Yitao Yang

Yitao Yang

(English name: Eli Yang)

PhD Student

Xi'an Jiaotong University

Research Interests

Computational MechanicsFEMBEMIsogeometric AnalysisPrecision Assembly
0 Visits

About

I am a PhD candidate in Mechanical Engineering at Xi'an Jiaotong University (XJTU), where I focus on computational mechanics and isogeometric analysis methods for precision assembly applications.

I began my Master's program in Mechanical Engineering at XJTU in 2021. In 2023, I was selected for the prestigious Master-to-PhD direct transfer program, allowing me to continue my doctoral studies without completing a separate Master's degree. Before that, I obtained my Bachelor's degree in Mechanical Engineering from Taiyuan University of Technology (2017-2021).

My research interests lie at the intersection of computational mechanics and engineering applications, particularly in developing advanced numerical methods for solving complex mechanical problems in precision manufacturing and assembly processes.

Latest News

2025-12

Modified the layout of my academic website ๐ŸŽจ

Selected Publications

View All โ†’
Assembly accuracy prediction for over-constrained interference fits by coupling surface error and part deformation

Yitao Yang, Qiangqiang Zhao, Dewen Yu, Xiaokun Hu, Xiaohu Li, Jun Hong

Results in Engineering

Assembly accuracy prediction for over-constrained interference fits by coupling surface error and part deformation

A generalized isogeometric-analysis-based method for assembly accuracy prediction considering non-ideal surface morphology and part deformation

Yitao Yang, Qiangqiang Zhao, Dewen Yu, Xiaokun Hu, Xiaohu Li, Jun Hong

Applied Mathematical Modelling

A generalized isogeometric-analysis-based method for assembly accuracy prediction considering non-ideal surface morphology and part deformation

Latest Projects

View All โ†’
Digital Assembly Technology for Precision Spindles

Digital Assembly Technology for Precision Spindles

2021-2024

Project Description: Constructed a digital assembly model for precision spindles using measured data.

ANSYSAPDLPrecision AssemblyDigital Modeling

Latest Resources

View All โ†’

Numerical Methods for Partial Differential Equations

Self-Study Notes

2022-2023

Study notes for Numerical Methods for PDEs course, including learning materials for GeoPDEs library.

NotesMathematicsPDEsGeoPDEs