Skip to:

Reduced Order Modelinlg for Under Body Blasts

Charbel Farhat, Stanford University
Philip Avery, Stanford University
Pat Collins, Army Research Laboratory
Jari Knap, Army Research Laboratory

Parametric model order reduction has become an indispensable tool for computational-based design and optimization, statistical analysis, embedded computing, and real-time optimal control. In essence, it enables solutions to complex modeling problems in a fraction of the compute time.  It is also essential for “what-if” scenarios where real-time simulation responses are desired. The parametric model reduction methodologies developed at the Army High Performance Computing Research Center (AHPCRC) in collaboration with ARL/CISD, and their underlying high-dimensional computational technologies have significantly advanced the state-of-the-art in this field, particularly for DoD and Army problems. They have also been disseminated at Boeing, Northrop Grumman Newport News, the Navy Research Laboratory, the Jet Propulsion Laboratory, and many other research laboratories.  Accomplishments of the Stanford collaboration include:

Projection-based nonlinear model order reduction technologies have been developed and implemented in the AERO Suite deployed at ARL. These computational technologies have been successfully applied to the solution in real time of “what-if” underbody blast problems (ARL/WMRD), structural collapse problems (Navy), aerodynamic problems (Boeing), and automotive problems (Volkswagen). They have also been successfully applied to the reduction of the CPU time associated with the solution of Army multiscale problems in structural dynamics and solid mechanics by 6 orders of magnitude and have enabled a new vision for virtual testing that will reduce cost by avoiding destructive testing, and increase safety.

Model order reduction leverages the power of supercomputing with the ability of low-dimensional computational models to perform in real-time.  This enables predictive simulation on mobile devices and therefore effectively assist testing. It is also a main enabler of embedded computing for model predictive control and decision making.


The advances made by the Army High Performance Computing Research Center on non-linear, model order reduction methods have enabled capabilities with the potential to increase the effectiveness of soldier protection systems through faster and better designs of such systems by enabling optimization methods to quickly search much larger parameter spaces for the optimal design.Dr. J. Pat Collins, ARL