10 results about "Flight test data" patented technology

The present application belongs to the technical field of hypersonic wind tunnel test, and discloses a test checking method of hypersonic frictional resistance standard value. The test checking method of hypersonic frictional resistance standard value is carried out in a hypersonic quiet wind tunnel, and the model is stably kept in a laminar flow state by means of a quiet flow field provided by the hypersonic quiet wind tunnel, so that the interference of flow field noise on the hypersonic frictional resistance is eliminated, real near space flight test data is obtained, and the obtained hypersonic frictional resistance standard value can be used for related test calculation or theoretical analysis, thereby providing technical support for the research on the frictional resistance of a spacecraft.

This invention provides an online monitoring method and system for airborne flight runaway precursors of civil aircraft based on flight test data. The method includes: constructing a set of input flight parameters for identifying airborne flight runaway precursors; acquiring daily operational data and flight test data of the target aircraft model; and extracting physical feature information of airborne flight runaway by referencing the aerodynamic mechanism model and extreme flight envelope boundary of the target aircraft model. Based on the domain adaptation concept, an offline precursor recognition model is constructed that integrates physical feature information embedding, meta-learning, and multi-instance learning. Based on knowledge distillation technology, the recognition capability of the offline precursor recognition model is transferred to a lightweight network constructed from gated recurrent units to generate an online precursor monitoring model, enabling real-time precursor probability calculation and early warning. Finally, an intelligent agent model based on a dual-delay deep deterministic policy gradient algorithm is constructed to verify the effectiveness of the online precursor warning. This invention overcomes the cross-domain data gap and meets the requirements of online lightweight computation.

The application relates to an airplane static pressure source error correction method based on a Bayesian regularization neural network, comprising the following steps: constructing a mixed training data set, wherein the mixed training data set comprises flight test data, CFD simulation data and physical boundary data; pre-processing and dividing the mixed training data set to form a training set and a test set; constructing a Bayesian regularization neural network model and training; and using the trained Bayesian regularization neural network model to realize static pressure source error correction. The application uses CFD numerical simulation technology to economically and efficiently generate dense samples covering the whole design envelope, so as to make up for the insufficiency of flight test data in the coverage range and data density of the envelope; meanwhile, a Bayesian regularization training algorithm is introduced, the model complexity and data fitting degree are automatically balanced through Bayesian inference, overfitting is effectively inhibited, and the model generalization ability is improved.

A quantitative flight test data gathering system is configured to, during flight test operation of an aircraft, obtain, from one or more flight data sensors, from a flight control system, or both, one or more feedback signals indicative of a state of the aircraft. The quantitative flight test data gathering system is configured to generate, based at least in part on the feedback signals, one or more signals associated with a flight test maneuver. The quantitative flight test data gathering system is configured to provide the one or more signals as input to the flight control system to cause one or more control laws of the flight control system to generate control signals to cause the aircraft to operate according to the flight test maneuver.

This application provides a method, apparatus, device, storage medium, and program product for measuring fuselage parameters, belonging to the field of aviation technology. The method includes: performing a flight test to obtain target flight test data for a target area of ​​the aircraft, the flight test data including the aircraft's vertical acceleration, fuselage angular acceleration about the pitch axis, longitudinal strain variation, and circumferential strain variation; performing a ground test to obtain target ground test data for the target area, the ground test data including the coefficients of pressure circumferential stress versus pressure, pressure longitudinal stress versus pressure, and bending longitudinal stress versus bending moment; and determining local fuselage physical parameters for the target area based on the target flight test data and the target ground test data, the local fuselage physical parameters including local torque and moment of inertia. This application can reduce the cost of obtaining local fuselage physical parameters.

This invention discloses a method for identifying the cruise drag coefficient of an aircraft based on flight test data. First, a longitudinal motion state space model of the aircraft containing trim information is established, and the trim information is introduced as a parameter to be identified, enabling direct input of the original full flight test data. Then, the model is decoupled into four independent single-output subsystems, and the optimal estimates of the aerodynamic derivative and trim information are obtained in one step through global optimization using the least squares method. Finally, the true trim state point is determined based on the trim information, and the time history of the cruise drag coefficient is calculated using a linear incremental drag coefficient model. This invention has strong anti-interference capabilities, does not rely on initial trim values, reduces the standard deviation of the identified drag coefficient by 14.6% compared to traditional methods, and achieves a model prediction accuracy R² of over 0.99. It can directly utilize polar curve flight test QAR data without additional flight test costs and is suitable for aerodynamic parameter identification in all flight phases of conventional layout civil aircraft.

The application discloses a kind of helicopter airborne rotor blade tip displacement measurement methods based on point light source detection, belong to image data processing field.The method will point light source be fixed in helicopter rotor blade tip area to realize all-weather blade tip mark, and industrial camera is fixed at the bottom of helicopter with the angle of inclination to collect rotor blade tip area image, in second, a kind of small target detection deep learning model is designed in combination with Gauss receptive field and weighted feature fusion, can realize the accurate identification and positioning of point light source in larger field of view.The method mainly includes the following steps:1) visual calibration of industrial camera;2) rotor blade tip area image acquisition;3) point light source detection based on deep learning;4) oblique vision error compensation, and calculate blade tip displacement amount.The application uses deep learning network model and computer vision to realize helicopter airborne rotor blade tip displacement measurement, with the advantages of high precision, real-time, all-weather, can provide flight test data support for new helicopter design.

This invention belongs to the field of flight test data processing technology and discloses a method and system for quality assessment of multi-source flight test data based on physical-temporal constraints. The method includes the following steps: data preprocessing and benchmark calibration; physical-temporal joint verification; multi-source spatiotemporal alignment; scenario adaptive evaluation; and comprehensive quality scoring and feedback optimization. This invention solves the problems of insufficient correlation and inconsistent spatiotemporal benchmarks in existing flight test multi-source data, achieving full-dimensional and high-precision quality assessment of multi-source flight test data.

The invention relates to an aircraft, a method of collecting flight test data, and a computer readable medium. A quantitative flight test data collection system is configured to acquire, during a flight test operation of an aircraft, one or more feedback signals indicative of a state of the aircraft from one or more flight data sensors, from a flight control system, or from both. The quantitative flight test data collection system is configured to generate, based at least in part on the feedback signals, one or more signals associated with a flight test maneuver. The quantitative flight test data collection system is configured to provide the one or more signals as input to the flight control system to cause one or more control laws of the flight control system to generate control signals to cause the aircraft to operate in accordance with the flight test maneuver.

The application relates to a static pressure position error correction design method based on CFD simulation data and flight test data. The method is based on CFD simulation data and flight test data, and Pr or Cp fitting is carried out in sections. Then, based on the fitting result, Pr is calculated, and a static pressure correction table with Mach number and angle of attack as inputs is established. The indicated Mach number is calculated, and a static pressure correction table with the indicated Mach number and the angle of attack as inputs is obtained. By adopting the design method, only a small amount of flight test data samples are needed, flight test working conditions are greatly reduced, and aircraft research and development expenses are reduced; meanwhile, the static pressure correction value obtained is very small in difference from the flight test data, and high precision is maintained.