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Journal of Vibration Testing and System Dynamics 10(2) (2026) 177--189 | DOI:10.5890/JVTSD.2026.06.006

Joseph Shibu K$^1$, K Shankar$^2$, Ch. Kanna Babu$^1$

$^1$ Hindustan Aeronautics Limited, Bengaluru, India

$^2$ Indian Institute of Technology Madras, Chennai, India

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Abstract

Final compromise solution selection from a Pareto surface using a clustering-based methodology is presented in this paper. The Pareto surface is generated through multi-objective optimization of an aero engine rotor system with the response at critical speed due to unbalance of the rotor, the response during turning maneuvering of the aircraft and the weight of the shaft of the rotor system as objectives under critical speed constraint. Clustering is carried out in both the design and the objective space and an indirect mapping between the two spaces is created. Literature survey has shown that the application of the clustering-based methodology for the final compromise solution is limited to the Pareto front generated by two objective optimization. Present work introduces the clustering-based methodology for the final compromise solution to the Pareto surface generated from three objective optimization. Utopia point methodology used for arriving at the final compromise solution requires three separate single objective optimizations to identify the Utopia point. This step is eliminated by introducing the clustering-based methodology. The improvement in objective values are found to be same, as compared to the initial design, for the final compromise solution obtained through both the selection methodologies. Time consumed for the final compromise solution selection is reduced by 1/4${}^{\rm th}$ in comparison to the Utopia point methodology by using the proposed methodology.

Acknowledgments

We are indebted to GM, AERDC, HAL for the inspiration and the support for publication of this paper.

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