🤖 AI Summary
This work addresses the challenges of high computational cost, complex distributed deployment, and the lack of user-friendly, high-throughput frameworks in large-scale multibody system simulation. It presents the first deep integration of the high-fidelity multibody dynamics engine Chrono with the distributed computing platform Ray, yielding a modular and scalable simulation workflow framework. The framework provides high-level, engineering-oriented abstractions that enable users to efficiently conduct large-scale parametric simulation studies without managing underlying infrastructure. Its scalability and practical utility are demonstrated through applications in lunar lander parameter identification and experimental design for continuum-based terramechanics models. Released as open-source software, the framework significantly lowers the barrier to entry for high-throughput simulation.
📝 Abstract
Large-scale simulation studies can provide invaluable insights across computational engineering efforts, but they are often computationally demanding, requiring the use of distributed computing, which is itself not a simple task. Chrono::Ray addresses this challenge by integrating the high-fidelity multibody dynamics simulation engine Chrono with the open-source distributed computing platform Ray. The result is a modular workflow framework providing user-friendly abstractions for large-scale engineering simulation studies, supporting scalable orchestration of large ensembles of simulation trials without requiring users to directly manage distributed infrastructure. The current capabilities of the framework are demonstrated through two representative examples: parameter recovery for a multibody lunar lander model, and design of experiments for parameters of a continuum terramechanics model. Chrono::Ray is a part of the larger Project Chrono ecosystem and is released as an open-source software package, with source code available at https://github.com/uwsbel/chrono-ray.git.