Performance Degradation Evaluation of Low Bypass Ratio Turbofan Engine Based on Flight Data

Qin, Haiqin; Zhao, Jie; Ren, Likun; Li, Bianjiang; Li, Zhengguang

doi:10.3390/su14138052

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…The Arnold Engineering Development Center in the United States developed the calculation software ATEST, which can calculate the engine performance under any working conditions from startup to shutdown [11]. Qin et al proposed a performance degradation evaluation method for engines based on flight data, and the method expands the equation system to solve the underdetermined problem caused by the lack of engine sensors based on multiple operating point analyses [12].…”

Section: Introductionmentioning

confidence: 99%

Aero-engine performance evaluation model in the entire flight envelope

Li,

Chen,

Fan

2024

J. Phys.: Conf. Ser.

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An aero-engine performance characteristics mathematical model in the entire flight envelope is proposed based on the stratification analysis method and multi-condition test data. The thrust, fuel flow, low/high-pressure rotor speed, exhaust temperature, and other engine output parameters can be calculated in the entire flight envelope, according to the engine operating characteristic model and input parameters of aircraft flight altitude, velocity, ambient temperature, throttle angle, etc. Based on the evaluation model proposed in this paper, the evaluation data of an aero-engine is consistent with the test data, and the error is less than 5%. The research and model can support flight performance calculation and mission planning and can be applied to flight simulator engine modules.

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Section: Introductionmentioning

confidence: 99%

Aero-engine performance evaluation model in the entire flight envelope

Li,

Chen,

Fan

2024

J. Phys.: Conf. Ser.

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Aircraft Engines Performances Estimation from Multi-Point and Multi-Time Operational Data via Neural Networks

Vu,

Razakarivony,

Thepaut

et al. 2024

2024 IEEE Conference on Artificial Intelligence (CAI)

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A Novel Adaptive Generation Method for Initial Guess Values of Component-Level Aero-Engine Start-Up Models

Zhou

Kai

et al. 2023

Sustainability

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To solve the difficult problem of selecting initial guess values for component-level aero-engine start-up models, a novel method based on the flow-based back-calculation algorithm (FBBCA) is investigated. By exploiting the monotonic feature of low-speed aero-engine component characteristics and the principle of flow balance abided by components in the start-up process, this method traverses all the flows in each component characteristic at a given engine rotor speed. This method also limits the pressure ratios and flow rates of each component, along with the surplus power of the high-pressure rotor. Finally, a set of “fake initial values” for iterative calculation of the aero-engine start-up model can be generated and approximate true initial guess values that meet the accuracy requirement according to the Newton–Raphson iteration method. Extensive simulation verifies the low computational cost and high computational accuracy of this method as a solver for the initial guess values of the aero-engine start-up model.

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Performance Degradation Evaluation of Low Bypass Ratio Turbofan Engine Based on Flight Data

Cited by 3 publications

References 23 publications

Aero-engine performance evaluation model in the entire flight envelope

Aero-engine performance evaluation model in the entire flight envelope

Aircraft Engines Performances Estimation from Multi-Point and Multi-Time Operational Data via Neural Networks

A Novel Adaptive Generation Method for Initial Guess Values of Component-Level Aero-Engine Start-Up Models

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