Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
May 6, 2026AerospaceOpen Access

A Component-Decoupled and Physics-Constrained Hybrid Modeling Framework for Turbojet Engine Performance Prediction

View Full Paper
Ask AI
Bookmark
Share

Authors

顾顾怀平LJLinyuan JiaHDHui Duan

Discussion

Loading...

Member takes

Overview

A hybrid modeling approach improves performance prediction accuracy in turbojet engines, suggesting better condition monitoring and health management.

Key Points

  • The research aims to create a hybrid modeling framework for turbojet engine performance prediction.
  • Developed a component-decoupled, physically constrained modeling framework.
  • Applied physics-guided feature engineering and mutual-information-based feature selection.
  • Coupled predictions via aerothermodynamic constraints to reconstruct parameters.
  • Achieved 1.157% maximum relative deviation and 0.226% average relative deviation for thrust.
  • Model predictions for key gas path parameters showed deviations within 0.3%.
  • Demonstrated improvement in accuracy and generalization compared to data-driven models.

Cite This Study

顾怀平 et al. (2026) studied this question.

synapsesocial.com/papers/69fa980604f884e66b531dcchttps://doi.org/10.3390/aerospace13050425
View Full Paper
Ask AI
Bookmark
Share