86 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 belongs to the technical field of aircraft simulation, and relates to a flight simulation method and device under gust load considering a downwash effect. The method comprises the following steps: S1, carrying out 1g level flight balancing on the aircraft, and determining a balancing attack angle of the aircraft and a balancing elevator skewness of the aircraft; s2, superposing the aerodynamic coefficient increment brought by the downwash angle of attack to the aerodynamic coefficient of the aircraft body, and determining the aerodynamic force of the aircraft; s3, dynamic response solving is conducted on aerodynamic force of the aircraft, the flight speed is obtained, the corrected speed of the aircraft is determined after the flight speed is superposed with the wind speed of the vertical gust field, and the aerodynamic attack angle and the flight Mach number are determined according to the corrected speed; and S4, re-determining the aerodynamic force of the aircraft according to the flight Mach number, the pitch angle speed, the aerodynamic angle of attack, the given wind speed of the vertical gust field and the elevator skewness, and circulating the steps until the simulation is finished. The method is simple in simulation process and high in precision, and meets the requirements of engineering practice.

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.

The invention belongs to the technical field of aircraft simulation, and particularly relates to an aircraft maneuvering load simulation method and device considering a downwash effect. The method comprises the following steps: S1, carrying out 1g level flight balancing on the aircraft, and determining a balancing attack angle of the aircraft and a balancing elevator skewness of the aircraft; s2, superposing the aerodynamic coefficient increment brought by the downwash angle of attack to the aerodynamic coefficient of the aircraft body, and determining the aerodynamic force of the aircraft; s3, dynamic response solving is carried out on aerodynamic force of the aircraft, and the aerodynamic attack angle, the flight speed and the pitch angle speed are obtained; s4, calculating elevator skewness at the current moment according to elevator instruction input; and S5, re-determining the aerodynamic force of the aircraft according to the flight speed, the pitch angle speed, the aerodynamic angle of attack and the elevator skewness until the simulation is finished. According to the method, the effect of the downwash influence in the aircraft maneuvering load determination process is reasonably considered, the simulation process is simple, the precision is high, and the engineering practice requirement is met.

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 discloses an aeroelastic model correction method based on frequency domain response data, and belongs to the field of aeroelastic design of aircrafts. According to the method, an aeroelastic model of a to-be-studied aircraft is constructed, a ground flight control structure coupling test is firstly carried out, correction coefficients of generalized mass, generalized damping, a generalized stiffness matrix and a control surface coupling inertia mass matrix are calculated by using frequency response data of the ground test through an optimization method, and then a flight aerodynamic servo stability test is carried out. And calculating correction coefficients of the unsteady aerodynamic coefficient matrix of the body mode and the unsteady aerodynamic coefficient matrix of the control plane deflection mode based on the corrected structural parameters, and finally realizing correction of the aeroelastic model. According to the method, the time and labor consumption of traditional manual correction is avoided, multi-variable correction can be carried out while multi-test response is considered, the problem that pneumatic correction in traditional correction is relatively difficult is solved, pneumatic correction is considered while the aircraft structure is corrected, and an accurate aeroelastic model can be obtained.

The present invention provides an online estimation method, device and medium for aircraft aerodynamic parameters based on thrust deviation correction, and relates to the technical field of online identification of aircraft parameters. The method comprises: using an extended Kalman filter to iteratively predict the augmented state of the aircraft system step by step; using a forgotten recursive least squares estimator to estimate the axial aerodynamic force and normal aerodynamic force coefficients and the thrust deviation proportional coefficient within a given time window, and then calculating the aerodynamic lift, drag coefficients and the corrected thrust estimation results; judging the convergence of the thrust deviation proportional coefficient estimation results; if it has not converged, aerodynamic derivative estimation is carried out based on the uncorrected thrust and lift and drag aerodynamic coefficients; if it has converged, the corrected thrust and the corrected lift and drag aerodynamic coefficients are brought into the general recursive least squares estimator to carry out aerodynamic derivative estimation. The online estimation method proposed by the present invention can effectively improve the accuracy of longitudinal aerodynamic derivative estimation under thrust measurement deviation.

The invention discloses a flight state estimation method and system for a low-cost small propeller aircraft, and belongs to the technical field of navigation, guidance and control, and the method mainly comprises the steps: building parameterized models of aerodynamic coefficients and propeller thrust coefficients, the difference between the air speed and the ground speed and the derivative deviation of the normal force coefficient to the attack angle are used as system state quantities to be estimated, and the axial apparent acceleration measured by an accelerometer is used as a quantity for measurement; calculating a theoretical value of the quantity measurement by using a kinetic model; and estimating the flight state of the aircraft based on extended Kalman filtering recursion. According to the method, an existing navigation system is utilized, and a simple, practical and effective flight state estimation means is provided for the propeller propulsion aircraft on the premise that no hardware needs to be added.

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 provides a device and a method for measuring a near-wall aerodynamic coefficient of particles. The device comprises an airflow pipeline, a particle injection assembly and an electric field generation assembly. Wherein clean airflow with a stable flow direction is arranged in the airflow pipeline, the particle injection assembly is used for injecting charged particles into the airflow pipeline, the electric field generation assembly is used for forming a flow direction electric field and a vertical electric field in the airflow pipeline, and the flow direction electric field is used for providing electric field force opposite to the flow field for the charged particles in the airflow pipeline; a vertical electric field is used for providing electric field force in the direction opposite to the gravity for the charged particles in the airflow pipeline. By controlling the electric field intensity of the flow direction electric field and the vertical electric field, the charged particles can be kept static in the mainstream area and the near wall surface area respectively, and the aerodynamic coefficient of the charged particles in the near wall surface area is calculated based on the size of the charged particles and the position of the charged particles in the near wall surface area. According to the invention, the stress characteristics and stress rules of charged particles at different wall surface positions can be measured.

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 invention provides a railway bridge tower area wind barrier optimization method and system based on deep learning, and relates to the technical field of railway barriers, and the method comprises the steps: obtaining the structure parameters of a target main beam, a bridge tower and a train, and building a train-bridge three-dimensional model of a tower area according to the structure parameters of a target bridge tower-main beam intersection area; processing the train-bridge model by adopting a self-adaptive grid technology to obtain an overlapped grid model for simulating train movement; defining initial variables and boundary conditions of the model in computational fluid dynamics software, and performing iterative computation through an N-S equation and a turbulence equation to obtain an aerodynamic coefficient time history of the train; based on the CFD calculation result, processing the aerodynamic coefficient time history to obtain a data set for neural network training; and training and testing the data set to complete optimization of the wind barrier in the bridge tower area. The deep reinforcement learning technology is adopted, the wind load sudden change effect of the tower area is effectively controlled, and the calculation time and the calculation cost are reduced.

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 present invention provides a data augmentation and model training method, system and medium for predicting aerodynamic coefficients of three-dimensional aircraft, relates to the field of rapid prediction of aerodynamic coefficients of aircraft, and solves the scarcity and high cost problems of high-quality CFD data in intelligent CFD; the method comprises: defining a basic aerodynamic data set including shape, working condition and aerodynamic coefficient, and forming an aerodynamic database with sampled point cloud data, working condition and aerodynamic coefficient; randomly sampling point cloud data to obtain augmented point cloud data, and forming an augmented aerodynamic database on the basis of the aerodynamic database; constructing an aerodynamic coefficient prediction neural network, and after pre-training, fine-tuning the neural network using the augmented aerodynamic database to obtain an intelligent model for predicting aerodynamic coefficients of three-dimensional aircraft; the present invention is a low-cost aerodynamic data augmentation solution, which utilizes general three-dimensional data, reduces the model's demand for aerodynamic data, and improves the performance of the intelligent model.

The invention provides a dual-pulse guided missile initial guidance method based on a pseudo-spectral method and a neural network. The dual-pulse guided missile initial guidance method comprises the following steps that 1, a trajectory optimization model is established; only a preliminary guidance method of a missile in a vertical plane is considered; step 2, solving an optimal trajectory by using a pseudo-spectral method; giving a corresponding state quantity, a control quantity, an initial moment constraint, a terminal moment constraint and a process constraint; step 3, fitting a control instruction by using a neural network; a BP neural network is used for fitting; 4, fitting a trajectory inclination angle by using a neural network; step 5, obtaining a suboptimal double-pulse initial guidance law; and obtaining a fitted attack angle control rule and an expected flight trajectory inclination angle, obtaining an expected instruction and an expected trajectory inclination angle by using a neural network according to the current flight state, and performing instruction control. According to the method, the guided missile can fly according to the optimal trajectory, and the energy loss in the flight process is effectively reduced. By introducing a ballistic inclination angle feedback item, the method still can perform well when the initial launching inclination angle and the aerodynamic coefficient have deviations. The disadvantage that all the performance of the guided missile is difficult to give play to through traditional program guidance is solved.

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.