Work

Rapid generation method of element model with geometric distribution errors for high-fidelity aero-engine simulation

Year: 2026

Type: article

Abstract: • Novel method generates error-embedded meshes directly without CAD reconstruction. • Modeling efficiency is improved 62-fold, requiring only 20.8s for generation. • Automated generation module is integrated into commercial CAE software. • Method improves high-fidelity prediction accuracy of aero-engine casing assembly. The performance and service life of aero-engines strongly depend on assembly quality. High-fidelity assembly simulation is therefore essential, yet its accuracy relies on precise representation of non-uniform geometric errors on assembly interfaces. However, existing approaches cannot adequately capture their spatial distribution or meet modern industrial requirements in accuracy and efficiency. This study proposes a rapid method for generating element models with geometric distribution errors (EM-GDE). The method reconstructs the geometric error surface from point-cloud measurements. It then embeds the error field directly into the finite element mesh using a node-wise vector-height re-placement algorithm, eliminating CAD reconstruction. An EM-GDE generation module was integrated into commercial CAE software to support engineering deployment. Experimental tests show that the method reduces modeling time to 20.8 s, achieving a 62-fold improvement while maintaining high accuracy. A case study on aero-engine casing assembly shows that EM-GDE-based simulation improves coaxiality prediction accuracy compared with ideal and tolerance-based models, reaching an average precision of 88.59% with experimental measurements. The results further reveal how non-uniform geometric errors induce asymmetric deformation and coaxiality degradation. This study establishes an effective framework for online high-precision simulation of aero-engines performance and offers significant engineering value for digital assembly and assembly process optimization.

Source: Results in Engineering

DOI: 10.1016/j.rineng.2026.110649

Authors: Yiqi Liu, Xin Jin, Xiao Chen, Lingling Shi, Chaojiang Li

Institution: Beijing Institute of Technology

Language: English

Topic: Manufacturing Process and Optimization

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,940

Funders: National Natural Science Foundation of China, Natural Science Foundation of Beijing Municipality, Beijing Municipal Natural Science Foundation