The primary goals of the lab are associated with development of advanced algorithms for modeling, simulating, and analyzing the behavior of complex dynamic systems. Examples of such systems include, but are not limited to, spacecraft, bio-molecular systems, molecular dynamics of advanced materials, robotic systems, automotive applications, the human body, and manufacturing operations. These analysis and simulation tools emphasize the development of equations of motion, solution for state derivatives, temporal integration, determination of parameter sensitivities, and the nature of the computing system on which the simulation may be performed as a single unified problem. Consequently, these algorithms can obtain the desired accuracy, while requiring far fewer computational operations than their more traditional counterparts. This results in simulations which run much more quickly or, equally important, allow a level modeling and analysis that would otherwise be prohibitively expensive. This is often accomplished through the use of special low order algorithms, specialized integration schemes, and the intelligent exploitation of parallel computing.


  • Development of highly efficient parallelizable algorithms for general multibody systems;
  • Multi-Rate temporal integration schemes;
  • Advanced material modeling; Molecular systems; Biomechanical modeling;
  • Physics-based modeling of cellular interaction and behavior; (Stem Cell Simulation 1416 KB)
  • Multi-continuous body modeling of the dynamic behavior of translating media (e.g. drive belts, tracks and tracked vehicles, etc.);
  • Simulation, Design and Control of MEMS devices;
  • Determination of the optimal form of the equations of motion (in the sense of maximizing simulation speed) for a complex multibody system when available computer resources have sub-optimal numbers of processors;
  • Automated determination of design parameter values that yield near optimal design performance of complex mechanism from a dynamics point of view;
  • Development of methods for producing design sensitivity information at a greatly reduced cost;
  • Improve ability to effectively exploit future massively parallel computing systems;
  • Enhance ability to consider unilateral constraints, friction, impact, interference, etc. in the simulation and control of complex dynamic processes;

  • For a brief overview of the activities that go on in our lab a slideshow which opens in a new window is available.

    Last modified on July 12, 2004 by J. Evans; Created by J. Evans
    2004 Rensselaer - Department of Mechanical, Aerospace and Nuclear Engineering