Examination of parametric cycle analysis of a turbofan engine with Python code

In this chapter the wide array of engineering devices, from the kitchen tap (a valve) to supersonic aircraft, the basic design of which depends upon considerations of the flow of gases and liquids, is shown. Much the same is true of most natural phenomena from the atmosphere and our weather to ocean waves, and the movement of sperm and other bodily fluids. In this textbook a number of the concepts, principles, and procedures which underlie the analysis of any problem involving fluid flow or a fluid at rest are introduced. In this Introduction, examples have been selected for which, by the end of the book, the student should be in a position to make practically useful engineering-design calculations. These include a dam, a rocket motor, a supersonic aerofoil with shock and expansion waves, a turbojet engine, a turbofan engine, and the blading of a gas turbine.

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ER rule classifier with an optimization operator recommendation

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210629 ◽

2021 ◽

pp. 1-13

Author(s):

Xiaoyan Wang ◽

Jianbin Sun ◽

Qingsong Zhao ◽

Yaqian You ◽

Jiang Jiang

Keyword(s):

Classification Accuracy ◽

Comprehensive Analysis ◽

Small Sample ◽

Empirical Knowledge ◽

Classification Methods ◽

Turbofan Engine ◽

Training Samples ◽

Optimization Operator ◽

Recommendation Strategy

It is difficult for many classic classification methods to consider expert experience and classify small-sample datasets well. The evidential reasoning rule (ER rule) classifier can solve these problems. The ER rule has strong processing and comprehensive analysis abilities for diversified mixed information and can solve problems with expert experience effectively. Moreover, the initial parameters of the classifier constructed based on the ER rule can be set according to empirical knowledge instead of being trained by a large number of samples, which can help the classifier classify small-sample datasets well. However, the initial parameters of the ER rule classifier need to be optimized, and choosing the best optimization algorithm is still a challenge. Considering these problems, the ER rule classifier with an optimization operator recommendation is proposed in this paper. First, the initial ER rule classifier is constructed based on training samples and expert experience. Second, the adjustable parameters are optimized, in which the optimization operator recommendation strategy is applied to select the best algorithm by partial samples, and then experiments with full samples are carried out. Finally, a case study on a turbofan engine degradation simulation dataset is carried out, and the results indicate that the ER rule classifier has a higher classification accuracy than other classic classifiers, which demonstrates the capability and effectiveness of the proposed ER rule classifier with an optimization operator recommendation.

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