A gas turbine engine performance solving method and system fusing micro-operators and static computation graphs

By combining differentiable operators with static computation graphs and using inverse mode automatic differentiation techniques to obtain the Jacobian matrix, the problems of solution efficiency and accuracy in the performance simulation of gas turbine engines are solved, and efficient and accurate performance solutions are achieved.

CN122287337APending Publication Date: 2026-06-26INST OF ENGINEERING THERMOPHYSICS - CHINESE ACAD OF SCI
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
INST OF ENGINEERING THERMOPHYSICS - CHINESE ACAD OF SCI
Filing Date
2026-03-30
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

The existing gas turbine engine performance simulation suffers from problems such as low Jacobian matrix solution efficiency, insufficient numerical accuracy, and decoupling between component models and solvers, resulting in high computational complexity, large numerical errors, and the inability to obtain accurate gradients using automatic differentiation techniques.

Method used

The characteristics of the components are reconstructed using differentiable operators, a static computation graph is constructed, and the Jacobian matrix is ​​obtained through inverse mode automatic differentiation technology to achieve analytical solution.

Benefits of technology

It improves the efficiency of solving the Jacobian matrix, eliminates numerical errors, enhances the robustness and accuracy of the solver, and supports gradient-driven design and optimization.

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Abstract

This invention discloses a method and system for solving the performance of a gas turbine engine by integrating differentiable operators and static computation graphs. The method includes: constructing differentiable component operators: reconstructing the input-output mapping relationship of the core components of the gas turbine engine into a globally differentiable surrogate model, which possesses analytical gradient solving capability; constructing the overall engine static computation graph: utilizing a programming framework supporting automatic differentiation, establishing a general graph construction mechanism decoupled from the engine topology, which automatically instantiates the corresponding static computation graph based on the input engine topology description; and reverse-mode automatic differentiation solving: calculating the residual vector through forward propagation, and using reverse-mode automatic differentiation technology to propagate the gradient flow backward along the static computation graph, analytically obtaining the Jacobian matrix required for Newton iteration, and completing the solution of the engine's overall performance residual equation. This invention achieves efficient, accurate, and stable simulation of gas turbine engine performance.
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