Session: 37-03 Preliminary and structural design optimization

Paper Number: 102428

102428 - Robust Design of Herringbone Grooved Journal Bearings Using Multi-Objective Optimization Assisted With Artificial Neural Networks 

Herringbone grooved journal bearings (HGJB) are widely used in micro-turbocompressor applications due to their high load-carrying capacity, low friction, and oil-free solution. However, the performance of these bearings is sensitive to manufacturing tolerances, which can lead to significant variations in their performance and stability. In this study, design guidelines for robust design against manufacturing tolerances of herringbone grooved journal bearings supported micro-turbocompressors are proposed. These guidelines are based on surrogate model-assisted multi-objective optimization using ensembles of artificial neural networks trained on a large dataset of rotor and bearing designs as well as operating conditions. The developed framework is then applied to a series of case studies representative of heat-pump and fuel cell micro-turbomachines. To highlight the importance of rotor geometry and bearing aspect ratio in the robustness of HGJBs, two types of optimizations are performed: one focusing on optimizing the bearing geometry, and the other focusing on both the bearing and rotor geometry. The analysis of the Pareto fronts and Pareto optima of each type of optimization and case study allows for the derivation of design guidelines for the robust design of HGJB-supported rotors. Results show that by following these guidelines, it is possible to significantly improve the robustness of herringbone grooved journal bearings against manufacturing tolerances, resulting in stable operation. The best design achieved $\pm\SI{6}{\micro\meter}$ tolerance on local bearing clearance, and designs optimized for both rotor and bearing geometry outperformed those optimized for bearing geometry alone with an aspect ratio fixed at 1. Overall, this work successfully identifies design guidelines for the robust design of herringbone grooved journal bearings in micro-turbocompressor applications, demonstrating the strength of surrogate model assisted multi-objective optimization. It provides a valuable tool for engineers and designers seeking to optimize the performance and reliability of these bearings.

Presenting Author: Soheyl Massoudi Ecole Polytechnique Fédérale de Lausanne (EPFL)

Presenting Author Biography: Soheyl Massoudi currently works as a Ph.D. candidate at the Laboratory for Applied Mechanical Design (LAMD). His research focuses on design methodology via surrogate-model assisted optimization using artificial neural networks and evolutionary algorithms. The target application is gas-bearings supported turbocompressors for heat-pumps and fuel cells.

Publications:
MASSOUDI, Soheyl, PICARD, Cyril, et SCHIFFMANN, Jürg. "Robust design using multiobjective optimisation and artificial neural networks with application to a heat pump radial compressor". Design Science, 2022, vol. 8
https://doi.org/10.1017/dsj.2021.25

MASSOUDI, Soheyl et SCHIFFMANN, Jürg. "ARRID: ANN-based Rotordynamics for Robust and Integrated Design". arXiv preprint arXiv:2208.12640, 2022.
https://doi.org/10.48550/arXiv.2208.12640

Authors:

Soheyl Massoudi Ecole Polytechnique Fédérale de Lausanne (EPFL)
Jürg Schiffmann Ecole Polytechnique Fédérale de Lausanne (EPFL)