Rui Li

The conventional grey relational analysis (GRA) demonstrates limitations in dynamic simulation data evaluation due to its failure to simultaneously account for geometric similarity among dynamic indicators and the proximity of data curve distances. This deficiency manifests as a compromised robustness in noise resistance and interference suppression, consequently leading to discrepancies between model accuracy and practical scenarios. To address these shortcomings, this paper proposes a robust grey relational analysis-based accuracy evaluation method (RGRA-AEM). The methodology incorporates the expected penetration rate to facilitate interpolation computation and employs deviation acceptability as a distance threshold indicator. By integrating the grey relational degree, mean squared deviation distance, and accuracy modeling, this approach achieves enhanced stability in accuracy assessment. It effectively mitigates the inherent weakness of traditional GRA that overemphasizes sequential curve similarity while significantly improving the anti-noise performance and interference resistance of grey relational coefficients. Experimental validation through the internal ballistic test-simulation dynamic data of a hybrid rocket motor conclusively demonstrates the superior robustness of the proposed methodology.