73 results about "Aerodynamic coefficient" patented technology

The invention provides a vertical axis wind turbine power prediction method based on multi-dimensional environment data, and relates to the technical field of vertical axis wind turbine power based on the multi-dimensional environment data. Multi-dimensional data are collected in real time based on a sensor network, and the multi-dimensional data comprise environment parameters such as wind speed, turbulence intensity and temperature and humidity, and operation states such as rotor rotating speed and pitch angle; secondly, performing empirical mode decomposition on a vibration acceleration signal, constructing a physical information neural network, taking environmental data and an aerodynamic load frequency domain energy spectrum as input, outputting a real-time wind energy utilization coefficient and an aerodynamic coefficient, and finally, determining the aerodynamic load based on an aerodynamic load time domain signal. A load cyclic spectrum is extracted by adopting online rain flow counting of dynamic window length, an accumulated damage value is calculated in combination with a fatigue damage model of humidity correction, and when the residual life is lower than a threshold value, load peak suppression and power generation efficiency balance are realized through cooperative control of magnetic resistance torque linear attenuation and flap fuzzy PID (Proportion Integration Differentiation).

The invention discloses an iced conductor galloping amplitude prediction method and system and a storage medium, and belongs to the technical field of power system safety monitoring and prediction.The method comprises the steps that environmental meteorological data of a power transmission conductor are collected in real time and preprocessed; inputting the preprocessed environmental meteorological data into a pre-constructed conductor dynamic response prediction model under the normal working condition to obtain a galloping amplitude prediction value of the transmission conductor under the normal working condition; inputting the preprocessed environmental meteorological data into a pre-constructed icing thickness prediction model to obtain an icing thickness prediction value; determining a lift coefficient and a resistance coefficient based on the preprocessed wind speed data and the icing thickness prediction value; and calculating an equivalent aerodynamic coefficient according to the lift coefficient and the resistance coefficient, converting the galloping amplitude predicted value of the transmission conductor under the normal working condition in combination with a pre-constructed nonlinear conversion formula, obtaining the galloping amplitude predicted value of the transmission conductor under the current icing condition, and transmitting the galloping amplitude predicted value to a monitoring center in real time.

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 relates to a self-adaptive backstepping fault-tolerant control method for a transportation airfoil surface fault. The method comprises the following steps: establishing a six-degree-of-freedom equation of an airplane; in combination with a six-degree-of-freedom equation, determining a relationship between different types of faults and aerodynamic coefficients according to an airfoil fault mode; the wing surface fault mode comprises wing damage, control surface damage and control surface jamming; designing an angular velocity loop adaptive backstepping control law according to a nonlinear dynamic item about a state variable and a control input matrix about a control variable in a fault mode based on a relationship between different types of faults and aerodynamic coefficients; and performing control distribution on the angular velocity loop by using a weighted pseudo-inverse method, combining the adaptive backstepping control law of the angular velocity loop with the weighted pseudo-inverse method, and performing valuing on a weight matrix of the control distribution of the angular velocity loop according to an airfoil fault mode to obtain an adaptive backstepping fault-tolerant angular velocity control law with control distribution. According to the method, the fault-tolerant control efficiency after the airplane breaks down is optimized, and the fault-tolerant flight capability of the airplane is improved.

The invention relates to the technical field of aircraft control, in particular to an aircraft airspeed attack angle estimation method based on augmented generalized Kalman filtering, which comprises the following steps: acquiring sensor data and correcting acceleration, calculating initial estimation of an airspeed attack angle, constructing an error matrix and scoring, extracting variation trend adjustment parameters, and calculating a state variation coefficient. And multi-source information is fused to output the combined airspeed angle of attack quantity. According to the method, through acceleration component body coordinate system projection and back projection correction, an airspeed initial estimation value is constructed in combination with a height difference value, a physical matching degree is enhanced, a slope score and trend change extraction are performed to establish a dynamic compensation mechanism, and the adaptive capacity to a non-stationary flight state is improved; the introduction of the pitch angle speed, the control surface deflection and the aerodynamic coefficient bias makes the state evolution more structural, a change coefficient is established through ratio calculation, the coupling of state prediction and maneuvering dynamic is enhanced, the weighted fusion of an observation residual error and a predicted value is realized, and the convergence capability of an estimated value to complex aerodynamic interference is improved.

The present invention belongs to the field of wind power generation system control, and discloses a fan torque regulation system based on actual aerodynamic coefficients, which includes a fan blade information collection module, an aerodynamic coefficient calculation module, a power regulation module, and a host computer module. The power regulation module includes a torque adjustment unit and a control unit connected to each other. The fan blade information collection module is connected to the aerodynamic coefficient calculation module, and the host computer module and the aerodynamic coefficient calculation module are respectively connected to the torque adjustment unit. The fan blade information collection module is used to identify the geometric shape data of the fan blades. The aerodynamic coefficient calculation module is used to calculate the corresponding actual aerodynamic coefficients according to the geometric shape data of the fan blades. The host computer module is used to input information on the required change in fan power. The torque adjustment unit calculates the actual torque required by the fan based on the actual aerodynamic coefficients, the fan motor speed information, and the information on the required change in fan power. The control unit adjusts the fan torque based on the real-time torque required by the fan.

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 belongs to the technical field of rotary aircraft wind tunnel tests, and particularly relates to a wind tunnel test data processing method suitable for the Magnus effect of a rotary aircraft, and the method comprises the following steps: 1, preprocessing an original voltage signal; step 2, calculation of aerodynamic load; step 3, carrying out time-frequency analysis processing on the transient signal; 4, averaging the transient value of each component to obtain a time-average aerodynamic coefficient; therefore, all data processing is completed, and the transient and time-average aerodynamic characteristics of the Magnus effect wind tunnel test of the rotating aircraft are finally obtained. According to the method, the transient balance signal is deeply processed and analyzed based on a conventional force measurement wind tunnel test data processing method, so that the transient aerodynamic law and time-average aerodynamic data of the rotating aircraft can be quickly and accurately obtained, and data support is provided for overall design iteration.

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 relates to the technical field of tilt rotor simulation, and particularly discloses a tilt rotor aerodynamic coefficient prediction system and method based on machine learning, and the method employs a parameter embedding coding technology to carry out physical embedding processing and cascade fusion on airfoil shape parameters, attack angles and flow field parameters. Then, a neural network model is introduced to carry out deep joint feature extraction on the airfoil profile shape parameter, the attack angle and the flow field parameter so as to predict an initial lift coefficient, an initial resistance coefficient and an initial boundary layer parameter; and carrying out compressibility correction and symmetry constraint on the initial prediction parameters in combination with a physical model to ensure that the initial prediction parameters conform to the aerodynamic law, and finally calculating the aerodynamic coefficient based on the corrected related parameters of the aerodynamic coefficient. According to the method, the attack angle limitation of a traditional Viterna extrapolation method can be broken through, full-range coverage is achieved, the aerodynamic force prediction precision of the extreme attack angle area is remarkably improved, and meanwhile the calculation efficiency is greatly improved.

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 present invention belongs to the field of flight simulators and simulation technology, and specifically relates to a dual-tail vortex encounter effect simulation method for flight simulators, aiming to solve the problem of being unable to meet the real-time and high-precision simulation requirements of flight training platforms. The present invention calculates the tail vortex index by obtaining relevant parameters of the preceding aircraft operation, divides the wake dissipation stage and establishes a tail vortex strength model, determines the vortex position to calculate the induced velocity, compares the tail vortex strength to determine the relative distance between the front and rear aircraft, and selects different methods to calculate the incremental velocity and angular velocity and integrate them to obtain the aircraft state parameters. The new incremental aerodynamic coefficient can be calculated based on the aircraft state parameters, and the aircraft state obtained based on the new incremental aerodynamic coefficient is then iterated. The calculated aircraft state tends to be accurate after coefficient iteration, which reduces the average running time. The present invention effectively improves the simulation accuracy and real-time performance, and can fully meet the relevant requirements of the flight training platform.

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.

The present invention relates to the field of aircraft control technology, specifically to an aircraft airspeed angle of attack estimation method based on augmented generalized Kalman filtering, comprising the following steps: acquiring sensor data and correcting acceleration, calculating an initial estimate of the airspeed angle of attack, constructing an error matrix and scoring it, extracting a change trend adjustment parameter, calculating a state change coefficient, and fusing multi-source information to output a joint airspeed angle of attack quantity. In the present invention, an initial airspeed estimate is constructed by projecting and back-projecting the acceleration component onto the body coordinate system, combining the height difference to enhance physical matching, establishing a dynamic compensation mechanism based on slope scoring and trend change extraction, improving adaptability to non-stationary flight states, introducing pitch angular velocity, rudder deflection, and aerodynamic coefficient bias to make state evolution more structured, establishing a change coefficient through ratio calculation, strengthening the coupling between state prediction and maneuvering dynamics, weighted fusion of observation residuals and predicted values, and improving the convergence ability of the estimate to complex aerodynamic interference.

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.