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Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

机译：使用实验数据在参数不确定条件下使用实验数据估计涡轮发动机部件的性能参数

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Zero-dimensional models based on the description of the thermo-gas-dynamic process are widely used in the design of engines and their control and diagnostic systems. The models are subjected to an identification procedure to bring their outputs as close as possible to experimental data and assess engine health. This paper aims to improve the stability of engine model identification when the number of measured parameters is small, and their measurement error is not negligible. The proposed method for the estimation of engine components’ parameters, based on multi-criteria identification, provides stable estimations and their confidence intervals within known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularized identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Synthesis of objective functions and subsequent scalar convolutions of these functions are used to estimate gas-path components’ parameters. A comparison with traditional methods showed that the main advantage of the proposed approach is the high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions that do not correspond to a priori assumptions and also helps to implement the gas path analysis with a limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, gas path analysis and adapting dynamic models to the needs of the engine control system.
机译：基于热气动力学过程的描述的零维模型广泛用于发动机的设计及其控制和诊断系统。该模型经过识别程序，以尽可能接近试验数据并评估发动机健康的识别程序。本文旨在改善当测量参数的数量小时发动机模型识别的稳定性，并且它们的测量误差并不可忽略不计。基于多标准识别，所提出的用于估计发动机组件的参数的方法提供了已知的测量误差内的稳定估计和它们的置信区间。有关引擎的先验信息，其参数和性能直接在正则化识别过程中使用。这种方法的数学基础是模糊集理论。这些函数的目标函数的合成和随后的标量卷曲用于估计天然气路径组件的参数。与传统方法的比较表明，所提出的方法的主要优点是参数不确定条件中估计的高稳定性。正则化减少散射，不包括不对应于先验假设的不正确的解决方案，并且还有助于利用有限数量的测量参数实现气体路径分析。该方法可用于将热力学模型与实验数据，气体路径分析和适应动态模型匹配到发动机控制系统的需求。

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