation comprehensive electromechanical system, power source driving form data of the power device and cooling mode data of the aviation comprehensive electromechanical system corresponding to each working point to be optimized.
3. The method of claim 2, wherein the electromechanical system mathematical model is further used for calculating and obtaining a fuel heat sink heat exchange temperature value, a power turbine exhaust temperature value and an engine bleed air value according to flight state data, control data of the avionics system, power source driving form data of the power plant and cooling mode data of the avionics system;
correspondingly, the constraint condition further comprises that the fuel heat sink heat exchange temperature value does not exceed a first limit value, the power turbine exhaust temperature value does not exceed a second limit value, and the engine bleed air value does not exceed a third limit value.
4. The method according to claim 1, wherein the method further comprises:
and obtaining the flight state data corresponding to the plurality of working condition points to be optimized in the full flight envelope working condition by using a Latin hypercube method.
5. The method of claim 4, wherein the density of the operating points to be optimized selected during the take-off, combat, landing phases of the aircraft is greater than the density of the other phases.
6. The method of claim 1, wherein the flight status data includes a flight altitude value, an engine bleed air temperature value, an engine bleed air pressure value, a cooling device heat dissipation power value.
7. The method of claim 1, wherein the system architecture of the avionics integrated system is:
the aviation comprehensive electromechanical system comprises a power device, a semi-closed air circulation system, a liquid cooling system, an evaporation circulation system, a fuel system and cooling equipment;
the power source driving mode of the power device comprises electric energy driving, engine air entraining driving and air entraining combustion driving;
the cooling mode of the aviation comprehensive electromechanical system comprises fan bypass heat exchange and fuel oil heat sink heat exchange, the fan bypass heat exchanger is provided with a first bypass valve, and the liquid cooling heat exchanger is provided with a second bypass valve;
Correspondingly, the control data of the aviation comprehensive electromechanical system comprise power plant rotating speed data, compressor front pressure data, first bypass valve opening data and second bypass valve opening data.
8. The method of claim 1, wherein the predetermined optimal solution algorithm is a particle swarm optimization algorithm.
9. The method according to claim 1, wherein the method further comprises:
acquiring real-time flight state data according to a preset time interval value;
according to the real-time flight state data, utilizing an optimization model of the aviation comprehensive electromechanical system to obtain a control data optimization target value, a power source driving form data optimization target value and a cooling form data optimization target value;
and optimizing the energy of the aviation comprehensive electromechanical system in real time according to the control data optimization target value, the power source driving form data optimization target value and the cooling mode data optimization target value.
10. An apparatus for constructing an optimization model of an avionics integrated system, the apparatus comprising:
the first model building module is used for building an electromechanical system mathematical model according to the system architecture of the aviation comprehensive electromechanical system; the electromechanical system mathematical model is used for calculating and obtaining an engine air entraining value, a fuel consumption value, an evaporation circulation electricity consumption value and a motor electricity consumption value according to flight state data, control data of the aviation comprehensive electromechanical system, power source driving form data of a power device and cooling mode data of the aviation comprehensive electromechanical system;
The formula construction module is used for constructing a calculation formula of a system fuel compensation value according to the engine bleed air value, the fuel consumption value, the evaporation cycle power consumption value and the motor power consumption value;
the optimal solution calculation module is used for calculating and obtaining control data of the aviation comprehensive electromechanical system, power source driving form data of the power device and cooling mode data of the aviation comprehensive electromechanical system corresponding to each working condition point to be optimized by using a preset optimal solution algorithm according to the flight state data corresponding to a plurality of working condition points to be optimized in a full flight envelope working condition and the electromechanical system mathematical model, wherein the minimum value of the system fuel oil compensation value is taken as an optimization target;
the second model building module is used for building a neural network model, the input of the neural network model comprises the flight state data, and the output of the neural network model comprises control data of the aviation comprehensive electromechanical system, power source driving form data of the power device and cooling mode data of the aviation comprehensive electromechanical system;
and the machine learning module is used for training the neural network model by utilizing the flight state data, the control data of the aviation comprehensive electromechanical system, the power source driving form data of the power device and the cooling mode data of the aviation comprehensive electromechanical system corresponding to all the working points to be optimized to obtain an optimization model of the aviation comprehensive electromechanical system.
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