A multiphysics-based artificial neural networks model for atherosclerosis

A novel method for the natural frequency estimation of the jet engine turbine blades based on its dimensions

This study provides a novel methodology for the natural frequency estimation of the jet engine turbine blade by using the dimension check. This paper presents a summarization of the ongoing research devoted to the method for the turbine blade natural frequency estimation. The main target of the research presented in the paper is to develop a novel method that can calculate the natural frequency of a particular turbine blade by using the dimensions of investigated turbine blade from a dimension check. This goal is achieved by the combination and interaction of several methods as for instance computed aided design (CAD) finite element modelling (FEM), artificial neural network (ANN) and others. As it is mentioned in the following chapters of the article a unique novel method is developed that can predict natural frequency according to the dimensions. The results confirmed the correctness of the new methodology, which can predict natural frequency by the dimensions of a turbine blade immediately with a relatively high level of accuracy (maximal errors are under 1.5%). Every jet engine manufacturer (GE aviation, Rolls Royce, Prat and Whitney, etc.) has to test jet engine parts for the natural frequencies in order to avoid the resonance at early stage of the manufacturing process in order to mount the blades into the engine. The experimental tests of every single turbine blade are time-consuming, a novel method can predict natural frequency according to the dimensions by using data from dimension check in 0.0051 s. The presented method is under patent pending.