# An error indicator-based adaptive reduced order model for nonlinear structural mechanics -- application to high-pressure turbine blades

## TL;DR

This paper introduces an adaptive reduced order modeling approach with an error indicator for nonlinear structural mechanics, specifically applied to high-pressure turbine blades with uncertain thermal loading, improving computational efficiency and accuracy.

## Contribution

A novel error indicator-based adaptive reduced order model that updates itself when the error exceeds a threshold, suitable for complex nonlinear industrial applications.

## Key findings

- Effective in handling nonlinear elastoviscoplastic behavior
- Successfully applied to a 5 million DOF industrial problem
- Computations performed in parallel with distributed memory

## Abstract

The industrial application motivating this work is the fatigue computation of aircraft engines' high-pressure turbine blades. The material model involves nonlinear elastoviscoplastic behavior laws, for which the parameters depend on the temperature. For this application, the temperature loading is not accurately known and can reach values relatively close to the creep temperature: important nonlinear effects occur and the solution strongly depends on the used thermal loading. We consider a nonlinear reduced order model able to compute, in the exploitation phase, the behavior of the blade for a new temperature field loading. The sensitivity of the solution to the temperature makes {the classical unenriched proper orthogonal decomposition method} fail. In this work, we propose a new error indicator, quantifying the error made by the reduced order model in computational complexity independent of the size of the high-fidelity reference model. In our framework, when the {error indicator} becomes larger than a given tolerance, the reduced order model is updated using one time step solution of the high-fidelity reference model. The approach is illustrated on a series of academic test cases and applied on a setting of industrial complexity involving 5 million degrees of freedom, where the whole procedure is computed in parallel with distributed memory.