A comprehensive modeling method for the fusion structural shape parameters of aerospace vehicles

Abstract

The present invention discloses a comprehensive modeling method for integrating the structural shape parameters of an aerospace vehicle. The method comprises the following steps: establishing a computing power data model for the vehicle that takes into account the structural shape parameters; obtaining an aerodynamic / propulsion database for different structural shape parameters; and determining the key parameters of the computing power model using harmonic analysis. Combining the dynamics and kinematic equations of a six-degree-of-freedom vehicle, an integrated model of the aerospace vehicle integrating multiple shape structures is constructed. The validity of the constructed equivalent model is then verified using goodness-of-fit. The influence of changes in structural shape parameters on the performance of the aerospace vehicle model is analyzed, and a correlation between flight performance and structural shape parameters is established, providing excellent technical support for practical engineering design.

Description

Technical Field

[0001] The invention relates to a comprehensive modeling method for integrating structural shape parameters of an aerospace vehicle. Background Art

[0002] Future aerospace vehicles will operate across subsonic, supersonic, and aerospace sonic speeds, performing a variety of specialized missions. Their flight envelopes will vary greatly. The use of combined propulsion and variable configuration adjustment technologies will enable vehicles to maintain satisfactory flight performance across different missions, as well as within each segment of a given mission. This will effectively increase range and conserve energy. Furthermore, they will be able to perform multiple missions, possessing enhanced environmental adaptability, resilience, attack, and survivability. However, the strong coupling between aerospace vehicle aerodynamics, propulsion, control, and structure, coupled with large external disturbances and an unknown flight environment, makes model uncertainty difficult to estimate and predict. Furthermore, the need to adjust propulsion modes during motion to meet performance requirements in different speed ranges presents new challenges to aerospace vehicle model design.

[0003] The unique aerodynamic / propulsion integrated configuration of aerospace vehicles leads to a strong coupling between their aerodynamics, propulsion, structure, and control. Traditional modeling methods may no longer be applicable to such complex objects. It is necessary to adopt a comprehensive modeling method that integrates structural and external parameters to construct a comprehensive model of aerospace sonic vehicles that integrates these parameters. By coordinating the matching relationship between aerospace vehicle structural and external parameters and performance quality, the overall performance of the vehicle can be improved, the flight range can be expanded, and the mission requirements of aerospace vehicles within a wide speed range can be met. This will provide theoretical reserves and technical support for my country's future independent development of new aerospace vehicles. Summary of the Invention

[0004] In order to solve the defects of the existing technology, a comprehensive modeling method for the fusion structure shape parameters of an aerospace vehicle is provided.

[0005] Technical solution: A comprehensive modeling method for integrating structural parameters of aerospace vehicles, including the following steps:

[0006] Step 1: Establish a computing power data model of the aerospace vehicle's fusion structure and shape parameters;

[0007] Step 2: Extract key parameters from the computing power model of the integrated structural shape parameters and determine the equivalent computing power model of the aerospace vehicle;

[0008] Step 3: Construct an integrated aerospace vehicle model integrating structural and shape parameters;

[0009] Step 4: Analyze and verify the aircraft model of the fusion structure shape parameters.

[0010] Furthermore, the step 1 includes the following steps:

[0011] Step 11: The aerospace vehicle is divided into fuselage cross-section shapes and airfoil shapes. For the fuselage cross-section shape, the cross-section curve characteristic parameters and the changes in the cross-section curve characteristic parameters are used to describe the geometric shape of the vehicle.

[0012] Step 12: Based on the geometric shape of the aircraft, apply engineering estimation methods to obtain an aerodynamic / propulsion database that considers different structural shape parameters, and establish a computing power data model that integrates the structural shape parameters.

[0013] Furthermore, the method for extracting key parameters from the computing power data model of the fusion structure shape parameters in step 2 is:

[0014] The main influencing factors of the aerodynamic force / torque coefficient are determined by the harmonic analysis method. If the frequency of the input signal of the computing power data model satisfies the sampling frequency that is higher than twice the highest frequency of the input signal; the influence of the input signal on the output signal can be determined by analyzing the spectrum of the output signal. The Fourier series is applied to analyze the components of the Fourier series, and the main factors affecting the output signal are determined, and finally the key parameters of the computing power model are determined.

[0015] Furthermore, the method for determining the equivalent model in step 2 is:

[0016] Considering the structural shape adjustment parameters, it is necessary to establish an equivalent model that integrates the shape parameters. First, determine the model structure with flight conditions and motion states as variables, and then simplify the undetermined coefficients of the expression into a form related to the structural shape parameters.

[0017] For the determined aerospace vehicle computing power model structure, the least squares method is used to establish the equivalent model coefficients, and then different fitting variables are evaluated and analyzed within a wide speed domain. The complex aerodynamic forces, torques and thrust expressions of the aircraft are expressed as nonlinear functions of the aircraft conditions and structural shape parameters, and an equivalent model of aerospace vehicle computing power with multiple shape variable parameters is obtained.

[0018] Furthermore, the step 3 includes:

[0019] Step 31: Based on the changed aircraft structural parameters and in combination with the six-degree-of-freedom aircraft dynamics and kinematics equations, an integrated model of the aerospace vehicle integrating multiple external structures is constructed;

[0020] Step 32: Consider the structural shape parameters as parameters and combine them with the flight state to determine the relationship between the integrated model, the flight state, and the structural shape parameters;

[0021] Step 33: Consider the structural shape parameters of the aerospace vehicle as the undetermined coefficients of the model, and simplify the integrated model of the multi-shape structure to obtain an equivalent model of the fused structural shape parameters.

[0022] Furthermore, in step 4, the aircraft model of the fusion structure shape parameters is analyzed and verified, specifically by using variance ratio, root mean square error, maximum standard residual and goodness of fit to verify the model:

[0023] (a) Variance ratio

[0024]

[0025] (b) Root mean square error

[0026]

[0027] (c) Maximum standard residual

[0028]

[0029] (d) Goodness of fit

[0030]

[0031] Among them, C is the verification data, Calculate data for the model, C i is the i-th verification data, Calculate data for the i-th model, is the mean value of the validation data, is the model average.

[0032] Beneficial effects:

[0033] Compared to conventional aircraft, aerospace vehicles have higher overall performance requirements. They not only need to fly across domains to complete their missions, but also require comprehensive consideration of the collaborative design of multiple systems to ensure stability across a wide speed range and suppress the effects of strong disturbances. By adopting a proposed comprehensive modeling method for integrating structural and shape parameters of aerospace vehicles, we can coordinate the matching relationship between structural and shape parameters and performance quality, improve their mission execution capabilities across a wide speed range, expand their flight range, and meet the multi-objective mission requirements of aerospace vehicles within a wide speed range. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flow chart for constructing the aerospace vehicle computing power data model of the present invention.

[0035] Figure 2 It is a flow chart for constructing an equivalent model of aerospace vehicle computing power.

[0036] Figure 3 It is a flow chart for performance analysis of an integrated aerospace vehicle model. DETAILED DESCRIPTION

[0037] Step 1: Establish a computing power data model of the aerospace vehicle fusion structure shape parameters.

[0038] For typical aerospace vehicle configurations, the geometric parameters used to describe their shape characteristics are determined based on their shape characteristics and modeling requirements, and the independence of these parameters and the constraints between the parameters are analyzed to obtain a complete set of geometric parameters used to describe the vehicle. By comprehensively applying spatial geometry construction methods such as shape type functions, cubic curves, and Bezier curves, the vehicle structural components are constructed separately, and finally the complete aerospace vehicle geometric shape and characteristic parameters are obtained through the combination of components. The obtained parametric geometric configuration of the aerospace vehicle is divided into facets to obtain the characteristic parameters of each facet. The aircraft's computing power database is obtained using engineering estimation methods, and a computing power data model is established, such as Figure 1 shown.

[0039] Step 2: Extract key parameters from the computing power model of the integrated structural shape parameters and determine the equivalent model

[0040] The Latin hypercube sampling method is used to optimize the selection of reasonable sample points in the model database. Unlike traditional uniform sampling, the Latin hypercube sampling method samples every dimension in the sample space, and the number of samples is the number of samples. Improved Latin hypercube sampling methods include optimal Latin square sampling, Latin square sampling based on orthogonal arrays, and optimal Latin square sampling based on orthogonal arrays. Optimal Latin square sampling uses the evaluation index of the experimental design as the optimization target and uses a reasonable optimization algorithm to optimize the sample space. Latin hypercube sampling based on orthogonal arrays can improve sampling performance by changing the arrangement order, thereby optimizing the sample potential energy, thereby achieving optimal orthogonal Latin hypercube sampling based on orthogonal arrays.

[0041] Harmonic analysis is used to determine the primary influencing factors of the aerodynamic force / torque coefficients. The specific process is as follows: If the frequency of the input signal to the aerospace vehicle computing power data model meets certain requirements, the output signal's frequency spectrum can be analyzed to determine the form of the input signal's influence on the output signal. Essentially, the response in a finite sample space is treated as a periodic signal. By studying its Fourier series and analyzing its components, the primary factors influencing the output signal are determined. Alternatively, the output signal of the aerospace vehicle computing power data model can be treated as a random signal. By analyzing the correlation coefficient between the random signal and the input correlation signal (which can be in various forms: primary, secondary, cubic, and cross-correlated with other factors), the form and magnitude of the influence can be analyzed. Mathematically, this is the intensity of the output signal's power spectrum at the frequency of the input correlation signal.

[0042] If the sampling frequency f s Satisfy the conditions: f s >2f o,max , where f o,max is the maximum input signal frequency. Assume that the frequency of the input signal of the single factor of the aerospace vehicle computing power data model is f in , then the spectrum of the output signal satisfies the following conditions:

[0043] Table 1 Relationship between the output spectrum of the aerospace vehicle computing power data model and the number of input factors

[0044]

[0045] Among them, f1 and f2 are the frequency spectra corresponding to the factor orders x1 and x2 respectively. Through Table 1, the relationship between the output and input of the aerospace vehicle model data can be judged, thereby determining the key parameters in the computing power model. Taking into account the structural shape adjustment parameters, it is necessary to establish an equivalent model that integrates the shape parameters. First, determine the model structure with flight conditions and motion states as variables, and then simplify the undetermined coefficients of the expression into a form related to the structural shape parameters. For the determined aerospace vehicle computing power model structure, the least squares method is used to establish the equivalent model coefficients, and then evaluate and analyze different fitting variables within a wide speed range, and express the complex aerodynamic force, torque and thrust expressions of the aircraft as nonlinear functions of the aircraft conditions and structural shape parameters, and obtain a computing power equivalent model with multiple shape variable parameters, such as Figure 2 shown.

[0046] Step 3: Establish an integrated model of the aerospace vehicle taking into account the structural and shape parameters.

[0047] Based on the varying vehicle structural parameters and combined with the six-degree-of-freedom vehicle dynamics and kinematic equations, an integrated model of aerospace vehicles integrating multiple configurations is constructed. This strategy then designs an inverse integrated adjustment strategy for the aerospace vehicle's force-shock wave shape-geometry-shock wave shape-force. Under different flight conditions, the shock wave effects of the aerospace vehicle's variable structural configuration are considered separately. The forces acting on the shock wave are estimated by studying the relationship between the shock wave, the flight state, and the airflow between the shock waves. Alternatively, the shock wave force required for stable flight can be determined based on the flight state, and the shock wave shape of the vehicle's structural configuration can be designed. This inverse design approach is then used to design the geometry. This approach breaks away from the conventional configuration-force design model in aircraft design. Its greatest advantage lies in its ability to rapidly estimate the aerodynamic force based on the vehicle's geometry and rapidly adjust the vehicle's geometry based on the required force. This approach is suitable for the study of aerospace vehicles with multiple configurations.

[0048] The structural shape parameters are considered as parameters and combined with the flight state to determine the relationship between the integrated model and the flight state and structural shape parameters. The modal analysis method is used to conduct a compromise analysis of the equilibrium state and dynamic characteristics of the aerospace vehicle model. The flight conditions and structural shape deformations are considered to affect the static equilibrium characteristics (flight envelope, trim surfaces, angle of attack and thrust) and dynamic characteristics (unstable poles and right half plane zeros). Combined with the input and state constraints of the object model, the system characteristics are analyzed according to the change of flight state and the performance limits that the control system can achieve, mainly including tracking performance, robust performance, flight controllable boundaries, etc. Then, the sensitivity analysis strategy is applied to explore the impact of flight condition changes and structural shape deformation on stability, analyze the intrinsic relationship between them and the dynamic characteristics of the model, determine the controllable safety boundary of the aircraft, determine the key structural shape parameters, and the model characteristic analysis scheme is as follows. Figure 3 shown.

[0049] Based on the results of model performance analysis, the structural shape parameters of the aerospace vehicle are considered as the undetermined coefficients of the model according to the inverse integrated design strategy. By analyzing the impact of changes in structural shape parameters on the model performance, the integrated model of multiple shape structures is simplified to obtain an equivalent model of the fused structural shape parameters.

[0050] Step 4: Analyze and verify the aircraft model of the fusion structure shape parameters.

[0051] To evaluate the resulting aircraft model based on the fused structural parameters, metrics such as variance factor (VAF), root mean square error (RMSE), maximum standard residual (MSR), and goodness of fit (GOF) were introduced to validate the model. If the equivalent model has high accuracy, the validity of the constructed model is verified; otherwise, the process returns to step 2 and redefines the equivalent model structure.

[0052] a) Variance ratio

[0053]

[0054] b) Root mean square error

[0055]

[0056] c) Maximum Standard Residual

[0057]

[0058] d) Goodness of fit

[0059]

[0060] Among them, C is the verification data, Calculate data for the model, C iis the i-th verification data, Calculate data for the i-th model, is the mean value of the validation data, is the model average.

Claims

1. A comprehensive modeling method for integrating structural parameters of an aerospace vehicle, characterized in that: The steps include: Step 1: Establish a computing power data model of the aerospace vehicle's fusion structure and shape parameters; Step 2: Extract key parameters from the computing power model of the integrated structural shape parameters and determine the equivalent computing power model of the aerospace vehicle; Step 3: Construct an integrated aerospace vehicle model integrating structural and shape parameters; Step 4: Analyze and verify the aircraft model of the fusion structure shape parameters; The step 1 comprises the following steps: Step 11: The aerospace vehicle is divided into fuselage cross-section shapes and airfoil shapes. For the fuselage cross-section shape, the cross-section curve characteristic parameters and the changes in the cross-section curve characteristic parameters are used to describe the geometric shape of the vehicle. Step 12: Based on the geometric shape of the aircraft, an engineering estimation method is applied to obtain an aerodynamic / propulsion database that considers different structural shape parameters, and a computing power data model that integrates the structural shape parameters is established; The method for extracting key parameters from the computing power data model of the fusion structure shape parameters in step 2 is: The main influencing factors of the aerodynamic force / torque coefficients are determined through harmonic analysis. If the frequency of the input signal of the computing power data model satisfies the sampling frequency that is higher than twice the highest frequency of the input signal, the influence of the input signal on the output signal can be determined by analyzing the spectrum of the output signal. The Fourier series is applied to analyze the components of the Fourier series to determine the main factors affecting the output signal, and ultimately determine the key parameters of the computing power model. The method for determining the equivalent model in step 2 is: Considering the structural shape adjustment parameters, it is necessary to establish an equivalent model that integrates the shape parameters. First, determine the model structure with flight conditions and motion states as variables, and then simplify the undetermined coefficients of the expression into a form related to the structural shape parameters. For the determined aerospace vehicle computing power model structure, the least squares method is used to establish the equivalent model coefficients. Then, different fitting variables are evaluated and analyzed over a wide speed range. The complex aerodynamic forces, torques, and thrust expressions of the aircraft are expressed as nonlinear functions of the aircraft conditions and structural shape parameters, and an equivalent model of the aerospace vehicle computing power with multiple shape-variable parameters is obtained. The step 3 comprises: Step 31: Based on the changed aircraft structural parameters and in combination with the six-degree-of-freedom aircraft dynamics and kinematics equations, an integrated model of the aerospace vehicle integrating multiple external structures is constructed; Step 32: Consider the structural shape parameters as parameters and combine them with the flight state to determine the relationship between the integrated model, the flight state, and the structural shape parameters; Step 33: Consider the structural shape parameters of the aerospace vehicle as the undetermined coefficients of the model, and simplify the integrated model of the multi-shape structure to obtain an equivalent model of the fused structural shape parameters.

2. The comprehensive modeling method for integrating structural shape parameters according to claim 1, characterized in that: In step 4, the aircraft model of the fusion structure shape parameters is analyzed and verified, specifically by using variance ratio, root mean square error, maximum standard residual and goodness of fit to verify the model: (a) Variance ratio (b) Root mean square error (c) Maximum standard residual (d) Goodness of fit Among them, C is the verification data, Calculate data for the model, C i is the i-th verification data, Calculate data for the i-th model, is the mean value of the validation data, is the model average.

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