Prediction of aero-engine test bed performance based on big data technology

Abstract

Traditional mechanical product design methods often rely on the experience of designers. With these methods, only a few mechanical properties can be checked, as a result, designers cannot comprehensively understand the product performance at the design stage, which causes a great increase of the research and development costs. With the arrival of the big data era, predicting product performances by simulations has become more and more important. In order to explore the influence of the circumferential deflection angle, the axial deflection angle, the thrust and other factors of the aircraft engine on the measurement accuracy of the test bench, FEM (Finite Element Method) model with 213530 finite elements of aero-engine test bed is built in ANSYS software. Through the calculation of 173 working conditions, the results, including the displacement, stress and strain, of all the elements can be obtained. According to a large number of analysis data, the performance of the test bench in the test process can be overall predicted, which makes the test bench measurement data more accurate and reliable.