Aircraft jet control method, device, equipment and medium

Abstract

The present application provides a method, device, equipment, and medium for jet control of an aircraft, relating to the field of aircraft control technology, including: obtaining the aircraft's shape parameters, flight parameters during flight, and multiple configured jet parameters; inputting the shape parameters and flight parameters into a pre-trained first prediction model to obtain the surface heat flux density of the aircraft in a non-jet state; inputting the shape parameters, flight parameters, and target jet parameters into a pre-trained second prediction model to obtain the surface heat flux density of the aircraft in a jet state; if the surface heat flux density of the aircraft in a jet-free state and the target jet parameters meet the configured constraints, then generating aircraft jet control parameters based on the shape parameters, flight parameters, and target jet parameters. The present application can dynamically and specifically perform jet control at different positions of the aircraft.

Description

Technical Field

[0001] The present application relates to the field of aircraft control technology, and in particular to a method, device, equipment and medium for jet control of an aircraft. Background Art

[0002] In recent years, the aerodynamic heating of hypersonic vehicles has become a research hotspot. Heat flux concentrations often occur at stagnation points due to the shock waves of the incoming hypersonic flow. Concurrently, heat flux concentrations also occur at the shoulders and corners of the vehicle due to flow separation. Therefore, the location of peak heat flux on the entire vehicle surface has become a key consideration for researchers. The location and flux of peak heat flux vary with both the vehicle's shape and operating parameters. Efficiently and accurately predicting peak heat flux on a vehicle's surface is crucial for improving its aerodynamic performance.

[0003] As a novel method for reducing drag and heat, reverse jetting can inject low-temperature gas / liquid at a localized location on an aircraft, pushing shock waves away from the surface, changing the local flow field structure, and simultaneously forming a low-temperature recirculation zone, thereby reducing the heat flux density on the aircraft surface. Existing technologies (hypersonic aircraft heat and drag reduction methods, devices, equipment, and storage media) disclose adjusting target setting parameters for the aircraft based on target flight state data, thereby reducing aerodynamic heat and drag at non-zero angles of attack and improving the aircraft's aerodynamic performance. However, this technology does not clearly specify the initial position determination of the aircraft jet and the setting of flow parameters, making it impossible to perform precise jet control.

[0004] The existing aircraft jet control methods have the following problems:

[0005] First, jet control is based on predicted data on the heat flux density of the aircraft surface. However, most existing technologies only predict the heat flux density at the stagnation point on the aircraft's head, without considering the heat flux density at flow separation points such as the aircraft's shoulder. As a result, the predicted value is lower than the actual heat flux density, which in turn leads to poor jet control and causes the aircraft surface to exceed the tolerance limit of the heat protection material, resulting in ablation.

[0006] Secondly, during the actual flight of the aircraft, the local heat flux density of various parts of the aircraft is different under different working conditions. The jet should be turned on in places with more difficult thermal environments to perform jet cooling. However, the existing technology cannot perform targeted jet control at different positions of the aircraft, and the effect and efficiency of the jet process are also poor. Summary of the Invention

[0007] The purpose of the embodiments of the present application is to provide a jet control method, device, equipment and medium for an aircraft to solve the above-mentioned problems existing in the prior art, and to dynamically and specifically perform jet control at different positions of the aircraft.

[0008] In a first aspect, a method for controlling a jet flow of an aircraft is provided, which may include:

[0009] Obtaining the shape parameters of the aircraft, flight parameters during flight, and multiple configured jet parameters;

[0010] Inputting the shape parameters and the flight parameters into a pre-trained first prediction model to obtain the surface heat flux density of the aircraft in a non-jet state; wherein the first prediction model is trained using a plurality of historical non-jet surface heat flux density data; each piece of historical non-jet surface heat flux density data includes: historical shape parameters, historical flight parameters, and corresponding historical surface heat flux density data in a non-jet state;

[0011] If the surface heat flux density is greater than or equal to a configured heat flux density threshold, any one of the multiple jet parameters is used as a target jet parameter;

[0012] Inputting the shape parameters, the flight parameters, and the target jet parameters into a pre-trained second prediction model to obtain a surface heat flux density of the aircraft in a jet state; wherein the second prediction model is trained using a plurality of historical jet surface heat flux density data; each piece of historical jet surface heat flux density data includes: historical shape parameters, historical flight parameters, historical jet parameters, and historical surface heat flux density data under the corresponding jet state;

[0013] If the surface heat flux density of the aircraft in the jet state and the target jet parameters meet the configured constraints, aircraft jet control parameters are generated based on the shape parameters, the flight parameters and the target jet parameters to control the aircraft jet.

[0014] In an optional implementation, the jet parameters include: jet flow rate; the jet flow rate includes: head jet flow rate and shoulder jet flow rate;

[0015] The constraints include heat flux density constraints and jet flow rate constraints;

[0016] The surface heat flux density includes: head surface heat flux density and shoulder surface heat flux density.

[0017] In an optional implementation, inputting the shape parameters, the flight parameters, and the target jet parameters into a pre-trained second prediction model includes:

[0018] The shape parameters, the flight parameters and the target jet flow rate are input into a pre-trained second prediction model to obtain the surface heat flux density of the aircraft in the jet state.

[0019] In an optional implementation, if the surface heat flux is greater than or equal to a configured heat flux threshold, one of the multiple jet parameters is randomly selected as the target jet parameter, including:

[0020] If the head surface heat flux density is less than a configured heat flux density threshold, any one of the multiple head jet flow rates is used as a target head jet flow rate;

[0021] If the heat flux density on the shoulder surface is less than a configured heat flux density threshold, any one of the multiple shoulder jet flow rates is used as the target shoulder jet flow rate.

[0022] In an optional implementation, after obtaining the surface heat flux density of the aircraft in a non-jet state, the method further includes:

[0023] When the head surface heat flux density and the shoulder surface heat flux density of the aircraft in the jet state both meet the heat flux density constraint conditions, if the sum of the target head jet flow rate and the target shoulder jet flow rate meets the jet flow rate constraint conditions, the aircraft jet control parameters are generated based on the shape parameters, the flight parameters, the target head jet flow rate and the target shoulder jet flow rate.

[0024] In an optional implementation, the method further includes:

[0025] If the heat flux density on the head surface or the heat flux density on the shoulder surface of the aircraft in the jet state does not meet the heat flux constraint condition, new target jet parameters are selected from multiple jet parameters, and the execution step is returned to: the shape parameters, the flight parameters and the target jet parameters are input into a pre-trained second prediction model until the heat flux density on the head surface and the heat flux density on the shoulder surface in the jet state that meet the heat flux constraint condition are obtained.

[0026] In an optional implementation, the method further includes:

[0027] If the sum of the target head jet flow rate and the target shoulder jet flow rate does not satisfy the jet flow rate constraint condition, adjusting the target head jet flow rate and the target shoulder jet flow rate;

[0028] The adjusted target head jet flow rate and the adjusted target shoulder jet flow rate are used as new target jet parameters, and the execution step is returned to: the shape parameters, the flight parameters and the target jet parameters are input into a pre-trained second prediction model until the head surface heat flux density and the shoulder surface heat flux density under the jet state that meet the constraints are obtained.

[0029] In a second aspect, a jet control device for an aircraft is provided, which may include:

[0030] An acquisition unit, used to acquire the shape parameters of the aircraft, flight parameters during flight, and multiple configured jet parameters;

[0031] a first prediction unit, configured to input the shape parameters and the flight parameters into a pre-trained first prediction model to obtain a surface heat flux density of the aircraft in a non-jet state; wherein the first prediction model is trained using a plurality of historical non-jet surface heat flux density data; each piece of historical non-jet surface heat flux density data includes: historical shape parameters, historical flight parameters, and corresponding historical surface heat flux density data in a non-jet state;

[0032] a second prediction unit configured to, if the surface heat flux density is greater than or equal to a configured heat flux density threshold, use any one of the plurality of jet parameters as a target jet parameter; input the shape parameter, the flight parameter, and the target jet parameter into a pre-trained second prediction model to obtain the surface heat flux density of the aircraft in the jet state; wherein the second prediction model is trained using a plurality of historical jet surface heat flux density data; each piece of historical jet surface heat flux density data includes: historical shape parameters, historical flight parameters, historical jet parameters, and historical surface heat flux density data under the corresponding jet state;

[0033] A generating unit is used to generate aircraft jet control parameters based on the shape parameters, the flight parameters and the target jet parameters to control the aircraft jet if the surface heat flux density of the aircraft in the jet state and the target jet parameters meet the configured constraints.

[0034] In a third aspect, an electronic device is provided, the electronic device including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0035] Memory for storing computer programs;

[0036] The processor is configured to implement any of the method steps described in the first aspect when executing a program stored in the memory.

[0037] In a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any of the method steps described in the first aspect is implemented.

[0038] This application forms a database through numerical simulation of historical shape parameters and historical flight parameters, constructs a first prediction model for the stagnation point (head) heat flux density and shoulder heat flux density in the non-jet state, adds jet parameters to the historical shape parameters and historical flight parameters, and simulates again to form a second prediction model for the heat flux density in the jet state. In actual application, by quickly predicting the heat flux density in the non-jet state and comparing it with the aircraft material's limit heat flux density, it is determined whether a jet is needed and where the jet is needed. The flow rate of each jet is recorded, and the thermal protection effect of reducing the local heat flux density of the aircraft is achieved under the total flow constraint. If the flow rate exceeds the constraint, the flow rate needs to be readjusted, thereby achieving dynamic and precise jet control.

[0039] This application can quickly locate the position where the aircraft needs a jet, and perform jets at the position with higher heat flux, reducing the local heat flux density without causing material ablation; at the same time, the application of the prediction model can greatly reduce the computing resource consumption brought by numerical simulation, reduce the computing time and improve the computing efficiency, quickly and accurately predict the heat flux density on the aircraft surface, give jet instructions, and reduce the consumption of jet flow; and can directly obtain parameters such as the position and flow of the aircraft jet for complex ballistic conditions, which is conducive to the early scheme design.

[0040] The flow constraint adopted in this application will make the jet flow at each working point in the process more reasonable. Compared with the simultaneous jetting at multiple positions, the jet flow usage can be reduced, avoiding excessive flow usage in the early stage and insufficient flow when the jet is needed in the later stage, resulting in a reduction in the thermal protection effect throughout the process. The flow distribution and adjustment through flow constraint greatly improves the overall energy utilization efficiency of the jet flow. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 A flow chart of a jet control method for an aircraft provided in an embodiment of the present application;

[0043] Figure 2 A schematic diagram of constructing the first prediction model and the second prediction model provided in the embodiment of the present application;

[0044] Figure 3 A schematic diagram of a jet control method for an aircraft provided in an embodiment of the present application;

[0045] Figure 4A schematic structural diagram of a jet control device for an aircraft provided in an embodiment of the present application;

[0046] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0048] The jet control method for an aircraft provided in an embodiment of the present application can be applied in a server or in a terminal with strong computing power. The server can be a physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and basic cloud computing services such as big data and artificial intelligence platforms. The terminal can be a user equipment (UE) such as a mobile phone, a smart phone, a laptop, a digital broadcast receiver, a personal digital assistant (PDA), a tablet computer (PAD), a handheld device, a vehicle-mounted device, a wearable device, a computing device or other processing device connected to a wireless modem, a mobile station (MS), a mobile terminal, etc. The terminal and the server can be directly or indirectly connected through a wired or wireless communication method, which is not limited in this application.

[0049] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application can be combined with each other if there is no conflict.

[0050] Figure 1 The following is a flow chart of a jet control method for an aircraft provided in an embodiment of the present application. Figure 1 As shown, the method may include:

[0051] Step S110, obtaining the shape parameters of the aircraft, the flight parameters during flight, and the configured multiple jet parameters; inputting the shape parameters and the flight parameters into a pre-trained first prediction model to obtain the surface heat flux density of the aircraft in a non-jet state.

[0052] In the embodiment of the present application, the shape parameters of the aircraft include: head radius, shoulder radius and total length, etc.; the flight parameters include: flight altitude, flight Mach number and angle of attack, etc.; the jet parameters include: jet flow rate; the jet flow rate includes head jet flow rate and shoulder jet flow rate.

[0053] In an embodiment of the present application, a first prediction model and a second prediction model are pre-constructed; wherein, the first prediction model is trained using a data set of heat flux density on a surface without a jet; and the second prediction model is trained using a data set of heat flux density on a surface with a jet; the first prediction model and the second prediction model can select any one of the models such as radial basis, kriging or BP neural network.

[0054] In an embodiment of the present application, the jet-free surface heat flux density data set includes multiple historical jet-free surface heat flux density data; each historical jet-free surface heat flux density data includes: historical shape parameters, historical flight parameters and corresponding historical surface heat flux density data under the jet-free state.

[0055] In an embodiment of the present application, multiple historical shape parameters and multiple historical flight parameters are obtained by performing orthogonal experiments within the value range of the aircraft shape parameters and the value range of the flight parameters; the historical surface heat flux density data in the jet-free state corresponding to any historical shape parameter or historical flight parameter is obtained by performing CFD numerical simulation in the jet-free state; specifically, a three-dimensional model of the aircraft is constructed according to the historical shape parameters, and the three-dimensional model of the aircraft is simulated according to the historical flight parameters, so as to obtain the historical surface heat flux density data of the aircraft in the jet-free state.

[0056] In the embodiment of the present application, the surface heat flux density data includes: head heat flux density and shoulder heat flux density.

[0057] Step S120: If the surface heat flux density is greater than or equal to the configured heat flux density threshold, any one of the multiple jet parameters is used as the target jet parameter; the shape parameters, flight parameters and target jet parameters are input into a pre-trained second prediction model to obtain the surface heat flux density of the aircraft in the jet state.

[0058] In the embodiment of the present application, the user can set the heat flux density threshold according to actual conditions or experience values. The heat flux density threshold is the limit heat flux density value of the material used in the aircraft.

[0059] In an embodiment of the present application, the jet surface heat flux density data set includes multiple historical jet surface heat flux density data; each historical jet surface heat flux density data includes: historical shape parameters, historical flight parameters, historical jet parameters and corresponding historical surface heat flux density data in the no-jet state.

[0060] In the embodiment of the present application, the jet parameters include: historical jet flow, historical jet angle, historical jet temperature, etc.

[0061] In an embodiment of the present application, multiple historical shape parameters, multiple historical jet parameters and multiple historical flight parameters are obtained by performing orthogonal experiments within the value range of the aircraft shape parameters, the value range of the flight parameters and the value range of the jet parameters; the historical surface heat flux density data under the jet state corresponding to any historical shape parameter, historical flight parameter and historical jet parameter is obtained by performing CFD numerical simulation under the jet state after adding a reverse jet; specifically, a three-dimensional model of the aircraft is constructed according to the historical shape parameters, and the three-dimensional model of the aircraft is simulated according to the historical flight parameters and the historical jet parameters, so as to obtain the historical surface heat flux density data under the jet state of the aircraft.

[0062] In an embodiment of the present application, if the heat flux density of the head and the heat flux density of the shoulder of the aircraft in the non-jet state are both less than the heat flux threshold, the operating parameters and flight parameters are directly output.

[0063] In an embodiment of the present application, if the head heat flux density of the aircraft in a non-jet state is greater than or equal to the heat flux density threshold, it means that the head needs to be jetted. At this time, one head jet flow rate is selected from multiple head jet flow rates as the target head jet flow rate, and the head heat flux density of the aircraft in the jet state is predicted in combination with the flight parameters and shape parameters.

[0064] In an embodiment of the present application, if the shoulder heat flux density of the aircraft in a non-jet state is greater than or equal to the heat flux density threshold, it means that the shoulder needs to be jetted. At this time, one shoulder jet flow rate is selected from multiple shoulder jet flow rates as the target shoulder jet flow rate, and the shoulder heat flux density of the aircraft in the jet state is predicted in combination with the flight parameters and shape parameters.

[0065] In the embodiment of the present application, you can choose to jet the head alone, you can choose to jet the shoulders alone, or you can choose to jet the shoulders and head at the same time.

[0066] Step S130: If the surface heat flux density and target jet parameters of the aircraft in the jet state meet the configured constraints, then generate aircraft jet control parameters based on the shape parameters, flight parameters and target jet parameters.

[0067] In an embodiment of the present application, the constraints include a heat flux density constraint and a jet flow rate constraint; specifically, the heat flux density constraint is that the surface heat flux density of the aircraft in the jet state should be less than the heat flux density threshold (i.e., the limiting heat flux density value of the material used in the aircraft); the jet flow rate constraint is that the total jet flow rate cannot exceed the total jet flow rate carried by the aircraft.

[0068] In an embodiment of the present application, after obtaining the surface heat flux density of the aircraft in the jet state, it is necessary to first determine whether the head heat flux density in the jet state and the shoulder heat flux density in the jet state are less than the heat flux threshold value; if the head heat flux density and the shoulder heat flux density in the jet state are both less than the heat flux threshold value (that is, the heat flux constraint condition is met), then calculate whether the sum of the target head jet flow and the target shoulder jet flow is less than or equal to the total jet flow (that is, the jet flow constraint condition is met). If so, the aircraft jet control parameters are generated directly based on the shape parameters, flight parameters, target head jet flow and target shoulder jet flow; if not, the target head jet flow and the target shoulder jet flow are adjusted to obtain new target head jet flow and new target shoulder jet flow, and return to determine whether the flow constraint condition is met until the parameters that meet the flow constraint condition are obtained.

[0069] In the embodiment of the present application, adjusting the target head jet flow rate and the target shoulder jet flow rate includes: multiplying the target head jet flow rate and the target shoulder jet flow rate by a coefficient of 90%, respectively.

[0070] In an embodiment of the present application, if only one of the head heat flux density and the shoulder heat flux density in the jet state meets the heat flux constraint condition, then for the position that does not meet the heat flux constraint condition, it is necessary to reselect a new jet flow rate, recalculate and judge; while for the position that already meets the heat flux constraint condition, no recalculation is required; that is, the calculation of the shoulder heat flux density and the head heat flux density in the jet state are independent.

[0071] For example, if the heat flux density of the head in the jet state meets the heat flux density constraint condition, but the shoulder does not, a new target shoulder jet flow rate is selected from the shoulder jet flow rate, and the flight parameters, shape parameters and the new target shoulder jet flow rate are input into the second prediction model to obtain the shoulder heat flux density in the new jet state. If it meets the condition, it is judged whether the sum of the new target shoulder jet flow rate and the target head jet flow rate meets the flow constraint condition, otherwise the new target shoulder jet flow rate is selected.

[0072] In an embodiment of the present application, if the heat flux density of the head in the non-jet state is greater than or equal to the heat flux density threshold, and the heat flux density of the shoulder in the non-jet state is less than the heat flux density threshold, it means that only the head needs to be jetted. At this time, a target head jet flow rate is selected from multiple head jet flow rates, and is input into the second prediction model together with the flight parameters and shape parameters to obtain the corresponding head heat flux density in the jet state; if the head heat flux density in the jet state satisfies the heat flux density constraint condition, it is determined whether the target head jet flow rate satisfies the flow constraint condition. If so, the aircraft jet control parameters are generated based on the flight parameters, shape parameters and target head jet flow rate.

[0073] In the embodiments of the present application, when generating aircraft jet control parameters, the position information of the target jet parameters is also extracted and combined with this position information to generate jet parameters to control the jet at the position corresponding to the position information. Taking the above example, jet control parameters for controlling the aircraft head jet are generated based on flight parameters, shape parameters, and target head jet flow rate.

[0074] In the embodiment of the present application, the position information of the head can be extracted based on the target head jet flow rate.

[0075] Based on the above solution, the present application can generate targeted jet control parameters for the shoulders and / or head of the aircraft, thereby achieving safe and efficient aircraft jet control.

[0076] In one embodiment of the present application, the jet control method of the aircraft of the embodiment of the present application includes the following steps:

[0077] 1. If Figure 1 As shown, build a prediction model:

[0078] Step 1: Determine the parameter range

[0079] Parameters that affect aircraft aerodynamic simulation and heat flux calculations are mainly divided into two categories: structural parameters, such as head radius, shoulder radius, and overall length; and flight parameters (also known as operating parameters), such as altitude, Mach number, and angle of attack. Determining the range of each parameter facilitates sampling and experimental design.

[0080] Step 2: Orthogonal experimental design

[0081] The orthogonal experimental design method is used to perform sampling within the parameter range to obtain calculation sample points, which is convenient for the next step of simulation calculation.

[0082] Step 3 (1): CFD numerical simulation without jet

[0083] A CFD numerical simulation is performed on each sample point with the above-mentioned determined shape and working condition under the non-jet state to form a database.

[0084] Step 4 (1): Obtain heat flux distribution

[0085] After the calculation is completed, the heat flux density distribution of the aircraft along the symmetry axis of each sample point is output, and the values ​​of the head heat flux density Qo and the shoulder heat flux density Qr are obtained at the same time.

[0086] Step 5 (1): Build a prediction model

[0087] The first prediction model constructs a prediction model with the head heat flux density Qot and the shoulder heat flux density Qrt as outputs and the shape and working condition parameters as inputs. The prediction model can choose radial basis, Kriging, BP neural network and other prediction models. After completing training and testing, the final first prediction model is generated.

[0088] Step 3 (2): Select the jet position

[0089] Add a reverse jet to each sample point with the above-mentioned determined shape and working conditions. First, select the jet position, with the head as O and the shoulder as R.

[0090] Step 4 (2): Add jet parameters

[0091] The basic parameters required for the introduction of reverse jets include jet flow rate, jet angle, jet temperature and other parameters.

[0092] Step 5 (2): CFD numerical simulation of jet flow

[0093] A CFD numerical simulation is performed on each sample point with the above-mentioned determined shape and working condition under the jet state to form a database.

[0094] Step 6 (2): Obtain heat flux distribution

[0095] After the calculation is completed, the heat flux density distribution of the aircraft along the symmetry axis of each sample point is output, and the values ​​of the head heat flux density Qo and the shoulder heat flux density Qr are obtained at the same time.

[0096] Step 7 (2): Build a prediction model

[0097] The second prediction model constructs a prediction model with the head heat flux density Qos and the shoulder heat flux density Qrs as outputs and the shape, working conditions and jet parameters as inputs. The prediction model can choose radial basis, Kriging, BP neural network and other prediction models. After completing training and testing, the final second prediction model is generated.

[0098] 2. If Figure 2 As shown, use the prediction model to make predictions:

[0099] Step 1: Fix the shape parameters and input the trajectory conditions

[0100] In actual flight, it is necessary to predict the aerodynamic performance of the aircraft in advance through the actual ballistic conditions and obtain relevant aerodynamic parameters to facilitate scheme design.

[0101] Step 2: Determine the overall constraints

[0102] There are two constraints, including the maximum heat flux density Qmax of the aircraft material and the total jet flow rate Mall carried. The heat flux density on the aircraft surface cannot exceed Qmax, otherwise ablation will occur, causing damage to the aircraft. The sum of the jet flow rates at each operating point and different positions cannot be greater than Mall.

[0103] Step 3: Calculate the heat flux in the no-jet state

[0104] The first prediction model is used to calculate Qot and Qrt at the ballistic operating point in the non-jet state.

[0105] Step 4: Determination of heat flow constraints without jet

[0106] Compare the predicted heat flux density with the maximum heat flux density Qmax of the aircraft material. If Qot is higher than Qmax, enter the head jet link. If Qrt is higher than Qmax, enter the shoulder jet link. The two can jet at the same time. If the heat flux density is less than Qmax, directly go to step 7 to calculate the total jet flow rate.

[0107] Step 5: Calculate the jet state

[0108] According to the judgment in step 4, the jet flow rate is selected for jetting, and at the same time, it is brought into the second prediction model to calculate the Qos and Qrs under the jet state at the operating point.

[0109] Step 6: Determination of jet heat flow constraints

[0110] Continue to compare the heat flux density with Qmax. If it is less than the limit heat flux density, it indicates that the jet is effective. Reduce the heat flux density on the aircraft surface to the range that the material can withstand and go to step 7. If it still does not reach below the limit heat flux, return to step 5, adjust the jet flow rate and recalculate.

[0111] Step 7: Calculate the total jet flow rate

[0112] The sum of the head and shoulder jet flow positions at each operating point is recorded as Mj.

[0113] Step 8: Flow Constraint Determination

[0114] Compare the total jet flow Mj with the total jet flow Mall. If it does not exceed the total flow, it means that the jet flow is reasonably selected and meets the flow constraint. Go to step 9. If it has exceeded the total flow, the head and shoulder jet flow is reduced to 0.9 of the original flow, and then go to step 5 and recalculate until it meets the flow constraint.

[0115] Step 9: Output the results

[0116] After the cycle of each operating point is completed, the operating parameters, jet position and jet flow rate at each position are output, and the jet flow rate control scheme for aircraft thermal protection under heat flow / flow constraints can be obtained.

[0117] Corresponding to the above method, the embodiment of the present application also provides a jet control device for an aircraft, such as Figure 4 As shown, the jet control device of the aircraft includes:

[0118] An acquisition unit 410 is used to acquire the shape parameters of the aircraft, flight parameters during flight, and multiple configured jet parameters;

[0119] The first prediction unit 420 is configured to input the shape parameters and flight parameters into a pre-trained first prediction model to obtain the surface heat flux density of the aircraft in a non-jet state. The first prediction model is trained using a plurality of historical non-jet surface heat flux density data. Each piece of historical non-jet surface heat flux density data includes: historical shape parameters, historical flight parameters, and corresponding historical surface heat flux density data in a non-jet state.

[0120] The second prediction unit 430 is configured to use any one of the plurality of jet parameters as a target jet parameter if the surface heat flux density is greater than or equal to a configured heat flux density threshold; input the shape parameters, flight parameters, and target jet parameters into a pre-trained second prediction model to obtain the surface heat flux density of the aircraft in the jet state; wherein the second prediction model is trained using a plurality of historical jet surface heat flux density data; each piece of historical jet surface heat flux density data includes: historical shape parameters, historical flight parameters, historical jet parameters, and historical surface heat flux density data under the corresponding jet state;

[0121] The generating unit 440 is used to generate aircraft jet control parameters based on the shape parameters, flight parameters and target jet parameters to control the aircraft jet if the surface heat flux density and target jet parameters of the aircraft in the jet state meet the configured constraints.

[0122] The functions of the various functional units of the jet control device of the aircraft provided in the above embodiments of the present application can be achieved through the above method steps. Therefore, the specific working process and beneficial effects of each unit in the jet control device of the aircraft provided in the embodiments of the present application will not be repeated here.

[0123] The present application also provides an electronic device, such as Figure 5 As shown, it includes a processor 510 , a communication interface 520 , a memory 530 and a communication bus 540 , wherein the processor 510 , the communication interface 520 , and the memory 530 communicate with each other via the communication bus 540 .

[0124] Memory 530, for storing computer programs;

[0125] The processor 510 is configured to execute the program stored in the memory 530 by performing the following steps:

[0126] Obtaining the shape parameters of the aircraft, flight parameters during flight, and multiple configured jet parameters;

[0127] Inputting the shape parameters and flight parameters into a pre-trained first prediction model to obtain the surface heat flux density of the aircraft in a jet-free state; wherein the first prediction model is trained using multiple historical jet-free surface heat flux density data; each historical jet-free surface heat flux density data includes: historical shape parameters, historical flight parameters, and corresponding historical surface heat flux density data in a jet-free state;

[0128] If the surface heat flux density is greater than or equal to the configured heat flux density threshold, any one of the multiple jet parameters is used as the target jet parameter; the shape parameters, flight parameters, and target jet parameters are input into a pre-trained second prediction model to obtain the surface heat flux density of the aircraft in the jet state; wherein the second prediction model is trained using multiple historical jet surface heat flux density data; each historical jet surface heat flux density data includes: historical shape parameters, historical flight parameters, historical jet parameters, and historical surface heat flux density data under the corresponding jet state;

[0129] If the surface heat flux density and target jet parameters of the aircraft in the jet state meet the configured constraints, the aircraft jet control parameters are generated based on the shape parameters, flight parameters and target jet parameters to control the aircraft jet.

[0130] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is used in the figure, but this does not mean that there is only one bus or only one type of bus.

[0131] The communication interface is used for communication between the above electronic device and other devices.

[0132] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage. Alternatively, the memory may be at least one storage device located away from the processor.

[0133] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0134] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments to solve the problems can be found in Figure 1 The various steps in the embodiment shown are implemented, therefore, the specific working process and beneficial effects of the electronic device provided by the embodiment of the present application are not repeated here.

[0135] In another embodiment provided in the present application, a computer-readable storage medium is provided, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes the jet control method of the aircraft in any of the above embodiments.

[0136] In another embodiment provided by the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute the jet control method for an aircraft according to any one of the above embodiments.

[0137] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of the present application can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0138] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0139] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0141] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0142] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the spirit and scope of the embodiments of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims and their equivalents, the embodiments of the present application are also intended to include these modifications and variations.

Claims

1. A jet control method for an aircraft, characterized in that: The method comprises: Obtaining the shape parameters of the aircraft, flight parameters during flight, and multiple configured jet parameters; Inputting the shape parameters and the flight parameters into a pre-trained first prediction model to obtain the surface heat flux density of the aircraft in a non-jet state; wherein the first prediction model is trained using a plurality of historical non-jet surface heat flux density data; each piece of historical non-jet surface heat flux density data includes: historical shape parameters, historical flight parameters, and corresponding historical surface heat flux density data in a non-jet state; If the surface heat flux density is greater than or equal to a configured heat flux density threshold, any one of the multiple jet parameters is used as a target jet parameter; Inputting the shape parameters, the flight parameters, and the target jet parameters into a pre-trained second prediction model to obtain a surface heat flux density of the aircraft in a jet state; wherein the second prediction model is trained using a plurality of historical jet surface heat flux density data; each piece of historical jet surface heat flux density data includes: historical shape parameters, historical flight parameters, historical jet parameters, and historical surface heat flux density data under the corresponding jet state; If the surface heat flux density of the aircraft in the jet state and the target jet parameters meet the configured constraints, aircraft jet control parameters are generated based on the shape parameters, the flight parameters and the target jet parameters to control the aircraft jet.

2. The method according to claim 1, wherein The jet parameters include: jet flow rate; the jet flow rate includes: head jet flow rate and shoulder jet flow rate; The constraints include heat flux density constraints and jet flow rate constraints; The surface heat flux density includes: head surface heat flux density and shoulder surface heat flux density.

3. The method according to claim 2, wherein Inputting the shape parameters, the flight parameters, and the target jet parameters into a pre-trained second prediction model includes: The shape parameters, the flight parameters and the target jet flow rate are input into a pre-trained second prediction model to obtain the surface heat flux density of the aircraft in the jet state.

4. The method according to claim 2, wherein If the surface heat flux is greater than or equal to the configured heat flux threshold, a random one of the multiple jet parameters is selected as the target jet parameter, including: If the head surface heat flux density is less than a configured heat flux density threshold, any one of the multiple head jet flow rates is used as a target head jet flow rate; If the heat flux density on the shoulder surface is less than a configured heat flux density threshold, any one of the multiple shoulder jet flow rates is used as the target shoulder jet flow rate.

5. The method according to claim 4, wherein After obtaining the surface heat flux density of the aircraft in a non-jet state, the method further includes: When the head surface heat flux density and the shoulder surface heat flux density of the aircraft in the jet state both meet the heat flux density constraint conditions, if the sum of the target head jet flow rate and the target shoulder jet flow rate meets the jet flow rate constraint conditions, the aircraft jet control parameters are generated based on the shape parameters, the flight parameters, the target head jet flow rate and the target shoulder jet flow rate.

6. The method according to claim 5, wherein The method further comprises: If the heat flux density on the head surface or the heat flux density on the shoulder surface of the aircraft in the jet state does not meet the heat flux constraint condition, new target jet parameters are selected from multiple jet parameters, and the execution step is returned to: the shape parameters, the flight parameters and the target jet parameters are input into a pre-trained second prediction model until the heat flux density on the head surface and the heat flux density on the shoulder surface in the jet state that meet the heat flux constraint condition are obtained.

7. The method according to claim 5, wherein The method further comprises: If the sum of the target head jet flow rate and the target shoulder jet flow rate does not satisfy the jet flow rate constraint condition, adjusting the target head jet flow rate and the target shoulder jet flow rate; The adjusted target head jet flow rate and the adjusted target shoulder jet flow rate are used as new target jet parameters, and the execution step is returned to: the shape parameters, the flight parameters and the target jet parameters are input into a pre-trained second prediction model until the head surface heat flux density and the shoulder surface heat flux density under the jet state that meet the constraints are obtained.

8. A jet control device for an aircraft, characterized in that: The device comprises: An acquisition unit, used to acquire the shape parameters of the aircraft, flight parameters during flight, and multiple configured jet parameters; a first prediction unit, configured to input the shape parameters and the flight parameters into a pre-trained first prediction model to obtain a surface heat flux density of the aircraft in a non-jet state; wherein the first prediction model is trained using a plurality of historical non-jet surface heat flux density data; each piece of historical non-jet surface heat flux density data includes: historical shape parameters, historical flight parameters, and corresponding historical surface heat flux density data in a non-jet state; a second prediction unit configured to, if the surface heat flux density is greater than or equal to a configured heat flux density threshold, use any one of the plurality of jet parameters as a target jet parameter; input the shape parameter, the flight parameter, and the target jet parameter into a pre-trained second prediction model to obtain the surface heat flux density of the aircraft in the jet state; wherein the second prediction model is trained using a plurality of historical jet surface heat flux density data; each piece of historical jet surface heat flux density data includes: historical shape parameters, historical flight parameters, historical jet parameters, and historical surface heat flux density data under the corresponding jet state; A generating unit is used to generate aircraft jet control parameters based on the shape parameters, the flight parameters and the target jet parameters to control the aircraft jet if the surface heat flux density of the aircraft in the jet state and the target jet parameters meet the configured constraints.

9. An electronic device, characterized in that: The electronic device includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; A processor, configured to implement the method according to any one of claims 1 to 7 when executing a program stored in a memory.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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