Simulation-based optimization of mechanical system reliability under variable load conditions

Abstract

Mechanical systems are increasingly required to perform reliably under variable and often unpredictable load conditions across diverse industrial applications. Traditional reliability analysis methods, which typically assume static or uniform loads, are insufficient to capture the dynamic behavior and failure mechanisms encountered in real-world scenarios. This article explores the role of simulation-based optimization in enhancing the reliability of mechanical systems subjected to fluctuating operational demands. It highlights how advanced modeling techniques - such as finite element analysis, probabilistic simulation, surrogate modeling, and multi-objective optimization - can be employed to evaluate performance, predict failure modes, and identify design improvements. The study emphasizes the importance of incorporating uncertainty, validating virtual models with experimental data, and using machine learning tools to augment simulation insights. Applications across rotating machinery, joints and interfaces, and thermal-mechanical systems are examined. The integration of simulation with real-time monitoring, material modeling, and sustainable engineering practices demonstrates its critical role in developing robust, cost-efficient, and future-ready mechanical systems.