Full-field dynamic strain reconstruction of an aero-engine blade from limited displacement responses

Blade strain distribution and its change with time are crucial for reliability analysis and residual life evaluation in blade vibration tests.Traditional strain measurements are achieved by strain gauges(SGs)in a contact manner at discrete positions on the blades.This study proposes a method of full-field and real-time strain reconstruction of an aero-engine blade based on limited displacement responses.Limited optical measured displacement responses are utilized to reconstruct the full-field strain.The full-field strain distribution is in-time visualized.A displacement-to-strain transformation matrix is derived on the basis of the blade mode shapes in the modal coordinate.The proposed method is validated on an aero-engine blade in numerical and experimental cases.Three discrete vibrational displacement responses measured by laser triangulation sensors are used to reconstruct the full-field strain over the whole operating time.The reconstructed strain responses are compared with the results measured by SGs and numerical simulation.The high consistency between the reconstructed and measured results demonstrates the accurate strain reconstructed by the method.This paper provides a low-cost,real-time,and visualized measurement of blade full-field dynamic strain using displacement response,where the traditional SGs would fail.

Biological Sequence Classification:A Review on Data and General Methods

With the rapid development of biotechnology,the number of biological sequences has grown exponentially.The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information.There are many branches of biological sequence classification research.In this review,we mainly focus on the function and modification classification of biological sequences based on machine learning.Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA,RNA,proteins,and peptides.However,there are hundreds of classification models developed for biological sequences,and the quite varied specific methods seem dizzying at first glance.Here,we aim to establish a long-term support website(http://lab.malab.cn/~acy/BioseqData/home.html),which provides readers with detailed information on the classification method and download links to relevant datasets.We briefly introduce the steps to build an effective model framework for biological sequence data.In addition,a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included.Finally,we discuss the current challenges and future perspectives of biological sequence classification research.