48 results about "Aerodynamic coefficient" patented technology

The invention relates to the technical field of flight safety of civil aircrafts, in particular to a flight trajectory prediction method under the condition of air high-speed target hood perforation, which comprises the following steps: acquiring calculation coefficient parameter data of a target flight dynamics model, establishing a trajectory prediction framework based on a three-degree-of-freedom target flight dynamics model and target real-time flight data; the method comprises the following steps: performing numerical simulation on a flow field of a hood perforation area through fluid mechanics analysis software to obtain speed field and pressure field data, establishing a hood perforation flow field pneumatic analysis model by adopting a high-order polynomial fitting method, and calculating Mach number and pressure distribution after hood perforation; dynamically updating aerodynamic coefficient data and an aerodynamic parameter database; the speed and pressure distribution data are introduced into the three-degree-of-freedom target flight dynamics model for registration updating, a prediction track is obtained, the prediction precision is improved by 30%-50%, and especially under the condition of a large angle of attack, the prediction error is controlled to be within 5% from 20% or above.

An intelligent unmanned aerial vehicle pneumatic structure reduced-order analysis model comprises a pneumatic reduced-order model and a structure reduced-order model. The aerodynamic structure order reduction analysis process comprises the steps that an aerodynamic order reduction model and a structure order reduction model are initialized; under the given calculation working condition, the aerodynamic load acting on the intelligent unmanned aerial vehicle structure is obtained through calculation of the aerodynamic reduced-order model; transmitting the load on the pneumatic grid to the structural grid through a force interpolation algorithm; solving deformation response by the structure order reduction model under the action of aerodynamic load; the structural deformation is fed back to the pneumatic grid through displacement interpolation, and the pneumatic configuration is updated; and performing iterative calculation until a convergence criterion is met, finally obtaining a pneumatic structure analysis result in an equilibrium state, and outputting a pneumatic coefficient and structural deformation and stress. According to the method, the CFD / CSD model is replaced by the equivalent model, so that the calculation efficiency is remarkably improved on the premise of ensuring the calculation precision.

The invention relates to an aircraft flow field and aerodynamic performance joint prediction method and system based on multi-task learning. The method comprises the following steps: collecting flow field data sets of different symmetric airfoils; after data preprocessing is carried out on the flow field data set, an input and output data set is constructed; constructing a neural network model, wherein the neural network model comprises a backbone network, a branch network and a fusion network; taking the space time sequence coordinates as input of the backbone network, and outputting backbone features; taking the static input characteristics and the historical flow field time sequence data as inputs of the branch network, and correspondingly outputting flow field prediction branch characteristics and aerodynamic coefficient prediction branch characteristics respectively; outputting a predicted value through the fusion network; constructing a weighted loss function; training the neural network model; and carrying out neural network model lightweight processing, and carrying out flow field prediction. The flow field characteristic prediction of the aircraft under the flow condition is realized quickly and accurately.

The invention belongs to the technical field of unmanned aerial vehicle aircraft design, and particularly relates to a longitudinal aerodynamic parameter identification calculation method in a closed-loop state and a large angle-of-attack range, and the method comprises the steps: building a flight simulation model; performing aerodynamic characteristic simulation based on a flight simulation model to obtain an aerodynamic load value, comparing the aerodynamic load value with data tested in an actual flight experiment, and calculating and minimizing an error sum of squares; dividing the whole angle-of-attack range into a plurality of sub-intervals, respectively obtaining the optimal aerodynamic parameters of each sub-interval based on the minimum sum of squares of errors, inputting each optimal aerodynamic parameter into the flight simulation model, and carrying out model accuracy verification; and drawing an aerodynamic characteristic curve according to each optimal aerodynamic parameter, and verifying the applicability of the flight simulation model. The method has universality and is suitable for longitudinal aerodynamic coefficient parameter identification of different types of unmanned aerial vehicles, the parameter identification efficiency is improved, and meanwhile the applicability of the model under the complex flight working condition is enhanced.

The invention relates to the technical field of aircraft aerodynamic analysis, and discloses an aircraft full envelope aerodynamic database intelligent generation method and system, and the method comprises the steps: obtaining the aerodynamic coefficients of an aircraft under different combination conditions through simulation, constructing an aerodynamic coefficient control derivative matrix of the aircraft under the corresponding combination conditions, a continuous derivative matrix is generated through three-dimensional grid interpolation, and aircraft full envelope control derivative matrix coverage is achieved; and performing global correction on the continuous derivative matrix through a method of combining experimental testing and simulation analysis. According to the method, CFD simulation and deep fusion of test data can be realized, and the prediction accuracy and reliability of the pneumatic database are improved; the method can ensure that the generated aircraft full envelope pneumatic database can reflect the pneumatic characteristics of the aircraft more truly, has the characteristics of high efficiency and accuracy, and can greatly improve the efficiency and accuracy of aircraft control law design.

The invention discloses an aerodynamic characteristic correction method for a wind turbine airfoil wind tunnel test in the field of wind turbine airfoil aerodynamic characteristic optimization. The method comprises the following steps: 1) constructing a wind turbine airfoil aerodynamic test platform for the wind tunnel test; 2) carrying out safety check on equipment and an experimental environment; (3) measuring the surface pressure of the wind turbine airfoil under the expected working condition by using a high-frequency pressure sensor to obtain a measured pressure signal; 4) performing frequency domain analysis on the pressure signal to obtain the power spectrum density of the pressure signal of the wind turbine airfoil under the expected working condition; 5) estimating and identifying vortex shedding frequency; (6) pressure signal pulsation and measurement errors caused by the vortex are distinguished, a Kalman filter is designed to filter the pressure signals, and corrected pressure signals are obtained; 7) estimating a pressure signal of a specific pressure measuring point position; and 8) calculating to obtain the lift coefficient and the resistance coefficient of the corrected wind turbine airfoil. According to the method, the vortex shedding frequency can be accurately identified, the noise can be effectively filtered, and the correction method of the real aerodynamic coefficient can be obtained.

The application provides an interpretable aerodynamic coefficient prediction method and system based on dynamic weighting. The method comprises: obtaining a flight state parameter feature dataset composed of a plurality of sample points and a corresponding aerodynamic coefficient label dataset, performing normalization processing on the feature dataset to obtain standardized sample features; training a group of heterogeneous machine learning models in parallel based on the standardized sample features and the label dataset, and obtaining unbiased prediction values of each model for each sample point through cross-validation; for each sample point, calculating and fusing three types of sample-level evaluation weights based on the unbiased prediction values of each model corresponding to the sample point to generate dynamic fusion weights of each model for the sample point; for each sample point, performing weighted summation on the unbiased prediction values of each model for the sample point by using the dynamic fusion weights corresponding to the sample point to obtain a final fusion prediction value of the sample point; and summarizing the final fusion prediction values of all sample points to output an aerodynamic coefficient prediction result and a corresponding interpretability analysis report.

The present application relates to a kind of ground effect wing ship longitudinal coefficient estimation method and its aerodynamic characteristic analysis system, belong to the technical field of ground effect wing ship. It covers preliminary design ground effect wing ship scheme, design and manufacture wind tunnel model, obtain data by 3 times variable flight height test, and utilize these data to deduce the mathematical expression of ground effect wing ship longitudinal aerodynamic coefficient, estimate the longitudinal aerodynamic characteristic in any flight height state by the mathematical expression of ground effect wing ship longitudinal aerodynamic coefficient, to simplify test procedure, reduce test number. The present application significantly reduces the workload of model blowing test by combining theoretical analysis with a small amount of test data, shortens the research and development cycle, reduces the consumption of funds. At the same time, the method can accurately obtain the key partial derivative and relative ground effect height in the stability calculation of ground effect wing ship, improve the design efficiency and accuracy, and has important promoting effect on the development and application of ground effect wing ship technology.

The invention belongs to the technical field of unmanned aerial vehicle design, and particularly relates to an open-loop state large-angle-of-attack range longitudinal aerodynamic coefficient identification calculation method, which comprises the following steps of: comparing simulation data with test data in an actual flight test, calculating an error sum of squares of the simulation data and the test data, and minimizing the error sum of squares; dividing the whole attack angle range into a plurality of sub-intervals, respectively obtaining the optimal aerodynamic parameters of each sub-interval based on the minimum sum of squares of errors, inputting each optimal aerodynamic parameter into the flight simulation model, comparing the optimal aerodynamic parameters with test data for verification, and executing the next step when the difference value between the optimal aerodynamic parameters and the test data is within a set range; and drawing an aerodynamic characteristic curve according to each optimal aerodynamic parameter, verifying the applicability of the flight simulation model, and optimizing the robustness of the flight simulation model. The method is suitable for longitudinal aerodynamic coefficient parameter identification of different types of unmanned aerial vehicles, the parameter identification efficiency is improved, and meanwhile the applicability of the model under the complex flight working condition is enhanced.

This invention provides a method for predicting the power of a vertical axis wind turbine based on multidimensional environmental data. It relates to the field of vertical axis wind turbine power technology using multidimensional environmental data. The invention utilizes a sensor network to collect multidimensional data in real time, including environmental parameters such as wind speed, turbulence intensity, temperature, and humidity; operating states such as rotor speed and blade pitch angle; and structural responses such as blade strain and vibration acceleration. Next, empirical mode decomposition is performed on the vibration acceleration signal to construct a physical information neural network. Using environmental data and the frequency domain energy spectrum of aerodynamic loads as input, the real-time wind energy utilization coefficient and aerodynamic coefficients are output. Finally, based on the time domain signal of the aerodynamic loads, online rainflow counting with dynamic window length is used to extract the load cycle spectrum. Combined with a humidity-corrected fatigue damage model, the cumulative damage value is calculated. When the remaining lifetime is below a threshold, the load peak suppression and power generation efficiency are balanced through the coordinated control of linear decay of magnetoresistive torque and fuzzy PID control of the flaps.

The application discloses a flight simulation model and a modeling method for a variable stability flight simulator, and belongs to the technical field of flight simulation. The flight simulation model comprises an interface control module, a variable aerodynamic simulation module, a variable flight control simulation module, a physical property simulation module and a six-degree-of-freedom motion simulation module. The interface control module defines external data to be received. The variable aerodynamic simulation module establishes an aerodynamic derivative interpolation table and an aerodynamic coefficient expression according to a nonlinear aerodynamic data package, and superimposes the influence of externally configured aerodynamic parameters on the aerodynamic coefficients as an increment by reconstructing the aerodynamic coefficient expression. The physical property simulation module outputs the weight and inertia of an airplane. The six-degree-of-freedom motion simulation module calculates the motion state of the airplane. The variable flight control simulation module constructs flight control laws under different instruction types, and switches corresponding control laws and control gains in real time according to external configuration. The flight simulation model realizes double adjustment of aerodynamic parameters and flight control laws in the same flight simulation model.

The application relates to the technical field of engineering machinery, and discloses a method for determining wind load of a tower crane, a processor and a storage medium, a component model corresponding to a component to be measured of the tower crane is subjected to a wind tunnel test, so as to determine a wind tunnel aerodynamic coefficient of the component model, a CFD simulation model is established based on the component model, so as to realize more accurate calculation of the wind load of a complex large machine through the CFD simulation model, and the strain and application range of the wind load calculation are improved, and after the CFD aerodynamic coefficient of the component model is determined according to the CFD simulation model, the determination of a CFD simulation calculation correction coefficient is carried out according to the wind tunnel aerodynamic coefficient and the CFD aerodynamic coefficient, the aerodynamic coefficient obtained by the CFD simulation calculation with larger error is corrected by using the wind tunnel test closer to the actual situation, so that when the wind load of the tower crane is determined according to the CFD simulation calculation correction coefficient and the CFD aerodynamic coefficient, the CFD simulation calculation has higher accuracy.

The invention provides a wing automatic generation and performance evaluation method based on a structured point cloud and a variational auto-encoder, and belongs to the field of aircraft design. Constructing an initial wing curved surface through parametric modeling and Latin hypercube sampling according to the shape of a certain reference wing, and representing the initial wing curved surface as a structured three-dimensional point cloud data set; and then, constructing a generative model based on a variational auto-encoder, carrying out feature learning and reconstruction by using the structured point cloud data set, and reconstructing the wing curved surface. Thirdly, carrying out batch grid division on all wing curved surfaces, and carrying out numerical simulation by adopting a CFD (Computational Fluid Dynamics) method to obtain an aerodynamic performance data set; based on this, establishing an aerodynamic performance proxy model from the potential variable to the aerodynamic coefficient; and finally, performing performance driving design in the potential space by using a gradient optimization algorithm to obtain a wing profile with a high lift-drag ratio. According to the method, the automation level and engineering diversity of wing optimization design are remarkably improved.

The invention belongs to the technical field of deep learning, and discloses a CNN-XGBoost fusion airfoil aerodynamic coefficient prediction method based on a self-attention mechanism. The prediction method comprises the following steps: establishing an airfoil profile and an aerodynamic coefficient database of the airfoil profile; generating an airfoil geometric image; generating an airfoil grayscale image; establishing a prediction model; training and storing a prediction model; and airfoil aerodynamic coefficient prediction is carried out. According to the prediction method, an attention mechanism is introduced into a CNN and XGBoost is used for replacing an output layer in a CNN structure, so that the structure of a CNN airfoil aerodynamic coefficient prediction model is improved, the network performance is improved, airfoil image key feature learning is effectively carried out, and high-precision prediction of the airfoil aerodynamic coefficient under small sample data is realized.

The invention provides a prediction method suitable for an aerodynamic coefficient of a power transmission conductor, and belongs to the technical field of power transmission line monitoring. By constructing a sample data set, establishing an aerodynamic coefficient low-fidelity agent model, establishing an aerodynamic coefficient prediction model, testing the aerodynamic coefficient prediction model, predicting the aerodynamic coefficient by using the aerodynamic coefficient prediction model and applying the aerodynamic coefficient prediction model to galloping calculation, the efficiency of predicting the aerodynamic coefficient of the transmission conductor is greatly improved, and the prediction is accurate; the method greatly facilitates the use and design of engineers, can further provide technical means for power transmission line galloping early warning and prediction, and has good practicability.

The invention relates to a method, a device and equipment for generating an anti-galloping strategy of an icing contact network, and a medium. The generation method comprises the following steps: acquiring contact network information, and constructing a first hydrodynamic simulation model of the contact network under an icing condition according to the contact network information and preset icing information; according to preset wind power information, carrying out aerodynamic characteristic analysis on the first fluid mechanics simulation model to obtain a first aerodynamic coefficient; according to the preset galloping information, the preset wind power information and the first aerodynamic coefficient, carrying out galloping instability analysis on the icing contact network to obtain a first galloping coefficient; constructing a second fluid mechanics simulation model according to the configuration information of the anti-galloping device, wherein the anti-galloping device is integrated on the contact network; performing aerodynamic and galloping analysis on the second fluid mechanics simulation model according to preset wind power information and preset galloping information to obtain a second aerodynamic coefficient and a second galloping coefficient; and according to the aerodynamic coefficient and the galloping coefficient, generating an anti-galloping strategy capable of efficiently inhibiting vertical galloping of the overhead line system.

This invention provides a method and related apparatus for assessing leading-edge erosion of wind turbine blades, belonging to the technical field of wind turbine blade reliability assessment. The method includes: acquiring meteorological data of the wind turbine to be assessed; discretizing the wind turbine blade along its spanwise direction into multiple blade element micro-segments; calculating a bidirectional coupling correction factor for each rainfall event recorded at each blade element micro-segment based on the instantaneous surface morphology of the wind turbine blade; calculating the Miner total cumulative damage variable based on this correction factor, and then calculating the aerodynamic coefficient after damage; calculating the aerodynamic torque based on the aerodynamic coefficient, and then calculating the equivalent annual power generation loss rate, ultimately determining the degree of erosion attenuation of the wind turbine. This invention quantifies the bidirectional coupling mechanism of rain and sand by introducing a liquid resistance-solid buffer attenuation factor and a solid-promoting liquid-water hammer amplification factor, and achieves phased determination of the latency period and mass loss period based on Miner linear cumulative damage, enabling efficient and accurate assessment of the time-varying impact of blade leading-edge erosion on annual power generation.

This invention belongs to the field of unmanned aerial vehicle (UAV) control technology, specifically relating to a method and system for attitude stabilization control of UAVs under special environmental disturbances. The method includes the following steps: S1: Establishing a wind field disturbance model, a dust particle impact model, and an electromagnetic interference model, and dynamically coupling these three models into a total disturbance vector experienced by the UAV; S2: Constructing a six-degree-of-freedom dynamic model incorporating the influence of the total disturbance vector. This model describes the translational and rotational motion of the UAV's center of mass under multi-source disturbances in complex environments; S3: Based on traditional aerodynamic models and incorporating environmental disturbance parameters, constructing a variable structure model with aerodynamic coefficients. This invention effectively improves the UAV's adaptability to multi-source coupled disturbances by constructing a unified multi-layered architecture for environmental identification, disturbance observation, Lyapunov robust control, and actuator compensation, thereby ensuring mission accuracy and safety.

This invention discloses a method, device, and system for predicting automotive aerodynamic flow fields, relating to the field of automotive aerodynamics technology. The method includes: acquiring a vehicle geometric model; extracting global macroscopic point clouds and local high-curvature microscopic point clouds; generating a physical bias mask based on the signed distance function of query points and surface curvature; inputting the dual-scale point cloud features into a pre-trained dual-scale transformer model, which includes a macroscopic encoder, a microscopic encoder, and a cross-scale attention layer, and superimposing the physical bias mask during attention calculation to inject physical prior constraints; and outputting the velocity field, pressure field, and aerodynamic coefficients via a decoder. This invention requires only geometric data to quickly output high-precision flow field prediction results that conform to physical laws.

This invention relates to the field of civil aircraft flight safety technology, and in particular to a method for predicting the flight trajectory of a high-speed target under conditions of visor perforation. The method includes: acquiring the calculated coefficient parameter data of the target's flight dynamics model; establishing a trajectory prediction framework based on a three-degree-of-freedom target flight dynamics model and real-time target flight data; numerically simulating the flow field in the visor perforation area using fluid dynamics analysis software to acquire velocity and pressure field data; establishing an aerodynamic analysis model of the visor perforation flow field using a high-order polynomial fitting method; calculating the Mach number and pressure distribution after visor perforation; dynamically updating the aerodynamic coefficient data and aerodynamic parameter database; and introducing the velocity and pressure distribution data into the three-degree-of-freedom target flight dynamics model for registration and updating to obtain the predicted trajectory. The prediction accuracy is improved by 30%–50%, especially under high angle-of-attack conditions, reducing the prediction error from over 20% to within 5%.

The application discloses a heliostat aerodynamic coefficient calculation method and system fusing geometric modal characteristics, and belongs to the technical field of heliostat simulation. The existing heliostat aerodynamic coefficient prediction method does not consider the mutual influence and interaction between multi-modal variables, resulting in a large deviation between the aerodynamic coefficient prediction result and the actual situation. The heliostat aerodynamic coefficient calculation method fusing geometric modal characteristics fully considers the mutual influence and interaction between the multi-modal variables of the heliostat, mines the interaction relationship between the modal characteristics of the heliostat, introduces a graph convolution operation, represents the modal coupling data as a graph structure, represents the dependency relationship between the modal characteristics, and obtains accurate heliostat aerodynamic coefficients, so that the multi-physical field interaction of the heliostat can be effectively modeled, the aerodynamic characteristics of the heliostat in a complex environment can be accurately reflected, and the heliostats of different design schemes can be quickly evaluated and optimized.

The invention discloses a floating air ball icing flight performance high-fidelity prediction method and system based on multi-physical field dynamic strong coupling iteration. According to the method, flexible structure deformation, computational fluid dynamics, microscopic icing physics and aircraft macrodynamics are subjected to full-closed-loop dynamic coupling solution for the first time. According to the method, a simulation framework comprising four solvers including a structural deformation and mass updating module, a computational fluid dynamics and heat transfer module, a water drop impact and ice accumulation simulation module and a balloon flight mechanics and helium thermodynamics module and a dynamic coupling controller is constructed, and multiple rounds of data exchange and iteration are performed in each time step until a coupling system converges. Wherein ice load induced structural deformation and pneumatic grid real-time updating, pneumatic coefficient time-varying feedback and phase change latent heat bidirectional coupling are three major technical breakthroughs. According to the method, the problem of prediction distortion caused by the fact that a key feedback loop is ignored in traditional sequential simulation is thoroughly solved, complex phenomena such as height locking, oscillation lowering and asymmetric instability in the icing environment can be accurately predicted, and an unprecedented high-fidelity numerical tool is provided for safety design, task planning and real-time risk assessment of the floating air ball.

This invention relates to the field of computer simulation technology, and discloses a rapid modeling and simulation method and system for deformable wing structures. The method includes: identifying key points of the wing section data; fitting the upper and lower surface curves of the deformable wing structure to determine the parameterized wing section; analyzing key variation parameters of the deformable wing structure along the wingspan to construct parameter variation curves along the wingspan, and fitting the wing geometric model of the deformable wing structure; determining the deformation mode of the deformable wing structure; calculating the airfoil and torsion angle changes during deformation; fitting an initial deformable wing model; simulating and calculating the aerodynamic coefficients of the initial deformable wing model to determine its aerodynamic performance; and optimizing the initial deformable wing model to obtain the target deformable wing model. This invention can improve the accuracy and design efficiency of deformable wing structure modeling and simulation.

The invention provides a free rolling wind tunnel test data analysis method and system considering bearing friction. The method comprises the following steps: constructing a rolling motion control equation considering bearing friction torque; calibrating a friction torque item in the control equation; and identifying the wind tunnel test data according to the friction torque item to obtain an aerodynamic coefficient. According to the small rolling aerodynamic force wind tunnel test data analysis method based on the mechanical bearing free rolling technology, the friction torque influence generated by the mechanical bearing or a mechanical bearing combination and the change generated after the friction torque is subjected to the aerodynamic load can be fully considered, and therefore the aircraft rolling aerodynamic coefficient can be identified more accurately. The method can be popularized and applied, other bearings or bearing systems are used in a test system, and when other types of friction force or magnetic force actions need to be considered, addition can be carried out during control equation modeling; meanwhile, corresponding ground simulation and measurement methods can be designed according to the characteristics of other acting forces considered and added.

The invention provides a self-adaptive control method for a high-rotation flight high-frequency-response turbulent flow actuating mechanism, which comprises the following steps of: firstly, quantitatively representing a control effect of the turbulent flow actuating mechanism through an additional aerodynamic coefficient, and constructing an aerodynamic model of the turbulent flow actuating mechanism and an aerodynamic coefficient database of the aerodynamic model; secondly, performing pneumatic calculation and quantitative calculation through the motion state parameters of the flying body in the flying process of the flying body to obtain the control effect of the turbulent flow actuating mechanism; and finally, on the basis of a self-adaptive control algorithm of the turbulent flow actuating mechanism, according to the required overload, the flight body motion state parameters and the control effect of the turbulent flow actuating mechanism, the self-adaptive actuating angle range of the turbulent flow actuating mechanism is calculated, the turbulent flow actuating mechanism is controlled to actuate accordingly, and continuous control of the high-rotation flight body meeting the required overload requirement is achieved.

The invention belongs to the technical field of unmanned aerial vehicle design, and particularly relates to a longitudinal aerodynamic parameter identification calculation method under a closed-loop state and a small attack angle range, and the method comprises the steps: simulating the flight state change process of an unmanned aerial vehicle under the condition of the closed-loop state and the small attack angle range based on a flight simulation model; dynamic characteristics of the aircraft under different longitudinal pneumatic conditions are obtained; performing parameter identification calculation on the dynamic characteristics of the aircraft under different longitudinal pneumatic conditions to obtain flight simulation data; iterative optimization is performed on the longitudinal aerodynamic model parameter combination set through a genetic algorithm, and an optimal longitudinal aerodynamic model parameter combination is obtained by combining the sum of squares of errors between flight simulation data and actual flight test data; and simulation verification and error analysis are carried out on the optimal longitudinal aerodynamic model parameters, and verification is completed when a preset precision requirement is met. The influence of the control law on the flight test in the flight process is considered, and the identification of the longitudinal aerodynamic coefficient parameters in the corresponding closed-loop state is more accurate.

The invention provides an aerodynamic coefficient prediction method, system and device for a supersonic lifting body aircraft and a medium, and the method comprises the steps: obtaining appearance characteristic parameters describing the appearance of a lifting body, and constructing an aerodynamic prediction model of a depth operator network based on the preprocessing of the appearance characteristic parameters; selecting a plurality of flight working condition points with different flight parameters in the flight envelope of the lifting body supersonic aircraft, and performing CFD calculation to obtain corresponding aerodynamic coefficients; forming a data set by taking the appearance characteristic parameter of one lifting body and the aerodynamic coefficient under a single flight condition as a group of data points, and dividing the data set into a training set and a test set; using the training set to train the pneumatic prediction model, and using the test set to test the pneumatic prediction model; and inputting the appearance characteristic parameters and the flight working condition points into the trained pneumatic prediction model to obtain a corresponding pneumatic coefficient prediction value so as to solve the problem that the pneumatic proxy model is insufficient in pneumatic appearance characteristic extraction capability during prediction and construction.

The present application belongs to the field of aircraft control technology, and provides an aircraft adaptive thrust vectoring control method based on attitude measurement, which comprises the following steps: S1, measuring the pitch angle rate, pitch angle and speed pitch angle of the aircraft; S2, solving the pitch angle error signal and the pitch angle error integral signal; S3, solving the error related term adaptive signal; S4, solving the angle rate related term adaptive signal; S5, realizing the vectoring control according to the aircraft thrust vectoring pitch total control signal. The present application constructs an equivalent control term model through aerodynamic coefficients, combines each total signal to form a thrust vectoring total control signal, realizes the tracking and regulation of the expected pitch angle of the aircraft, adjusts the control gain parameters through real-time compensation of the speed pitch angle and the pitch angle flight control parameters, makes the aircraft adapt to the changes of the environment and aerodynamic parameters, so that the aircraft control system can dynamically compensate the interference factors in real time, and the control precision and stability of the aircraft are improved.

A path planning method based on analytical gradients of a neural network includes: acquiring sample datasets of a rocket under multiple typical flight conditions; training the neural network with flight control variables as input and aerodynamic coefficients as output until convergence is met, resulting in a surrogate model for fitting the rocket's aerodynamic characteristics; collecting the rocket's current state vector and control vector, inputting the control vector into the surrogate model to obtain the current aerodynamic coefficients, and constructing the rocket's flight state equation; based on the flight state equation, using an automatic differentiation method, analytically calculating the gradient with respect to the control vector to form a gradient sensitivity matrix; using the gradient sensitivity matrix to determine the descent direction; and iteratively updating the control vector using a numerical optimization algorithm until the termination condition is met, outputting the optimal flight path. The optimal flight path obtained by this method more closely matches the rocket's actual aerodynamic environment, exhibiting higher terminal accuracy.

This invention discloses a transfer learning method for aerodynamic modeling based on symbolic regression, belonging to the field of aerodynamic modeling technology in aerodynamics. The method includes: collecting aerodynamic data of a source domain aircraft under various flow conditions, including flow state variables and aerodynamic coefficients; in the pre-training stage, constructing a correlation function expression for the aerodynamic coefficients of the source domain aircraft based on the flow state variables, combined with symbolic regression and shape parameter optimization methods; collecting sparse sample aerodynamic data of a target domain aircraft; in the fine-tuning stage, fine-tuning the correlation function parameters and performing aerodynamic modeling based on the sparse samples of the target domain, evaluating the optimal parameters of the correlation function, and using the optimal correlation function to perform polynomial fitting on the sparse sample aerodynamic data, thereby achieving transfer learning for aerodynamic modeling based on symbolic regression. This invention solves the problems of large aerodynamic data requirements and unreliable aerodynamic model extrapolation in existing aerodynamic database construction methods.