Control method for improving anti-interference performance of aircraft based on jet flow

Abstract

The invention relates to a control method for improving the anti-interference performance of an aircraft based on jet flow, belongs to the technical field of anti-interference of fixed-wing aircrafts, and solves the technical problems that a passive flow control method in the prior art cannot adjust the control effect according to the current situation in actual use and is poor in control effect under non-design working conditions. According to the control method, the requirement for safe and stable operation of the aircraft in complex environments such as water surface take-off and landing and gust disturbance is considered, and the active flow control lift-augmentation and drag-reduction method for jet wing circular rector control is used; the lift coefficient of the aircraft can be improved to the maximum extent, and meanwhile, the wave resistance or wind resistance of the aircraft is improved in a stable maneuverability mode.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-interference of fixed-wing aircraft, and in particular to a control method for improving the anti-interference performance of an aircraft based on jet flow. Background Art

[0002] Flow control technologies can be divided into active and passive control technologies. Passive flow control uses passive control devices to change the flow field environment. These pre-designed operating conditions cannot be changed in real time. In actual use, this leads to problems such as an inability to adjust the control effect based on the current situation and poor control effectiveness under non-designed operating conditions. Active flow control, on the other hand, injects energy into the flow field to create disturbances that couple with the flow pattern of the flow field itself, thereby changing the flow field and improving the flight performance of the aircraft, achieving lift increase and drag reduction, or anti-interference.

[0003] Circulation control, as a method of flow control, modifies the wing's circulation by blowing air over the wing's trailing edge to create a Coanda effect, thereby changing the wing's lift and drag. Currently, research on wing circulation control has matured, and with the continuous development of jet technology, this technology is increasingly being applied to flow control. High-speed fluid injection at the wing's trailing edge increases the energy of the fluid behind it, deflecting it downward and increasing the retained circulation, thereby increasing lift and achieving flight control. Jet circulation control offers advantages over passive flow control, such as flexible control and significant control effectiveness. Summary of the Invention

[0004] To address the technical issues of passive flow control, which is unable to adjust control effects according to actual conditions and exhibits poor control performance under off-design conditions, this invention provides a jet-based control method for improving aircraft anti-interference performance. This method maximizes the aircraft's lift coefficient while ensuring stable maneuverability, enhancing the aircraft's wave and wind resistance. Specifically, addressing the need for safe and stable aircraft operation in complex environments such as water takeoff and landing and gusty wind disturbances, this invention utilizes an active flow control method for increasing lift and reducing drag using jet wing circulation control. Compared to a scenario without flow control, the aerodynamic disturbances caused by external factors such as water vapor mixing and gusty wind disturbances are significantly reduced. During water takeoff and landing, jet circulation control (the jet momentum coefficient is the control variable of jet circulation control) can improve the aircraft's lift-to-drag ratio while suppressing aerodynamic changes caused by waves. This can reduce the aircraft's takeoff distance and the time it is exposed to surface wave disturbances. When subjected to gusty wind disturbances, jet circulation control can mitigate the disturbances and stabilize the aircraft's flight state.

[0005] The present invention provides a control method for improving the anti-interference performance of an aircraft based on jet flow, which comprises the following steps: Step 1. Arrange jet holes on the upper surface of the wing to form a jet flap; Step 2. Determine external disturbance parameters, conduct a fluid simulation experiment on the wing based on the disturbance parameters, and obtain first experimental values of the external disturbance parameters and the wing lift coefficient; and construct a model for the influence of disturbance on the wing lift coefficient based on the first experimental values; Step 3. Determine the jet momentum coefficient of the wing during flight, conduct a fluid simulation experiment on the wing based on the jet momentum coefficient, and obtain a second experimental value of the jet momentum coefficient and the wing lift coefficient varying with time; based on the second experimental value, construct a model in which the wing lift coefficient is controlled by the jet momentum coefficient; Step 4. Based on the model of the wing lift coefficient being affected by disturbances and the model of the wing lift coefficient being controlled by the jet momentum coefficient, a disturbance mitigation control model is obtained, in which the input is the external disturbance parameter and the output is the jet momentum coefficient. Step 5. Use the disturbance mitigation control model to obtain the jet momentum coefficient required for the aircraft to ensure the stability of the lift coefficient during flight, and control the jet momentum coefficient of the jet on the wing to be the required jet momentum coefficient.

[0006] Optionally, the external disturbance parameter is gusts of wind and / or waves experienced by the wing.

[0007] Optionally, the gust is the wind speed of a vertical gust generated by the outside world; and the wave is the height of a water surface wave generated by the outside world.

[0008] Optionally, the lift coefficient disturbance impact model is a neural network; the network architecture of the neural network includes an input layer, a hidden layer and an output layer.

[0009] Optionally, the model of the wing lift coefficient affected by external disturbances is a neural network; the network architecture of the neural network includes an input layer, a hidden layer and an output layer.

[0010] Optionally, the input layer includes wave height neurons and gust speed neurons.

[0011] Optionally, the input layer includes momentum coefficient neurons.

[0012] Compared with the prior art, the present invention has at least the following beneficial effects: 1) The method of the present invention utilizes fluid dynamics methods and ARMA model (autoregressive moving average model) identification methods to establish a model for the influence of external disturbances such as water surface waves and gusts on the lift coefficient of the wing, and can predict the changes in the lift coefficient caused by external disturbances.

[0013] 2) The method of the present invention introduces the jet momentum coefficient and establishes a relationship model between the jet momentum coefficient and the lift coefficient, which can predict the influence of different momentum coefficients on lift.

[0014] 3) The method of the present invention achieves different degrees of lift increase by changing the jet momentum coefficient, thereby offsetting the aerodynamic load generated by external disturbances and effectively mitigating the impact of external disturbances. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 This is a flow chart of a control method for improving the anti-interference performance of an aircraft based on jet flow according to the present invention; Figure 2 (a) is a schematic diagram of a wing simulation model of the present invention; FIG2( b ) is a schematic diagram of the jet holes on the wing simulation model of the present invention; Figure 3 The lift coefficient fluctuations of the wing of the present invention at different heights after being disturbed by waves at an angle of attack of 0°; Figure 4 The lift coefficient curve of the airfoil before and after the gust disturbance of the wing of the present invention; Figure 5 A model of the lift coefficient affected by disturbances in the jet-based control method for improving the anti-interference performance of an aircraft according to the present invention; Figure 6 Schematic diagram of the prediction effect of the lift coefficient change caused by "1-cos" type gusts using the model of the lift coefficient affected by disturbances in the jet-based method for improving the anti-interference performance of an aircraft according to the present invention; Figure 7 A prediction model for the influence of the jet momentum coefficient on the lift coefficient of the method for improving the anti-interference performance of an aircraft based on jet flow according to the present invention; Figure 8 This is a framework diagram of a control system for a method for improving aircraft anti-interference performance based on jet flow according to the present invention; Figure 9 The control effect of the jet-based method for improving the anti-interference performance of an aircraft under gust disturbances of the present invention; Figure 10 This is the control effect of the jet-based method for improving the anti-interference performance of an aircraft under wave disturbance under gust disturbance.

[0015] 1. Jet hole. DETAILED DESCRIPTION

[0016] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. In addition, the present invention can also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited by the specific embodiments disclosed below.

[0017] A specific embodiment of the present invention, as Figures 1-10As shown, a control method for improving the anti-interference performance of an aircraft based on jet flow is disclosed, and the specific steps are as follows: Step 1. As shown in Figure 2, jet holes are arranged on the upper surface of the wing to form a jet flap; Specifically, the jet hole arrangement method is to set a jet hole 1 tangent to the upper wing surface at 70% of the chord length.

[0018] Preferably, the wing is a circulation controlled wing.

[0019] Step 2. Determine the external disturbance parameters of the wing affected by external disturbance factors such as gusts and waves, conduct a fluid simulation experiment on the wing based on the disturbance parameters, and obtain first experimental values of the external disturbance parameters and the wing lift coefficient; based on the first experimental values, construct a model of the influence of disturbance on the wing lift coefficient.

[0020] For example, the disturbance parameters of external disturbance factors such as gusts and waves are the wind speed of external vertical gusts or the height of water surface waves.

[0021] Specifically, a fluid simulation experiment is performed on the wing using a computational fluid dynamics numerical calculation method. During the experiment, a gust disturbance or wave disturbance is introduced using a moving boundary method to obtain a first experimental value of the wing lift changing with time after the disturbance.

[0022] See also Figure 3 , the lift fluctuation diagram after the wing is disturbed by water waves, it can be seen that the lift coefficient has obvious periodic changes, and the square curve is the secondary waveform caused by the periodic fluctuation of the water surface. Figure 4 , the lift coefficient fluctuation caused by the wing being disturbed by a step vertical gust of wind.

[0023] The vertical gust speed obtained and / or wave height H as external disturbance parameter of external disturbance factor, the corresponding wing lift coefficient The first experimental value of is used as the system variable, and the multi-layer perceptron (MLP) neural network model identification method is used to establish the model of the wing lift coefficient affected by external disturbances. The specific method is as follows: Specifically, the wing lift coefficient Set as the output variable of the model of the wing lift coefficient being affected by external disturbances, the wave height generated by external disturbances and gust speed As the input variable of the system (disturbance parameter of the external disturbance factor), the first experimental values of the wing lift coefficient under different external disturbances are calculated using fluid simulation experiments. The first experimental values of the wing lift coefficient are divided into a training dataset and a validation dataset in a ratio of 8:2 for training the model of the lift coefficient affected by disturbances.

[0024] Furthermore, the model for the lift coefficient being affected by disturbances is a neural network. The neural network architecture consists of an input layer, hidden layers, and an output layer. The input layer includes neurons for wave height and gust velocity. Two hidden layers are used, each with 20 neurons. The activation function is a rectified linear unit (ReLu), which effectively avoids the vanishing gradient problem and provides high processing efficiency.

[0025] Furthermore, the loss function of the neural network is expressed as:

[0026] in, is the first experimental value of the wing lift coefficient (i.e. the simulated wing lift coefficient), is the predicted lift coefficient value of the wing lift coefficient affected by external disturbances, and N is the number of samples.

[0027] Furthermore, the adaptive moment estimation (Adam) optimizer of the neural network is optimized, so that a model of the wing lift coefficient affected by external disturbances can be established.

[0028] The training process of the wing lift coefficient model under external disturbance is divided into four parts: forward propagation, loss calculation, back propagation and iterative training. In the forward propagation stage, the wave height generated by the external disturbance is input. and gust speed After passing through each layer of the neural network, after weighting and biasing, it is passed to the next layer through the activation function, and finally the predicted lift coefficient value of the output layer is obtained. The loss function is then used to calculate the loss, based on the predicted lift coefficient value and the first experimental values of the wing lift coefficient The error between them is used to obtain the mean square error of the loss function, which can reflect the difference between the predicted value and the true value. The back propagation algorithm is then used to calculate the gradient of each layer, and the weights and biases in the network are updated through the Adam method to achieve a round of optimization of the system parameters. After that, the system is iterated multiple times until the loss function is less than 0.001. After the training is completed, the model is verified and the trained model is verified using the data of the test set. If the mean square error between the results obtained by the trained model and the results in the test set is less than 0.001, the training results of the neural network are considered reliable. Once the training of the model in which the lift coefficient is affected by disturbances is completed and the model performs well on the test set, it can be used to estimate the lift coefficient under disturbance conditions. Given new input data ( and ) can be used to calculate the corresponding lift coefficient through the neural network model Make predictions.

[0029] Furthermore, in order to verify the accuracy of the identified gust disturbance model in predicting the gust response of the airfoil, an external disturbance of 1-COS type gust was applied to the airfoil, and the CFD simulation results were compared with the model prediction results. The results are as follows: Figure 6 It can be seen that the model obtained by neural network model identification can accurately predict the change of lift coefficient caused by gust disturbance.

[0030] Step 3. Determine the jet momentum coefficient of the wing during flight, conduct a fluid simulation experiment on the wing based on the jet momentum coefficient, and obtain a second experimental value of the jet momentum coefficient and the wing lift coefficient changing with time; based on the second experimental value, construct a model in which the wing lift coefficient is controlled by the jet momentum coefficient.

[0031] Furthermore, the momentum coefficient of the jet during wing flight is The expression is:

[0032] in, is the mass flow rate at the jet orifice; is the jet velocity; is the far-field incoming current density; is the far-field incoming flow velocity; S represents the area of the jet hole. For a two-dimensional airfoil, it is the chord length c of the airfoil.

[0033] Specifically, the fluid simulation experiment is as follows: using the velocity inlet boundary condition in the simulation software, introducing a jet at the jet hole whose jet momentum coefficient changes with time and satisfies the random white noise, and calculating the second experimental value of the wing lift coefficient and the jet momentum coefficient changing with time.

[0034] Furthermore, the model of the wing lift coefficient affected by external disturbances is a neural network; the jet momentum coefficient Set as the input variable of the neural network, and the second experimental value of the wing lift coefficient as the output variable of the neural network. Use the multi-layer perceptron (MLP) system identification method to build a model in which the wing lift coefficient is controlled by the jet momentum coefficient. The method is the same as step 2 and will not be repeated here. The difference between the two is that the neural network input layer of the prediction model of the lift coefficient being affected by the jet momentum coefficient only has the momentum coefficient One input quantity, so the input layer has only one neuron. In this way, a model of the wing lift coefficient being controlled by the jet momentum coefficient can be established, as shown in the schematic diagram. Figure 7 shown.

[0035] Step 4. Based on the model of the wing lift coefficient being affected by disturbance and the model of the wing lift coefficient being controlled by the jet momentum coefficient, a disturbance mitigation control model is obtained, in which the input is the external disturbance parameter and the output is the jet momentum coefficient.

[0036] Specifically, this model can estimate the fluctuations in the lift coefficient caused by external disturbances, calculate the jet momentum coefficient required to maintain lift stability, and thus achieve stable control of the lift coefficient.

[0037] Specifically, the disturbance mitigation control model uses an active disturbance rejection controller (ADRC) to achieve stable control of the lift coefficient. The specific design scheme is as follows: Since the ADRC controller has the advantages of strong robustness and fast response, the present invention uses the ADRC controller to control the jet intensity. The entire ADRC controller is a closed-loop feedback system. The total lift coefficient fluctuation needs to be input into the ADRC controller as feedback. Then, the controller calculates the control quantity jet momentum coefficient based on this information. The structural diagram of the control system is shown in the figure below. Figure 8 shown.

[0038] Step 5. Use the disturbance mitigation control model to obtain the jet momentum coefficient required for the aircraft to ensure the stability of the lift coefficient during flight. Control the jet momentum coefficient of the jet on the wing to be the required jet momentum coefficient, which can slow down the fluctuation of the lift coefficient and complete the disturbance mitigation control of the wing.

[0039] It can be understood that the jet momentum coefficient is the control quantity of the jet circulation control. In practical applications, different degrees of lift enhancement are achieved by changing the momentum coefficient of the jet, thereby reducing the lift coefficient fluctuations caused by external interference.

[0040] Lift coefficient fluctuation refers to changes in an aircraft's lift coefficient during flight due to external disturbances such as gusts, which can cause turbulence or even a descent. To reduce the amount of lift variation after a disturbed aircraft is encountered during flight, this patent uses jets to alter the flow state, increasing the airflow velocity over the wing surface and thereby increasing the aircraft's lift coefficient. Therefore, when lift is reduced due to gusts, the jet control can be used to increase the lift coefficient, thereby maintaining lift stability.

[0041] Applying this method, the load reduction verification is implemented in the simulation software under the external disturbance of waves and gusts, and the effect is as follows: Figure 9 、 10 As shown in the figure, the lift coefficient of the aircraft under wave or gust disturbance is significantly reduced, which enhances the flight stability of the aircraft in a disturbed environment.

[0042] The above is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed in the present invention should be covered by the scope of protection of the present invention.

Claims

1. A control method for improving the anti-interference performance of an aircraft based on jet flow, characterized in that: The specific steps are: Step 1. Arrange jet holes on the upper surface of the wing to form a jet flap; Step 2. Determine external disturbance parameters, conduct a fluid simulation experiment on the wing based on the disturbance parameters, and obtain first experimental values of the external disturbance parameters and the wing lift coefficient; and construct a model for the influence of disturbance on the wing lift coefficient based on the first experimental values; Step 3. Determine the jet momentum coefficient of the wing during flight, perform a fluid simulation experiment on the wing based on the jet momentum coefficient, and obtain a second experimental value of the relationship between the jet momentum coefficient and the lift of the wing and time; Based on the second experimental value, a model is constructed in which the lift coefficient of the wing is controlled by the jet momentum coefficient; Step 4. Based on the model of the wing lift coefficient being affected by disturbances and the model of the wing lift coefficient being controlled by the jet momentum coefficient, a disturbance mitigation control model is obtained, in which the input is the external disturbance parameter and the output is the jet momentum coefficient. Step 5. Use the disturbance mitigation control model to obtain the jet momentum coefficient required for the aircraft to ensure the stability of the lift coefficient during flight, and control the jet momentum coefficient of the jet on the wing to be the required jet momentum coefficient.

2. The jet-based control method for improving the anti-interference performance of an aircraft according to claim 1, characterized in that: The external disturbance parameters are the gusts and / or waves that the wing is subjected to.

3. The jet-based control method for improving the anti-interference performance of an aircraft according to claim 1, characterized in that: Gusts are the wind speed of vertical gusts generated by external forces; waves are the height of water surface waves generated by external forces.

4. The jet-based control method for improving the anti-interference performance of an aircraft according to claim 2, characterized in that: The lift coefficient disturbance effect model is a neural network; the network architecture of the neural network includes an input layer, a hidden layer and an output layer.

5. The jet-based control method for improving the anti-interference performance of an aircraft according to claim 3, characterized in that: The model of the wing lift coefficient affected by external disturbances is a neural network; the network architecture of the neural network includes an input layer, a hidden layer and an output layer.

6. The jet-based control method for improving the anti-interference performance of an aircraft according to claim 4, characterized in that: The input layer includes wave height neurons and gust speed neurons.

7. The jet-based control method for improving the anti-interference performance of an aircraft according to claim 5, characterized in that: The input layer includes momentum coefficient neurons.

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