CN116910923A - Optimization design method and device for airborne electromechanical system - Google Patents
Optimization design method and device for airborne electromechanical system Download PDFInfo
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Abstract
The application relates to an optimization design method and device of an onboard electromechanical system, wherein the method comprises the following steps: constructing a simulation model of the airborne electromechanical system, wherein the simulation model of the airborne electromechanical system is used for calculating and obtaining an operation performance parameter value and a self characteristic parameter value of the airborne electromechanical system according to a design parameter value of the airborne electromechanical system; determining a fuel consumption calculation formula of the airborne electromechanical system in the full mission stage of the aircraft, wherein the fuel consumption calculation formula is used for calculating and obtaining a fuel consumption value of the airborne electromechanical system according to a flight state parameter value of the aircraft, an operation performance parameter value and a self characteristic parameter value of the airborne electromechanical system; and taking the minimum value of the fuel consumption value of the onboard electromechanical system as an optimization target, and calculating by using a simulation model and a preset optimal solution algorithm to obtain the optimal design parameter value of the onboard electromechanical system. Compared with the prior art, the application can shorten the optimization time of the design parameters of the electromechanical system and improve the optimization accuracy.
Description
Technical Field
The application relates to the technical field of aviation, in particular to an optimal design method and device of an onboard electromechanical system.
Background
The on-board electromechanical system is a key functional system necessary for ensuring the flight safety and task execution of the aircraft, and mainly comprises typical systems such as hydraulic system, fuel oil system, environmental control system, auxiliary/emergency power system, electric power system and the like, wherein the weight of the system is about 30% of the total weight of the duty cycle aircraft, the number of parts is about 60% of the total weight of the whole aircraft, the energy utilization efficiency is less than 30%, and the energy consumption level directly influences the range, the endurance and the economy of the aircraft.
Conventional on-board electromechanical systems require energy extraction from the engine, the amount of power extracted will directly affect the operational capabilities of the engine. Unlike ground equipment, on-board electromechanical systems need to carry weight with the lift provided by the propulsion of the engine, that is, the greater the weight of the on-board electromechanical system, the greater the lift and thrust requirements on the aircraft and the engine. The performance assessment and optimization design of the onboard electro-mechanical system will directly impact the aircraft platform capability level.
In the prior art, the performance level of the electromechanical system on the aircraft is often evaluated through the index of the power ratio, namely, products with larger power and lighter weight are designed under the guidance of design requirements, and the design method is designed from the capability attribute of the products, but the influence of the system efficiency and the weight on the aircraft is not considered, so that the problems that the performance of the electromechanical product reaches the standard but the performance level is not high can occur, and therefore, the optimization design work based on the performance optimization is needed to start at the beginning of the design of the electromechanical system on the aircraft.
Disclosure of Invention
In view of the above, an embodiment of the present application provides a performance evaluation and optimization design method for an electromechanical system of an aircraft, which solves a problem that the performance of the electromechanical system of the aircraft is low due to insufficient optimization of design parameters of the electromechanical system of the aircraft in the prior art.
The first aspect of the embodiment of the application discloses an optimization design method of an airborne electromechanical system, which is characterized by comprising the following steps:
constructing a simulation model of an airborne electromechanical system, wherein the simulation model of the airborne electromechanical system is used for calculating and obtaining an operation performance parameter value and a self characteristic parameter value of the airborne electromechanical system according to a design parameter value of the airborne electromechanical system;
determining a fuel consumption calculation formula of the onboard electromechanical system in a full mission stage of the aircraft, wherein the fuel consumption calculation formula is used for calculating and obtaining a fuel consumption value of the onboard electromechanical system according to an aircraft flight state parameter value, the operation performance parameter value and the self characteristic parameter value of the onboard electromechanical system;
and taking the minimum value of the fuel consumption value of the airborne electromechanical system as an optimization target, and calculating by using the simulation model and a preset optimal solution algorithm to obtain an optimal design parameter value of the airborne electromechanical system.
A second aspect of the embodiment of the present application discloses an apparatus for optimally designing an electromechanical system of an aircraft, which is characterized in that the apparatus includes:
the system comprises a model construction module, a model analysis module and a model analysis module, wherein the model construction module is used for constructing a simulation model of an airborne electromechanical system, and the simulation model of the airborne electromechanical system is used for calculating and obtaining an operation performance parameter value and a self characteristic parameter value of the airborne electromechanical system according to a design parameter value of the airborne electromechanical system;
the fuel consumption calculation module is used for determining a fuel consumption calculation formula of the airborne electromechanical system in the full mission stage of the aircraft, wherein the fuel consumption calculation formula is used for calculating and obtaining a fuel consumption value of the airborne electromechanical system according to the aircraft flight state parameter value, the running performance parameter value and the self characteristic parameter value of the airborne electromechanical system;
and the parameter value optimization module is used for taking the minimum value of the fuel consumption value of the airborne electromechanical system as an optimization target, and calculating and obtaining the optimal design parameter value of the airborne electromechanical system by utilizing the simulation model and a preset optimal solution algorithm.
According to the embodiment of the application, firstly, a simulation model is constructed by utilizing design parameters and simulation environment of the airborne electromechanical system, then, a fuel consumption calculation formula of the airborne electromechanical system in the whole mission stage of the aircraft is determined, the minimum fuel consumption is taken as an optimization target, and the optimal design parameter value of the airborne electromechanical system is obtained by utilizing the simulation model and a preset optimal solution algorithm. Compared with the prior art, when the optimization design method of the airborne electromechanical system constructed by the embodiment of the application is used for optimizing the design parameters of the airborne electromechanical system, the technical effects of comprehensive parameter optimization, selection of optimization targets as required and accurate optimization parameter values can be achieved.
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 flow chart of a method of optimizing design of an on-board electromechanical system in accordance with an embodiment of the present application;
FIG. 2 is a flow chart of a method of optimizing design of an onboard electro-mechanical system disclosed in example two of the present application;
fig. 3 is a block diagram of an apparatus for optimizing an electromechanical system of an aircraft 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 "comprises" and "comprising," along with any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, apparatus, article, or device 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 device, but may include other steps or elements not expressly listed or inherent to such process, method, article, or device.
Example one
Referring to fig. 1, fig. 1 is a flowchart of a method for optimizing design of an electromechanical system on board according to an embodiment of the present application, where the method includes:
s101, constructing a simulation model of the airborne electromechanical system.
In this embodiment, the simulation model of the airborne electromechanical system is used to calculate and obtain the operation performance parameter value and the self-feature parameter value of the airborne electromechanical system according to the design parameter value of the airborne electromechanical system.
In this embodiment, the design parameters of the onboard electromechanical system are parameters that need to be optimized, and the specific parameters are not limited in kind and can be reasonably selected according to actual application requirements. For example, the size, location, etc. of the on-board electromechanical system may be included.
In this embodiment, the operation performance parameter is used to characterize the operation state of the electromechanical system of the vehicle, and the specific parameter is not limited, and may include output power, efficiency, and the like, for example.
In this embodiment, the self-characteristic parameters are used to characterize the characteristic-related states of the relevant components of the electromechanical system of the vehicle, and in order to reduce the subsequent calculation time, specific parameter types may be determined according to expert experience, and parameters that may directly or indirectly affect the performance index of the electromechanical system of the vehicle are generally selected. The specific parameters included in the self characteristic parameters are not limited in types, and can be reasonably selected according to actual application requirements. For example, at least one of volume, weight, length, width may be included.
Optionally, considering that the design of the on-board power plant generally mainly includes the design of three performance components of the compressor, the combustion chamber and the power turbine, in order to obtain better design effect and reduce the data processing capacity, it may be preferable that the design parameters of the on-board electromechanical system include at least the design parameters of the compressor, the design parameters of the combustion chamber and the design parameters of the power turbine; the operation performance parameters of the airborne electromechanical system at least comprise the operation performance parameters of the air compressor, the operation performance parameters of the combustion chamber and the operation performance parameters of the power turbine; the self-characterizing parameters of the on-board electromechanical system include at least the total volume and/or weight of the self-characterizing parameters of the compressor, the power turbine and the combustion chamber.
Further, in order to obtain more accurate calculation results, it may be preferable that for impeller rotating components such as a compressor and a turbine, corresponding design parameters include a guide blade root, a guide blade tip, a moving blade root, a design interface installation angle in the moving blade and the moving blade tip, an inlet and outlet geometric angle, a front and rear edge wedge angle, a back bending angle and a front edge diameter; the corresponding operational performance parameters include flow, efficiency, power, reaction, absolute outlet Mach number, relative outlet Mach number, absolute outlet airflow angle, and relative outlet airflow angle; the corresponding self-characterizing parameters include volume and/or weight.
For the combustion chamber, the corresponding design parameters may preferably include ignition position, atomization angle, inlet and outlet area, combustion chamber size; the corresponding operating performance parameters include outlet temperature uniformity, combustion efficiency, and outlet Mach number; the corresponding self-characteristic parameters include volume and weight.
In this embodiment, software used for constructing the simulation model is not limited, and may be reasonably selected according to actual application requirements. For example, ansys, CATIA, UG, etc. may be mentioned.
S102, determining a fuel consumption calculation formula of the onboard electromechanical system in the full mission stage of the aircraft.
In this embodiment, the fuel consumption calculation formula is used for calculating and obtaining the fuel consumption value of the on-board electromechanical system according to the aircraft flight state parameter value, the operation performance parameter value of the on-board electromechanical system and the self characteristic parameter value. In the practical application process, the aircraft flight state parameter value, the operation performance parameter value of the onboard electromechanical system and the self characteristic parameter value all correspond to the value of the same time point.
The aircraft flight state parameter value is used for representing the flight state of the aircraft at a specific time point, the specific flight state parameter is not limited in type, and the aircraft flight state parameter value can be reasonably selected according to actual application requirements. For example, it may be altitude, aerodynamic mass, speed of flight, etc.
The operation performance parameter value of the airborne electromechanical system is used for representing the operation state of the airborne electromechanical system at a specific time point, the specific operation performance parameter type of the airborne electromechanical system is not limited, and the operation performance parameter value can be reasonably selected according to practical application requirements. For example, it may be a compressor inlet temperature, a turbine inlet temperature, output power, etc.
The self-characteristic parameter value is used for representing the attribute of the onboard electromechanical system, the specific self-characteristic parameter is not limited in type, and reasonable selection can be carried out according to actual application requirements. For example, it may be volume, weight, etc.
Optionally, the fuel consumption calculation formula is:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,
k represents the aerodynamic mass of the aircraft, i.e. the lift-to-drag ratio;
represents the specific fuel consumption of the engine in +.>；
The unit heat value of fuel combustion is expressed as J/kg;
the inlet temperature of the compressor is represented by K;
the inlet temperature of the turbine is expressed in K;
indicating the fuel combustion completion factor in the combustion chamber;
representing the compressor compression ratio of the compressor;
p represents output shaft power, and the unit is W;
the bleed air flow is expressed in kg/s;
d represents momentum resistance caused by bleed air, and the unit is N;
representing the intermediate stage increase ratio of the compressor;
representing the final stage increasing ratio of the compressor;
representing the inherent mass of the on-board electromechanical systems device;
representing an initial weight of the onboard electro-mechanical system carrying a variable weight;
indicating the time that the on-board electromechanical system is running.
The fuel consumption calculation formula of the whole airborne electromechanical system is obtained by respectively calculating and summing fuel consumption in five aspects of carrying self weight, carrying variable weight, overcoming air resistance, extracting mechanical power from an engine and entraining air from the engine, and comprehensively considering the fuel consumption mode of the airborne electromechanical system, so that the calculation result is more accurate.
In addition, the fuel consumption rate and the lift-drag ratio of the aircraft in different flight envelopes are changed severely, meanwhile, the working time and the working state of different airborne electromechanical systems in different envelopes are changed greatly, and the fuel consumption calculation formula calculates the fuel consumption in a differential mode, so that the calculation result of the fuel consumption value of the airborne electromechanical systems is more accurate and objective.
In this embodiment, the execution sequence of step S101 and step S102 is not limited, and may be reasonably selected according to the actual application requirement.
And S103, taking the minimum value of the fuel consumption value of the onboard electromechanical system as an optimization target, and calculating by using a simulation model and a preset optimal solution algorithm to obtain the optimal design parameter value of the onboard electromechanical system.
In this embodiment, in order to reasonably optimize the onboard electromechanical system, an optimal solution algorithm may be used to calculate and obtain an optimal design parameter value of the onboard electromechanical system. The specific types of the preset optimal solution algorithm are not limited, and the specific types can be reasonably selected according to actual application requirements. For example, the preset optimal solution algorithm may be one of a genetic algorithm, a particle swarm algorithm, and a gradient descent method.
In this embodiment, the optimal design parameter value of the on-board electromechanical system is a recommended value of the design parameter of the on-board electromechanical system obtained by calculation in consideration of the fuel consumption value.
Optionally, taking the minimum value of the fuel consumption value of the onboard electromechanical system as an optimization target, taking the operation performance limit value and the self characteristic limit value of the onboard electromechanical system as constraint conditions, and calculating to obtain the optimal design parameter value of the onboard electromechanical system by using a simulation model and a preset optimal solution algorithm.
The operation performance limit value of the onboard electromechanical system and the corresponding parameter type and specific value of the own characteristic limit value are not limited, and can be reasonably selected according to practical application requirements. By setting a limit value for the running performance and the self characteristic value of the onboard electromechanical system, the optimal solution algorithm can be constrained, invalid optimization results are avoided, and the optimization results have practicability.
For example, when the operation performance of the on-board electromechanical system is the ground output power and the air output power, the limit value corresponding to the ground output power may be set to 150kW, and the limit value corresponding to the air output power is 80kW, that is, the ground output power is greater than or equal to 150kW, and the air output power P is greater than or equal to 80kW; when the onboard electromechanical system is characterized by the total weight of three performance components, namely the compressor, the combustion chamber and the power turbine, the limit value corresponding to the total weight can be set to be 40kg, namely the total weight is less than or equal to 40kg.
Further, it may be preferable that the preset optimal solution algorithm is a genetic algorithm.
The genetic algorithm avoids the algorithm to be in local optimum through a mutation mechanism, has strong searching capability, and has randomness in individual selection due to the probability thought in natural selection, so that more favorable design parameter values are reserved as much as possible. And the genetic algorithm can be expanded through other constraint conditions and can be used in combination with other algorithms.
Furthermore, considering the actual application situation of the onboard electromechanical system, in order to improve the calculation efficiency under the premise of ensuring the accuracy of the calculation result, the population scale m=1600 may be optimized, the population offspring is cultivated by adopting the discrete recombination function recdis, the crossover rate pc=1, the uniform variation form is adopted, and the variation rate pm=0.01. And for better saving of computation time, it may be preferable to iterate the termination evolution algebra t=100.
Further, different penalty functions are selected for individuals with different deviation constraints. The design constraint condition and the design target calculation of the onboard electromechanical system are nonlinear, so that constraint and target composition auxiliary functions are processed in a mode of improving a genetic algorithm, constraint problems are converted into unconstrained problems by adopting a penalty function, and the optimization design can be performed more quickly and effectively.
Wherein the auxiliary function is defined as:
；
furthermore, in order to ensure accurate calculation, different penalty functions are selected for individuals with different deviation constraints, so that the genetic algorithm is improved:
；
as can be seen from the above embodiments of the present application, the present embodiment constructs a simulation model of an onboard electromechanical system; determining a fuel consumption calculation formula of an onboard electromechanical system in the full mission stage of the aircraft; and taking the minimum value of the fuel consumption value of the onboard electromechanical system as an optimization target, and calculating by using a simulation model and a preset optimal solution algorithm to obtain the optimal design parameter value of the onboard electromechanical system. Compared with the prior art, the method and the device can shorten the optimization time of the design parameters of the electromechanical system and improve the optimization accuracy.
Example two
As shown in fig. 2, fig. 2 is a flowchart of an optimization design method of an electromechanical system on board according to a second embodiment of the present application, where the method includes:
s201, constructing a simulation model of the onboard electromechanical system.
In this embodiment, the step S201 is substantially the same as or similar to the step S101 in the first embodiment, and will not be described herein.
S202, determining a fuel consumption calculation formula of the onboard electromechanical system in the full mission stage of the aircraft.
In this embodiment, the step S201 is substantially the same as or similar to the step S101 in the first embodiment, and will not be described herein.
S203, calculating a plurality of groups of design parameter values of the airborne electromechanical system by using the simulation model to obtain operation performance parameter values and self-characteristic parameter values corresponding to each group of design parameter values.
In this embodiment, in order to obtain the training set required by the machine learning algorithm, a plurality of sets of design parameter values, and corresponding running performance parameter values and self-feature parameter values may be calculated in advance.
The selection of the multiple groups of design parameter values and the mode of inputting the simulation model are not limited, and the selection can be reasonably performed according to actual application requirements. For example, when the Ansys is used to construct a simulation model of the onboard electromechanical system, the grid automatic generation function under the Ansys can be utilized to link the flow design method, and the operation performance parameter values and the self-feature parameter values corresponding to the multiple groups of design parameter values can be automatically calculated.
In addition, the design parameter value and the corresponding running performance parameter value and the number of the groups of the self characteristic parameter values are not limited, and can be reasonably selected according to actual application requirements. For example, any number from the group 100-1000 may be used.
Optionally, in order to implement automated and procedural calculation of the massive design parameters, it may be preferable that the software that constructs the simulation model has a grid auto-generation function, and that different design parameter values of the onboard electro-mechanical system may be automatically transferred to the simulation model. After the design parameters and the value interval of the airborne electromechanical system are determined, corresponding simulation models of different design parameters can be generated through the grid automatic generation function of the software.
Optionally, in order to ensure the accuracy of the calculation result of the dimension reduction model trained later, reduce the data processing amount, and consider the actual design and operation situation of the onboard electromechanical system, the number of the sets of the design parameter value and the corresponding operation performance parameter value and the self-feature parameter value may be preferably 500.
S204, learning all design parameter values, corresponding operation performance parameter values and self characteristic parameter values by using a preset machine learning algorithm to obtain a dimension reduction model.
In this embodiment, the dimension reduction model is a model obtained by learning, by a preset machine learning algorithm, a plurality of sets of operation performance parameter values and self performance characteristic parameter values obtained by calculating a plurality of sets of design parameter values and corresponding design parameter values thereof, and simplifying an original simulation model after obtaining a corresponding relation between a calculation result and the design parameter values.
In this embodiment, the type of the algorithm model of the machine learning algorithm is not limited, and may be reasonably selected according to the actual application requirement. For example, may include, but is not limited to: linear regression algorithms, logistic regression algorithms, neural network algorithms, etc.
Alternatively, the preset machine learning algorithm may be preferably a multi-layer BP neural network algorithm. The BP neural network algorithm is a multi-layer feedforward network trained according to an error back propagation algorithm, the model can be automatically learned through the back propagation algorithm, the multi-layer multi-node BP neural network algorithm is utilized to reduce the dimension of the simulation model, and multiple groups of data are introduced to learn the simulation model in different dimensions, so that the dimension-reduced model is greatly simplified under the condition that the simulation result of the original complex simulation model is hardly changed, and the optimal solution calculation time of the final airborne electromechanical system is further reduced.
Further, considering the actual design and operation of the on-board electromechanical system, it may be preferable that step S204 includes: and learning all design parameter values, corresponding operation performance parameter values and self characteristic parameter values by using a multi-layer BP neural network algorithm in a five-layer full-connection 128-node mode to obtain a dimension reduction model.
Optionally, in order to improve the accuracy of the subsequent calculation using the dimension reduction model, when learning all the design parameter values and the corresponding running performance parameter values and self-feature parameter values by using a preset machine learning algorithm, a bidirectional training manner is preferably adopted, that is, the mapping relationship between the design parameter values and the running performance parameter values and the self-feature parameter values, and the mapping relationship between the running performance parameter values and the self-feature parameter values and the design parameter values are trained respectively.
Further, in order to ensure the accuracy of the subsequent calculation using the dimension reduction model, it may be preferable that 90% of the design parameter values of all the groups and the corresponding operation performance parameter values and self-feature parameter values thereof have data as a training set, 10% of the data as a test set, and the calculation accuracy is not lower than 99%.
And S205, taking the minimum value of the fuel consumption value of the onboard electromechanical system as an optimization target, and calculating by using the dimension reduction model and a preset optimal solution algorithm to obtain the optimal design parameter value of the onboard electromechanical system.
In this embodiment, compared with step 103 in the foregoing embodiment, the difference is that the dimension reduction model is used to replace the simulation model, so that the data operand can be reduced, and the time consumption of the whole optimal solution calculation process is greatly shortened. According to the embodiment of the application, the dimension reduction model is calculated in a machine learning mode, and the dimension reduction model is utilized to calculate the optimal solution, so that the time of the simulation process is greatly reduced, and the overall optimal solution calculation time is reduced. The dimension reduction mode of the simulation model is further preferably BP neural network algorithm, and the dimension reduction can be carried out on the simulation model by combining a multi-node multi-layer structure and a back propagation algorithm, so that the obtained dimension reduction model is closer to the simulation result of the original complex simulation model, the structure is simpler, and the optimal solution calculation time of the final airborne electromechanical system is reduced.
Example three
As shown in fig. 3, fig. 3 is a schematic structural diagram of an optimization design device for an on-board electromechanical system according to a third embodiment of the present application, where the device is used for optimizing design parameters of the on-board electromechanical system, and includes:
the model construction module 301 is configured to construct a simulation model of the on-board electrical system. The simulation model of the airborne electromechanical system is used for obtaining the operation performance parameter value and the self characteristic parameter value of the airborne electromechanical system through calculation according to the design parameter value of the airborne electromechanical system.
Optionally, the design parameters of the on-board electromechanical system include at least the design parameters of the compressor, the design parameters of the combustor, and the design parameters of the power turbine; the operation performance parameters of the airborne electromechanical system at least comprise the operation performance parameters of the air compressor, the operation performance parameters of the combustion chamber and the operation performance parameters of the power turbine; the self-characterizing parameters of the on-board power system include at least the total volume and/or weight of the self-characterizing parameters of the compressor, the power turbine and the combustion chamber.
Optionally, design parameters of the compressor and the power turbine comprise guide blade root, guide blade top and moving blade root, design interface installation angles of the moving blade and the moving blade top, inlet and outlet geometric angles, front and rear edge wedge angles, back bending angles and front edge diameters; the design parameters of the combustion chamber comprise ignition position, atomization angle, inlet and outlet areas and sizes;
the operational performance parameters of the compressor and the power turbine include flow, efficiency, power, reaction, absolute outlet mach number, relative outlet mach number, absolute outlet airflow angle, and relative outlet airflow angle; the operating performance parameters of the combustion chamber include outlet temperature uniformity, combustion efficiency, and outlet Mach number;
the compressor, power turbine and combustor's own characteristic parameters include volume and/or weight.
The fuel consumption calculation module 302 is configured to determine a fuel consumption calculation formula for the on-board electromechanical system. The fuel consumption calculation formula is used for calculating and obtaining the fuel consumption value of the airborne electromechanical system according to the aircraft flight state parameter value, the running performance parameter value and the self characteristic parameter value of the airborne electromechanical system.
Optionally, the fuel consumption calculation formula is:
；
wherein K represents the aerodynamic mass of the aircraft;representing the fuel consumption of the engine; />A unit calorific value representing combustion of fuel; />Representing the inlet temperature of the compressor; />Representing turbine inlet temperature; />Indicating the fuel combustion completion factor in the combustion chamber; />Representing the compressor compression ratio of the compressor; p represents the output shaft power; />Representing bleed air flow; d represents the momentum resistance caused by bleed air; />Representing the intermediate stage increase ratio of the compressor; />Representing the final stage increasing ratio of the compressor; />Representing the inherent mass of the on-board electromechanical systems device; />Representing an initial weight of the onboard electro-mechanical system carrying a variable weight; />Indicating the time that the on-board electromechanical system is running.
The parameter value optimizing module 303 is configured to take the minimum value of the fuel consumption value of the on-board electromechanical system as an optimization target. And calculating to obtain the optimal design parameter value of the on-board electromechanical system by using the simulation model and a preset optimal solution algorithm.
Optionally, the device for optimally designing the electromechanical system of the vehicle further comprises:
the parameter calculation module is used for calculating a plurality of groups of design parameter values of the airborne electromechanical system by using the simulation model to obtain operation performance parameter values and self-characteristic parameter values corresponding to each group of design parameter values;
the dimension reduction processing module is used for learning all design parameter values, corresponding operation performance parameter values and self characteristic parameter values by using a preset machine learning algorithm to obtain a dimension reduction model;
correspondingly, the parameter value optimizing module 303 is further configured to calculate, with the minimum value of the fuel consumption value of the onboard electro-mechanical system as an optimization target, an optimal design parameter value of the onboard electro-mechanical system by using the dimension reduction model and a preset optimal solution algorithm.
Optionally, the preset machine learning algorithm is a multi-layer BP neural network algorithm, and correspondingly, the dimension reduction processing module is further used for learning all design parameter values, corresponding operation performance parameter values and self characteristic parameter values by adopting a five-layer full-connection 128-node form and utilizing the multi-layer BP neural network algorithm to obtain a dimension reduction model.
Optionally, the parameter value optimizing module 303 is further configured to calculate, with the minimum value of the fuel consumption value of the on-board electromechanical system as an optimization target, an optimal design parameter value of the on-board electromechanical system by using the dimension-reduction model and a preset optimal solution algorithm, where the calculating includes:
and taking the minimum value of the fuel consumption value of the airborne electromechanical system as an optimization target, taking the operation performance limit value and the self characteristic limit value of the airborne electromechanical system as constraint conditions, and calculating by using a simulation model and a preset optimal solution algorithm module to obtain the optimal design parameter value of the airborne electromechanical system.
Correspondingly, the preset optimal solution algorithm module is a genetic algorithm module.
Optionally, taking the minimum value of the fuel consumption value of the onboard electromechanical system as an optimization target, taking the operation performance limit value and the self characteristic limit value of the onboard electromechanical system as constraint conditions, and calculating the optimal design parameter value of the onboard electromechanical system by using the simulation model and the preset optimal solution algorithm module comprises the following steps:
and selecting different penalty function modules for individuals with different deviation constraint conditions.
Through the device for optimally designing the onboard electromechanical system of the embodiment, the corresponding method for constructing the front-band phenomenon alarm model 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. Thus, specific embodiments of the present application have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer storage media (including, but not limited to, magnetic disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
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, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. An optimization design method for an onboard electromechanical system, which is characterized by comprising the following steps:
constructing a simulation model of an airborne electromechanical system, wherein the simulation model of the airborne electromechanical system is used for calculating and obtaining an operation performance parameter value and a self characteristic parameter value of the airborne electromechanical system according to a design parameter value of the airborne electromechanical system;
determining a fuel consumption calculation formula of the onboard electromechanical system in a full mission stage of the aircraft, wherein the fuel consumption calculation formula is used for calculating and obtaining a fuel consumption value of the onboard electromechanical system according to an aircraft flight state parameter value, the operation performance parameter value and the self characteristic parameter value of the onboard electromechanical system;
and taking the minimum value of the fuel consumption value of the airborne electromechanical system as an optimization target, and calculating by using the simulation model and a preset optimal solution algorithm to obtain an optimal design parameter value of the airborne electromechanical system.
2. The method according to claim 1, wherein the method further comprises:
calculating a plurality of groups of design parameter values of the airborne electromechanical system by using the simulation model to obtain the running performance parameter value and the self characteristic parameter value corresponding to each group of design parameter values;
learning all the design parameter values, the corresponding running performance parameter values and the corresponding self characteristic parameter values by using a preset machine learning algorithm to obtain a dimension reduction model;
correspondingly, the step of calculating the optimal design parameter value of the onboard electromechanical system by using the simulation model and a preset optimal solution algorithm to obtain the minimum value of the fuel consumption value of the onboard electromechanical system as an optimization target comprises the following steps:
and taking the minimum value of the fuel consumption value of the airborne electromechanical system as an optimization target, and calculating by using the dimension reduction model and a preset optimal solution algorithm to obtain an optimal design parameter value of the airborne electromechanical system.
3. The method of claim 2, wherein the preset machine learning algorithm is a multi-layer BP neural network algorithm, and correspondingly, the learning of all the design parameter values and the corresponding operation performance parameter values and the self-feature parameter values by using the preset machine learning algorithm, and the obtaining of the dimension-reduction model includes:
and learning all the design parameter values, the corresponding operation performance parameter values and the corresponding self characteristic parameter values by using the multi-layer BP neural network algorithm in a five-layer full-connection 128-node mode to obtain the dimension reduction model.
4. The method of claim 1, wherein the fuel consumption calculation formula is:
；
wherein K represents the aerodynamic mass of the aircraft;representing the fuel consumption of the engine; />A unit calorific value representing combustion of fuel; />Representing the inlet temperature of the compressor; />Representing turbine inlet temperature; />Indicating the fuel combustion completion factor in the combustion chamber; />Representing the compressor compression ratio of the compressor; p represents the output shaft power; />Representing bleed air flow; d represents the momentum resistance caused by bleed air; />Representing the intermediate stage increase ratio of the compressor; />Representing the final stage increasing ratio of the compressor; />Representing the inherent mass of the on-board electromechanical systems device; />Representing an initial weight of the onboard electro-mechanical system carrying a variable weight; />Indicating the time that the on-board electromechanical system is running.
5. The method of claim 1, wherein the design parameters of the on-board electromechanical system include at least a design parameter of a compressor, a design parameter of a combustor, and a design parameter of a power turbine; the operation performance parameters of the airborne electromechanical system at least comprise the operation performance parameters of the air compressor, the operation performance parameters of the combustion chamber and the operation performance parameters of the power turbine; the self-characterizing parameters of the on-board electromechanical system include at least a total volume and/or a weight of the self-characterizing parameters of the compressor, the power turbine and the combustion chamber.
6. The method of claim 5, wherein the design parameters of the compressor and the power turbine include guide vane root, guide vane tip, rotor blade root, design interface mounting angle in the rotor blade, rotor blade tip, inlet-outlet geometry angle, leading-trailing edge wedge angle, trailing camber angle, and leading edge diameter; the design parameters of the combustion chamber comprise ignition position, atomization angle, inlet and outlet areas and sizes;
the operational performance parameters of the compressor and the power turbine include flow, efficiency, power, reaction, absolute exit mach number, relative exit mach number, absolute exit airflow angle, and relative exit airflow angle; the operation performance parameters of the combustion chamber comprise outlet temperature uniformity, combustion efficiency and outlet Mach number;
the compressor, the power turbine and the combustor's own characteristic parameters include volume and/or weight.
7. The method according to claim 1, wherein calculating the optimal design parameter value of the on-board electromechanical system using the simulation model and a preset optimal solution algorithm with the minimum value of the fuel consumption value of the on-board electromechanical system as an optimization target includes:
and taking the minimum value of the fuel consumption value of the airborne electromechanical system as an optimization target, taking the running performance limit value and the self characteristic limit value of the airborne electromechanical system as constraint conditions, and calculating by utilizing the simulation model and a preset optimal solution algorithm to obtain the optimal design parameter value of the airborne electromechanical system.
8. The method of claim 7, wherein the predetermined optimal solution algorithm is a genetic algorithm.
9. The method of claim 8, wherein said minimizing the fuel consumption value of the on-board electromechanical system as an optimization target and using the operation performance limit value and the self-feature limit value of the on-board electromechanical system as constraint conditions, and calculating to obtain the optimal design parameter value of the on-board electromechanical system using the simulation model and a preset optimal solution algorithm comprises:
different penalty functions are selected for individuals with different deviation constraints.
10. An apparatus for optimally designing an on-board electromechanical system, the apparatus comprising:
the system comprises a model construction module, a model analysis module and a model analysis module, wherein the model construction module is used for constructing a simulation model of an airborne electromechanical system, and the simulation model of the airborne electromechanical system is used for calculating and obtaining an operation performance parameter value and a self characteristic parameter value of the airborne electromechanical system according to a design parameter value of the airborne electromechanical system;
the fuel consumption calculation module is used for determining a fuel consumption calculation formula of the airborne electromechanical system in the full mission stage of the aircraft, wherein the fuel consumption calculation formula is used for calculating and obtaining a fuel consumption value of the airborne electromechanical system according to the aircraft flight state parameter value, the running performance parameter value and the self characteristic parameter value of the airborne electromechanical system;
and the parameter value optimization module is used for taking the minimum value of the fuel consumption value of the airborne electromechanical system as an optimization target, and calculating and obtaining the optimal design parameter value of the airborne electromechanical system by utilizing the simulation model and a preset optimal solution algorithm.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106355253A (en) * | 2016-06-27 | 2017-01-25 | 哈尔滨工业大学（威海） | Method for determining optimal operation performance intervals of aero-engines |
| EP3211621A1 (en) * | 2016-02-29 | 2017-08-30 | The Boeing Company | Method and electronic device for optimizing a flight trajectory of an aircraft subject to time of arrival control constraints |
| CN107885891A (en) * | 2016-09-29 | 2018-04-06 | 中国航发商用航空发动机有限责任公司 | A kind of aero-engine overall performance optimization method |
| CN110348595A (en) * | 2019-05-31 | 2019-10-18 | 南京航空航天大学 | A kind of unmanned plane mixed propulsion system energy management-control method based on flying quality |
| CN110909950A (en) * | 2019-11-29 | 2020-03-24 | 中国航空工业集团公司沈阳飞机设计研究所 | Method and device for optimizing fuel consumption by adopting non-inferior ranking algorithm |
| CN114462245A (en) * | 2022-02-11 | 2022-05-10 | 北京理工大学 | Method and system for determining optimal rotating speed of multi-shaft gas turbine |
| CN114547794A (en) * | 2022-02-17 | 2022-05-27 | 中国航发沈阳发动机研究所 | Multi-process coupled gear transmission turbofan engine optimization design method |
| CN115042977A (en) * | 2022-07-06 | 2022-09-13 | 北京航空航天大学 | Megawatt-level heat load onboard thermal management system applying low-temperature expendable heat sink |
-
2023
- 2023-09-13 CN CN202311177265.4A patent/CN116910923B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3211621A1 (en) * | 2016-02-29 | 2017-08-30 | The Boeing Company | Method and electronic device for optimizing a flight trajectory of an aircraft subject to time of arrival control constraints |
| CN106355253A (en) * | 2016-06-27 | 2017-01-25 | 哈尔滨工业大学（威海） | Method for determining optimal operation performance intervals of aero-engines |
| CN107885891A (en) * | 2016-09-29 | 2018-04-06 | 中国航发商用航空发动机有限责任公司 | A kind of aero-engine overall performance optimization method |
| CN110348595A (en) * | 2019-05-31 | 2019-10-18 | 南京航空航天大学 | A kind of unmanned plane mixed propulsion system energy management-control method based on flying quality |
| CN110909950A (en) * | 2019-11-29 | 2020-03-24 | 中国航空工业集团公司沈阳飞机设计研究所 | Method and device for optimizing fuel consumption by adopting non-inferior ranking algorithm |
| CN114462245A (en) * | 2022-02-11 | 2022-05-10 | 北京理工大学 | Method and system for determining optimal rotating speed of multi-shaft gas turbine |
| CN114547794A (en) * | 2022-02-17 | 2022-05-27 | 中国航发沈阳发动机研究所 | Multi-process coupled gear transmission turbofan engine optimization design method |
| CN115042977A (en) * | 2022-07-06 | 2022-09-13 | 北京航空航天大学 | Megawatt-level heat load onboard thermal management system applying low-temperature expendable heat sink |
Non-Patent Citations (2)
| Title |
|---|
| SEBASTIAN NICOLAY 等: ""Conceptual design and optimization of a general aviation aircraft with fuel cells and hydrogen"", 《INTERNATIONAL JOURNAL OF HYDROGEN ENERGY》, vol. 46, no. 64, pages 32676 - 32694, XP086761680, DOI: 10.1016/j.ijhydene.2021.07.127 * |
| 李勇 等: ""基于遗传算法-序列二次规划的涡扇发动机最低油耗性能寻优控制"", 《推进技术》, vol. 41, no. 7, pages 1638 - 1648 * |
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