A method for predicting the trajectory of aircraft external stores separation

Abstract

An aircraft external stores separation trajectory prediction method is proposed. This method uniformly selects known deployment states within the aircraft's flight envelope, acquires the separation trajectories corresponding to each known deployment state, forming a known deployment separation data matrix. The target deployment state and its corresponding unknown separation trajectory are assembled into a vector. The unknown separation trajectory data is then approximated based on the known deployment separation data matrix. The finally updated separation trajectory data is used as the external stores separation trajectory corresponding to the target deployment state, thus completing the external stores separation trajectory prediction. This prediction method can effectively extract the features of external stores deployment separation trajectory data obtained through numerical simulations or wind tunnel experiments, quickly and accurately predicting the external stores separation trajectory corresponding to the target deployment state, effectively reducing the external stores deployment separation calculation cycle.

Description

Technical Field

[0001] This invention relates to a method for predicting the trajectory of external stores, specifically, a method for predicting the separation trajectory of external stores from an aircraft. Background Technology

[0002] The release and separation of external stores is a crucial technology in the aviation field. This technology involves multiple factors, including the interaction between the aircraft and the external stores, the physical characteristics of the external stores themselves, and multibody aerodynamics. The safe release and separation of external stores is not only vital to the flight safety of the aircraft but also determines whether the external stores can complete their intended delivery mission. Factors affecting the separation motion of external stores include: the mass of the external stores, the aerodynamic shape of the external stores, the speed of the aircraft at the time of release, the angle of attack of the aircraft, the sideslip angle of the aircraft, and the ejection force. The separation trajectory of the external stores is important reference data for analyzing the safety of release and separation. Currently, existing technologies mainly obtain the separation trajectory of external stores under different operating conditions through three methods: numerical simulation calculation, wind tunnel experiments, and flight tests. Each of these methods has its advantages and disadvantages. Among them, wind tunnel experiments and flight tests are direct means of studying the release and separation of external stores, and can obtain release and separation data relatively quickly. However, flight tests have disadvantages such as high risk and high research costs, limiting their widespread application. While wind tunnel testing carries lower risk than flight testing, the amount of deployment and separation motion data obtained through wind tunnel testing is limited due to constraints such as research funding, research time, and equipment precision. With the development of Computational Fluid Dynamics (CFD), CFD-based numerical simulation methods are now widely used in external payload deployment and separation research. Numerical simulation can relatively easily analyze the influence of flow field conditions, external payload aerodynamic shape, and launch force on the external payload separation motion. However, when there are too many conditions to simulate, the computational resources and computation time required for numerical simulation will also increase. Therefore, in engineering applications, the amount of external payload deployment and separation motion data obtained through flight testing, wind tunnel testing, and numerical simulation is limited due to constraints such as research funding and research time.

[0003] Predicting separation trajectories based on limited deployment and separation data can effectively improve the efficiency of deployment and separation calculations by obtaining the separation trajectory of external stores corresponding to any deployment state within the aircraft's flight envelope. The unknown separation trajectory of an external store in a given deployment state can be considered as missing data. By using the known separation trajectory data corresponding to the deployment state and employing intrinsic orthogonal decomposition to extract data features for missing data prediction, the missing separation trajectory can be calculated quickly. Summary of the Invention

[0004] Purpose of the invention

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method for predicting the trajectory of aircraft external stores separation.

[0006] Technical solution

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A method for predicting the trajectory of external attachments separation from an aircraft, comprising the following steps:

[0009] Step 1: Select m known delivery states uniformly within the flight envelope of the carrier aircraft, obtain the separation trajectory corresponding to each known delivery state, and form a known delivery separation data matrix T;

[0010] Step 2: Select a target deployment state and assemble the target deployment state and the corresponding unknown separation trajectory into a vector U. The element form of vector U is the same as the row vector of matrix T.

[0011] Step 3: Based on the known deployment separation data matrix T, approximate the unknown separation trajectory data in vector U, and denote the replaced vector as U';

[0012] Step 4: Combine the known distributed data matrix T and vector U' to form a new matrix T', and calculate the basic modes of matrix T' based on the eigenorthogonal decomposition method;

[0013] Step 5: Select the first p-th order basis modes of matrix T' to fit vector U', and update the separation trajectory data in vector U' based on the fitting results;

[0014] Step 6: Repeat steps 4) and 5) to iteratively fit the separation trajectory data in U' until the magnitude of the difference before and after the vector U' update is less than the set threshold or the upper limit of the number of iterations is reached. Then, use the finally updated separation trajectory data in vector U' as the external object separation trajectory corresponding to the target deployment state to complete the external object separation trajectory prediction.

[0015] Preferably, in step 1), the i-th row vector T of the separated data matrix T is projected. i The form is shown in formula (1):

[0016] T i =[H,Ma,a,β,x1,y1,z1,…,x N ,y N ,z N (1)

[0017] In the formula: Ma, H, α, and β are the aircraft's Mach number, altitude, angle of attack, and sideslip angle, respectively, describing the deployment state; x, y, and z are the positions of the center of mass during the separation motion of the external attachments; and the subscript N represents the separation motion time sequence number.

[0018] Preferably, the approximate substitution method described in step 3) is as follows: for the unknown element in the i-th column of vector U, the average value of the elements in the i-th column of matrix T is used as the substitution.

[0019] Preferably, the method for determining the value of p in step 5) is to take the first p-th order basic modes with a cumulative explanatory power greater than 99%.

[0020] Preferably, the fitting method in step 5) is as shown in formula (2):

[0021]

[0022] Where vector U' is an approximate replacement for vector U. Let w be the mean vector of the column elements of matrix T. i Φ represents the modal coefficients of each order. i Let w be the i-th fundamental mode. The modal coefficients w for each order are... i The calculation method is shown in formula (3):

[0023]

[0024] Beneficial effects

[0025] This prediction method extracts features from known deployment and separation motion data using intrinsic orthogonal decomposition, enabling the prediction of unknown separation trajectories under target deployment conditions. Compared to current quasi-steady simulation methods based on computational fluid dynamics, this invention's prediction method features faster computation speed and higher prediction accuracy. Attached Figure Description

[0026] Figure 1 The graph compares the predicted separation trajectories with the actual values. The solid lines represent the actual values, and the scatter points represent the predicted values. The values ​​in the graph are dimensionless. Detailed Implementation

[0027] According to the present invention, a method for predicting the trajectory of aircraft external stores separation includes the following steps:

[0028] Step 1: Describe the external payload release state using parameters such as aircraft Mach number, altitude, angle of attack, and sideslip angle. Uniformly select 120 release states within the aircraft's flight envelope. Calculate the separation trajectory corresponding to each release state using quasi-steady numerical simulation, forming a release separation data matrix T. The separation motion time in the numerical simulation is 1 second, discretized into 40 time intervals. The i-th row vector of matrix T is T_i. i The form is shown in formula (1):

[0029] T i =[H,Ma,a,β,x1,y1,z1,…,x N ,y N ,z N(1)

[0030] Step 2: Randomly generate a set of delivery states within the flight envelope of the carrier aircraft as the target delivery states, and assemble the target delivery states and the corresponding unknown separation trajectories into a vector U;

[0031] Step 3: Based on the known deployment separation data matrix T, approximate the unknown separation trajectory data in vector U. For the unknown element in the i-th column of vector U, take the average value of the elements in the i-th column of matrix T for approximate replacement, and denote the replaced vector as U';

[0032] Step 4: Combine the known distributed data matrix T and vector U' to form a new matrix T', and calculate the basic modes of matrix T' based on the eigenorthogonal decomposition method;

[0033] Step 5: Select the first 8 fundamental modes of matrix T' to fit vector U', and update the separation trajectory data in vector U' based on the fitting results;

[0034] Step 6: Repeat steps 4) and 5) to iteratively fit the separation trajectory data in U' until the maximum number of iterations of 50 is reached. Use the final updated separation trajectory data in vector U' as the external object separation trajectory corresponding to the target deployment state to complete the external object separation trajectory prediction.

[0035] The trajectory of the external attachment separation in the target deployment state is calculated using a computational fluid dynamics-based method and used as the true value. This trajectory is then compared with the trajectory of the external attachment separation in vector U'. Figure 1 As shown in the figure, the solid line represents the actual value, and the scatter points represent the predicted value. The values ​​in the figure are dimensionless. It can be seen that the predicted trajectory of the external attachment separation motion of this method almost coincides with the actual value, demonstrating a fairly high prediction accuracy. At the same time, the computation time of this method is about 2 seconds, which is significantly improved compared to the 16 hours required by the numerical simulation method based on computational fluid dynamics.

[0036] The specific embodiments of the present invention have been described above. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention. These improvements and modifications should also be within the scope of protection of the present invention.

Claims

1. A method for predicting the trajectory of aircraft external stores separation, characterized in that... The process includes the following steps: 1) Uniformly select m known deployment states within the flight envelope of the carrier aircraft, obtain the separation trajectories corresponding to each known deployment state, and form a known deployment separation data matrix T; 2) Select a target deployment state and assemble the target deployment state and the corresponding unknown separation trajectory into a vector U, where the element form of vector U is the same as the row vector of matrix T; 3) Approximate the unknown separation trajectory data in vector U according to the known deployment separation data matrix T, and denote the substituted vector as U'; 4) Combine the known deployment separation data matrix T and vector U' to form a new matrix T', and calculate the fundamental modes of matrix T' based on the eigenorthogonal decomposition method; 5) Fit the vector U' with the first p-order fundamental modes of matrix T', and update the separation trajectory data in vector U' according to the fitting results; 6) Repeat steps 4) and 5) to iteratively fit the separation trajectory data in U' until the magnitude of the difference before and after the update of vector U' is less than a set threshold or the upper limit of the number of iterations is reached. Finally, use the updated separation trajectory data in vector U' as the external attachment separation trajectory corresponding to the target deployment state to complete the external attachment separation trajectory prediction.

2. The method for predicting the trajectory of aircraft external stores separation as described in claim 1, characterized in that, In step 1), the i-th row vector T of the separated data matrix T is projected. i The form is shown in formula (1): T i [H,Ma,a,β,x1,y1,z1,…,x N ,y N ,z N ] (1) In the formula: Ma, H, α, and β are the aircraft's Mach number, altitude, angle of attack, and sideslip angle, respectively, describing the deployment state; x, y, and z are the positions of the center of mass during the separation motion of the external attachments; and the subscript N represents the separation motion time sequence number.

3. The method for predicting the trajectory of aircraft external stores separation as described in claim 1, characterized in that, The approximate substitution method described in step 3) is as follows: for the unknown element in the i-th column of vector U, take the average value of the elements in the i-th column of matrix T as the substitution.

4. The method for predicting the trajectory of aircraft external stores separation as described in claim 1, characterized in that, In step 5), the value of p is determined by taking the first p-th order basic modes with a cumulative explanatory power greater than 99%.

5. The method for predicting the trajectory of aircraft external stores separation as described in claim 1, characterized in that, The fitting method in step 5) is shown in formula (2): Where vector U' is an approximate replacement of vector U, U is the mean vector of the column elements of matrix T, and w i Φ represents the modal coefficients of each order. i For the i-th fundamental mode, the modal coefficients w of each order are... i The calculation method is shown in formula (3): .

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