Dynamic Modeling and Simulation of Multi-Body Mechanical Systems: A Comprehensive Review of Methods, Tools, and Applications

Abstract

The dynamic modeling and simulation of multi-body mechanical systems (MBS) form a cornerstone in modern mechanical engineering, enabling in-depth analysis of the kinematic and kinetic behaviors of interconnected rigid and flexible components. MBS are foundational to a range of critical applications, from automotive suspensions and aerospace mechanisms to robotics and biomechanical structures. As system complexity and performance requirements increase, accurate and scalable modeling techniques are essential for both design validation and performance optimization. This review systematically explores fundamental modeling formulations, including Newton–Euler, Lagrangian, and Kane’s methods, as well as strategies for constraint stabilization and numerical integration. A detailed examination of industry-standard and open-source simulation platforms—such as Automated Dynamic Analysis of Mechanical Systems (ADAMS), Simpack, RecurDyn, Project Chrono, and MBsysC—is presented, with a focus on capabilities for co-simulation, flexible body dynamics, and control system integration. The paper analyzes domain-specific MBS applications and addresses emerging paradigms such as AI-based modeling, real-time digital twins, and multiphysics co-simulation. By bridging foundational theory with modern software tools and outlining future research trajectories, this article aims to serve as a consolidated reference for engineers, researchers, and system analysts engaged in mechanical dynamics and system-level simulation. By bridging foundational theory with modern software tools and outlining future research trajectories, this article aims to serve as a consolidated reference for engineers, researchers, and system analysts engaged in mechanical dynamics and system-level simulation. Emphasis is also placed on the importance of interoperability between simulation tools and control design environments, scalable model reduction techniques, and the integration of sensor data for model calibration. The review concludes by identifying key challenges and opportunities in the development of next-generation MBS frameworks that are more adaptive, intelligent, and responsive to real-world operating conditions.