A new compilation method of general standard test load spectrum for aircraft engine

Abstract

Based on the existing compilation technology of load spectrum, this paper proposed a new method for the compilation of the general standard test load spectrum of aircraft engine. On the premise of meeting operating requirements of the actual condition and the basic life test, it simplifies the test load conditions and overcomes the shortage of the existing technology. According to the actual flight of aircraft engine, the load spectrum of a flight mission profile was divided into three parts: the take-off phase, the middle-flight phase, and the landing phase. The proposed compilation method ensures that the generated load spectrum of the take-off phase and landing phase is consistent with the actual flight, and the load spectrum of the middle-flight phase is programmed according to a certain method so that a simple load cycle sequence can be implemented. On that basis, the load spectrum was analyzed and compiled by subsection. The general standard test load spectrum lays the foundation for carrying out the life test and reduces the error between the test and practical use. Since the parameters of the engine load are standardized in the process of compiling, the method can be widely used in various types of engines.

Compilation Method of Flight Service Environment Spectrum Based on Altitude Features and Fuzzy Clustering

“…Pressure altitude, as an important indicator of aircraft maneuverability, is currently always used to compile the service environment spectrum of all engine components [2][3][4] because all combat or airdrop actions, such as air-air, medium-air-ground, and airground states, involve either fast or slow ascent and descent processes.…”

Research on Engine Thrust and Load Factor Prediction by Novel Flight Maneuver Recognition Based on Flight Test Data

“…To utilize the considerable data to identify unusual flight behaviors, assess aircraft loads and risks, improve training methods, and enhance the safety of flight operations, Flight Maneuver Recognition (FMR)-a standard established by the Federal Aviation Administration (FAA) [1]-has been widely employed. The amount of FMR data is increasing for the flight action assessment [2], risk assessment [3], research on aircraft load spectra [4], and engine load spectra [5,6]. Nonetheless, the substantial volume of flight data presents the challenges in conducting FMR accurately and efficiently [7,8].…”

“…(4) Training machine learning models utilizing classification, clustering, and DTW techniques, incorporating novel approaches proposed in this paper. (5) Prediction and analysis of maneuvers. (6) Compute the engine load factor for the given flight mission.…”