A flight test and ground simulation aerodynamic force data comprehensive modeling method

Abstract

The invention discloses a flight test and ground simulation aerodynamic force data comprehensive modeling method, which is used for establishing a full-airspace and full-speed-domain aerodynamic forcemathematical model of an aircraft according to more than 10 flight test data samples and aerodynamic force ground simulation data in a flight envelope range of the aircraft. The method comprises thefollowing steps: firstly, performing aerodynamic force calculation ground simulation within a flight envelope range of an aircraft, obtaining aerodynamic force simulation data in a full airspace and afull speed domain, and converting the aerodynamic force simulation data into an aerodynamic force simulation sample; And performing a flight test, obtaining more than 10 times of flight test state measurement data, and obtaining an aerodynamic force identification data sample of the flight test according to an aerodynamic force identification method. And finally, taking the aerodynamic force identification data sample and the aerodynamic force simulation sample together as a total sample, and establishing an aircraft full-airspace and full-speed-domain aerodynamic mathematical model integrating flight test and ground simulation data through machine learning by utilizing an artificial intelligence-based BP neural network method.

Description

technical field [0001] The invention belongs to the field of aircraft system modeling, and relates to a method for mathematical modeling of aircraft flight test and ground simulation aerodynamic data. Background technique [0002] For ground simulation data of an aircraft, this is usually a data table. The aerodynamic data of the flight test of the aircraft are usually discrete data points along the ballistic trajectory. How to use the flight test data to correct the ground simulation data is a problem of widespread concern, and it is feasible to establish a comprehensive aerodynamic model that includes both the flight test data and the ground simulation data. For the ground simulation data of the whole airspace and full speed domain, as well as the flight test data of more than 10 times, the airspace, speed domain, and state range such as altitude, speed, angle of attack, sideslip angle, and rudder angle are relatively large, and the aerodynamic force and altitude, The re...

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Problems solved by technology

[0004] The technical problem to be solved by the present invention is: for the ground simulation data of the whole airspace and full speed domain, and the flight test data of more than 10 times, the airspace, speed domain, and state ranges such as altitude, speed, angle of attack, sideslip angle, and rudder deflection angle are relatively large, and the relationship between aerodynamic force and altitude, speed, angle of attack, sideslip angle, rudder angle and other parameters presents a highly nonlinear situation. Using the BP neural network method based on artificial intelligence, a comprehensive flight test and Aircraft full airspace and full speed domain aerodynamic mathematical model based on ground simulation data

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