Intelligent identification method for whole aero-engine connection stiffness

A novel intelligent identification method for determining connection stiffness values of the aircraft engine vibration model is proposed. Firstly, a dynamic finite element model of an aero-engine is established. The stiffness values of supports and mountings are taken as the connection parameters to be optimized, and then the natural frequencies of the whole machine are obtained under different connection stiffness values by finite element simulations. The regression function, which is from the stiffness to the natural frequency, is constructed in the support vector machines method. Then, the genetic algorithm is applied to a multi-objective optimization. Based on the real natural frequencies (which can be obtained by a modal test), a fitness function of multi-objective optimization of genetic algorithm is established. Using the real number coding method, the connection stiffness values of the whole machine are finally identified. An aero-engine rotor tester with casing is taken as an example to verify the method. According to the results of a modal test, the stiffness values of the supports and mountings are identified, and the results show the method effectiveness

Influence of support stiffness on aero-engine coupling vibration quantitative analysis

This paper investigates the whole aero-engine coupling vibration, as a rotor tester to be the research object. The rotor tester system is composed of two mountings, stator system, support structures and the rotor system. The modal experiment of the whole tester under the condition of mounting in the test room is carried out. The finite element (FE) model of the rotor tester is built, and the model was modified and validated according to the modal test results. A rotor-stator coupling factor and a section rotor-stator rubbing risk coefficient are proposed, the influence of the support stiffness values on the engine vibration characteristics, such as natural frequencies, modal shapes, rotor-stator coupling degree, and stator-rotor rubbing risk degree at compressor and turbine section is quantitative studied. Results show that the support stiffness contributes to the rigid body modal shapes greatly and to the rotor bending ones slightly. The factor and coefficient defined in this study are both reasonable, and they can reflect the corresponding characteristics exactly. Moreover, the effect of the supports stiffness values on the rotor-stator coupling degree and the rotor-stator rubbing risk degree is nonlinear. The rotor-stator coupling factor and the section stator-rotor rubbing risk coefficient proposed in this study provide a new way to quantitatively research the whole engine coupling vibration

Data-Driven Construction Method of Material Mechanical Behavior Model

To obtain the mechanical behavior response of the material under loading, a data-driven construction method of material mechanical behavior model is proposed, which is universal for predicting the mechanical behavior of any material under different loads. Based on the framework of artificial intelligence and finite element simulation, the method uses Python script to drive an Abaqus loop calculation to obtain data sets and performs artificial intelligence training on data sets to realize model construction. In this paper, taking the quasi-static tension of 9310 steel as an example, a material mechanical behavior model is constructed, and the accuracy of the prediction model is verified based on the experimental data. The results show that the simulation results are in good agreement with the experimental data. The error between the simulation results and the experimental results is within 2%, indicating that the model constructed by this method can effectively predict the mechanical properties of materials

Multi-Objective Optimization Design Method for Whole-Aeroengine Coupling Vibration

With the increasing thrust-to-weight ratio of modern aeroengines and the widespread adoption of thin-walled casing, the impact of the coupling vibration effect between the rotor and stator on the critical speed, rotor–stator mode shape coordination, and response characteristics of the whole engine have become increasingly prominent. However, the vibrational design of the whole-engine coupling has thus far relied on human experience, revealing a need for the study of its intelligent optimization. In this study, to quantitatively analyze the vibration of the whole engine, three vibration evaluation indexes were defined, which were risk coefficients for the critical speed, rotor strain energy, and cross-section rotor–stator rubbing. Using these indexes as the optimization targets, a multi-objective intelligent optimization design method for aeroengine support stiffness was proposed. The design optimization of the support stiffness of a turbofan engine with a large bypass ratio was performed. The results showed that the vibration index of the whole machine can be reduced to different degrees and that the whole-engine coupling vibration is optimized

A Slime Mold-Ant Colony Fusion Algorithm for Solving Traveling Salesman Problem

The Ant Colony Optimization (ACO) is easy to fall into the local optimum and its convergence speed is slow in solving the Travelling Salesman Problem (TSP). Therefore, a Slime Mold-Ant Colony Fusion Algorithm (SMACFA) is proposed in this paper. Firstly, an optimized path is obtained by Slime Mold Algorithm (SMA) for TSP; Then, the high-quality pipelines are selected from the path which is obtained by SMA, and the two ends of the pipelines are as fixed-point pairs; Finally, the fixed-point pairs are directly applied to the ACO by the principle of fixed selection. Hence, the SMACFA with fixed selection of high-quality pipelines is obtained. Through the test of the chn31 in Traveling Salesman Problem Library (TSPLIB), the result of path length was 15381 by SMACFA, and it was improved by 1.42% than ACO. The convergence speed and algorithm time complexity were reduced by 73.55 and 80.25% respectively. What's more, under the ten data sets of TSPLIB, SMACFA outperformed other algorithms in terms of the path length, convergence speed and algorithm time complexity by comparison experiments. It is fully verified that the performances of SMACFA is superior to others in solving TSP