We present a solution method that, compared to the traditional Gauss-Seidel approach, reduces the time required to simulate the dynamics of large systems of rigid bodies interacting through frictional contact by one to two orders of magnitude. Unlike Gauss-Seidel, it can be easily parallelized, which allows for the physics-based simulation of systems with millions of bodies. The proposed accelerated projected gradient descent (APGD) method relies on an approach by Nesterov in which a quadratic optimization problem with conic constraints is solved at each simulation time step to recover the normal and friction forces present in the system. The APGD method is validated against experimental data, compared in terms of speed of convergence and solution time with the Gauss-Seidel and Jacobi methods, and demonstrated in conjunction with snow modeling, bulldozer dynamics, and several benchmark tests that highlight the interplay between the friction and cohesion forces. . 2015.Using Nesterov's method to accelerate multibody dynamics with friction and contact.
Abstract. The last decade witnessed a manifest shift in the microprocessor industry towards chip designs that promote parallel computing. Until recently the privilege of a select group of large research centers, Teraflop computing is becoming a commodity owing to inexpensive GPU cards and multi to many-core x86 processors. This paradigm shift towards large scale parallel computing has been leveraged in Chrono, a freely available C++ multi-physics simulation package. Chrono is made up of a collection of loosely coupled components that facilitate different aspects of multi-physics modeling, simulation, and visualization. This contribution provides an overview of Chrono::Engine, Chrono::Flex, Chrono::Fluid, and Chrono::Render, which are modules that can capitalize on the processing power of hundreds of parallel processors. Problems that can be tackled in Chrono include but are not limited to granular material dynamics, tangled large flexible structures with self contact, particulate flows, and tracked vehicle mobility. The paper presents an overview of each of these modules and illustrates through several examples the potential of this multi-physics library.
SUMMARYMany-body dynamics problems are expected to handle millions of unknowns when, for instance, investigating the three-dimensional flow of granular material. Unfortunately, the size of the problems tractable by existing numerical solution techniques is severely limited on convergence grounds. This is typically the case when the equations of motion embed a differential variational inequality (DVI) problem that captures contact and possibly frictional interactions between rigid and/or flexible bodies. As the size of the physical system increases, the speed and/or the quality of the numerical solution decrease. This paper describes three methods -the gradient projected minimum residual (GPMINRES) method, the preconditioned spectral projected gradient with fallback (P-SPG-FB) method, and the Kucera method -that demonstrate better scalability than the projected Jacobi and Gauss-Seidel methods commonly used to solve contact problems that draw on a DVI-based modeling approach.
This paper addresses three questions related to the use of parallel computing in Multibody Dynamics (MBD) simulation. The "why parallel computing?" question is answered based on the argument that in the upcoming decade parallel computing represents the main source of speed improvement in MBD simulation. The answer to "when is it relevant?" is built around the observation that MBD software users are increasingly interested in multi-physics problems that cross disciplinary boundaries and lead to large sets of equations. The "how?" question is addressed by providing an overview of the state of the art in parallel computing. Emphasis is placed on parallelization approaches and support tools specific to MBD simulation. Three MBD applications are presented where parallel computing has been used to increase problem size and/or reduce time to solution. The paper concludes with a summary of best practices relevant when mapping MBD solutions onto parallel computing hardware.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.