使用神经网络组件图和遗传算法最小二乘法求解器估算涡轮风扇性能

IF 1.3 Q2 ENGINEERING, AEROSPACE
Giuseppe Lombardo, Pierantonio Lo Greco, Ivano Benedetti
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引用次数: 0

摘要

涡轮风扇的计算模型可以帮助设计和测试创新部件,对于减少其对环境的影响至关重要。在本文中,我们提出了一种开发涡轮风扇数值模型的有效方法,可以进行可靠的稳态涡轮风扇性能计算。所提出的方法与各种商业算法(如 GasTurb、GSP 12 和 NPSS)中使用的方法的主要区别在于使用神经网络作为旋转分量图的多维插值方法,而不是传统的 β 参数。另外一个重要方面是,在 Matlab 中实施这种方法非常简单,而且可以高度定制涡轮风扇组件,无需为了降低问题的维度而对变量进行任何操作,因为这通常会导致与非线性涡轮风扇系统相关的雅各布矩阵的条件数过高(从而产生重大误差)。在建议的方法中,可以通过分析关系和使用根据部件台架测试数据或 CFD 模拟数据训练的神经网络来模拟部件行为。通过前馈神经网络对旋转部件图进行泛化,所有变量的平均插值误差可达 1%左右。由此产生的非线性系统采用遗传算法和最小二乘法相结合的方法求解,而不是标准的牛顿法。结果表明,涡轮风扇数值模型是收敛的,而且结果表明,随着飞行条件的变化,涡轮风扇整体性能的变化趋势与 GSP 12 软件的输出结果是一致的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Turbofan Performance Estimation Using Neural Network Component Maps and Genetic Algorithm-Least Squares Solvers
Computational models of turbofans that are oriented to assist the design and testing of innovative components are of fundamental importance in order to reduce their environmental impact. In this paper, we present an effective method for developing numerical turbofan models that allows reliable steady-state turbofan performance calculations. The main difference between the proposed method and those used in various commercial algorithms, such as GasTurb, GSP 12 and NPSS, is the use of neural networks as a multidimensional interpolation method for rotational component maps instead of classical β parameter. An additional aspect of fundamental importance lies in the simplicity of implementing this method in Matlab and the high degree of customization of the turbofan components without performing any manipulation of variables for the purpose of reducing the dimensionality of the problem, which would normally lead to a high condition number of the Jacobian matrix associated with the nonlinear turbofan system (and, thus, to significant error). In the proposed methodology, the component behavior can be modeled by analytical relationships and through the use of neural networks trained from component bench test data or data obtained from CFD simulations. Generalization of rotational component maps by feedforward neural networks leads to an average interpolation error up to around 1%, for all variables. The resulting nonlinear system is solved by a combined genetic algorithm and least squares algorithm approach, instead of the standard Newton’s method. The turbofan numerical model turns out to be convergent, and results suggest that the trend in overall turbofan performance, as flight conditions change, is in agreement with the outputs of the GSP 12 software.
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来源期刊
CiteScore
2.30
自引率
21.40%
发文量
29
审稿时长
11 weeks
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