A methodology to determine the precision uncertainty in gas turbine engine cycle models

M. D. J. Gurrola Arrieta, R. Botez
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Abstract

This paper proposes a methodology to define and quantify the precision uncertainties in aerothermodynamic cycle model comparisons. The total uncertainty depends on biases and random errors commonly found in such comparisons. These biases and random errors are classified and discussed based on observations found in the literature. The biases account for effects such as differences in model inputs, the configurations being simulated, and thermodynamic packages. Random errors consider the effects on the physics modeling and numerical methods used in cycle models. The methodology is applied to a comparison of two cycle models, designated as the model subject to comparison and reference model, respectively. The former is the so-called Aerothermodynamic Generic Cycle Model developed in-house at the Laboratory of Applied Research in Active Control, Avionics and AeroServoElasticity (LARCASE); the latter is an equivalent model programmed in the Numerical Propulsion System Simulation (NPSS). The proposed methodology is intended to quantify the bias and random errors effects on different cycle parameters of interest, such as thrust, specific fuel consumption, among others. Each bias and random errors are determined by deliberately preventing the effects from other biases and random errors. The methodology presented in this paper can be extended to other cycle model comparisons. Moreover, the uncertainty figures derived in this work are recommended to be used in other model comparisons when no better reference is available.
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燃气轮机循环模型精度不确定度的确定方法
本文提出了一种定义和量化空气热力循环模式比较中精度不确定性的方法。总的不确定性取决于这种比较中常见的偏差和随机误差。这些偏差和随机误差的分类和讨论基于观察发现的文献。偏差解释了模型输入、被模拟的结构和热力学包的差异等影响。随机误差考虑了对循环模型的物理模拟和数值方法的影响。将该方法应用于两个周期模型的比较,分别指定为被比较模型和参考模型。前者是主动控制、航空电子和航空伺服弹性应用研究实验室(LARCASE)内部开发的所谓的空气热力学通用循环模型;后者是在数值推进系统仿真(NPSS)中编程的等效模型。所提出的方法旨在量化偏差和随机误差对不同循环参数的影响,如推力、比油耗等。每个偏差和随机误差都是通过故意防止其他偏差和随机误差的影响来确定的。本文提出的方法可以推广到其他周期模型的比较。此外,当没有更好的参考资料时,建议在其他模型比较中使用本工作中得出的不确定性数字。
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