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Under Body Blasts

The main objective of this research project is two-fold: (1) to develop predictive High Performance Computational (HPC) models for underbody blast and its effects on personnel and vehicles, and (2) to develop nonlinear Model Order Reduction (MOR) methods that are applicable to these and other HPC models in order to enable

Charbel Farhat, Stanford University
Adrian Lew, Stanford University
Tarek Zohdi, University of California, Berkeley
Mingjun Wei, New Mexico State University
Martine Ceberio, University of Texas, El Paso
Miguel Argaez, University of Texas, El Paso
Raju Namburu, Army Research Laboratory
Douglas Howle, Army Research Laboratory
Pat Collins, Army Research Laboratory
Stephen Bilyk, Army Research Laboratory
LisztFE Finite Element Codes for Exascale Computers

New HPC machines have many more processors than previous architectures but have much smaller local memories associated with each processor and more levels of memory hierarchy, at least some of which need to be explicitly managed by software.  Along with these changes in the computing platform, the increase in problem size and complexity of the simulations requires changing many of the numerical methods that have been traditionally adopted in

Pat Hanrahan, Stanford University
Alex Aiken, Stanford University
Eric Darve, Stanford University
Charbel Farhat, Stanford University
Dale Shires, Army Research Laboratory
Scalable, Shared and Distributed Memory Algorithms for Computational Solids, Fluids and Geometry

Numerical simulation of physical phenomena is of great importance to the military as it can be used for designing better weaponry, training new recruits or for testing equipments to be used in the field.  The core research is to develop an open-source platform for physics simulations which can be used by researchers in academia, industry and the military for tackling real-world problems.  The PI is working to develop:

Ron Fedkiw, Stanford University
David Grove, Army Research Laboratory
Brent Kraczek, Army Research Laboratory
CFD for Blood Transfusions on the Battlefield and Inhalation of Toxic Agents in the Lung

This project has two components.  One component is to study the adhesion of blood platelets to an injured vessel site. This is a critical initial stage for the formation of a platelet plug to stop bleeding.  The second component is to study the deposition of aerosol particles in the lungs to help study the effects of airborne pollutants, and infective and toxic agents.

Eric E. G. Shaqfeh, Stanford University
Gianlucca Iaccarino, Stanford University
Eric Darve, Stanford University
Louise Pitt, USAMRIID
2D Nano-Electromechanical Devices

Among the biggest challenges in harnessing the power of nanotechnology is achieving dynamic control of mechanical, chemical and electronic properties of nanoscale devices.  Many devices stand to benefit from such control including transistors, sensors, actuators, energy harvesters, motors, robots and other locomotive devices.

Evan Reed, Stanford University
Madan Dubey, Army Research Laboratory
Raju Namburu, Army Research Laboratory
Tomas Palacios, MIT Institute for Soldier Nanotechnology
Enabling Battlefield Decision-Making in the Tactical Cloud

This research will explore the use of cloud computing to get closer to the reality of the Warfighter having the “right” information, at the “right” time, at the “right” place, and displayed in the “right” format. Using cloud computing in battlefield scenarios can be challenging for several reasons: (1) Applications have different levels of complexity and deadlines in terms of time-to-solution

Patricia Teller, University of Texas, El Paso
Michael McGarry, University of Texas, El Paso
Dale Shires, Army Research Laboratory
Song-Jun Park, Army Research Laboratory
Lam Nguyen, Army Research Laboratory
Joseph Deroba, US Army CERDEC
Dynamic Target Surveillance under Ballistic Threat

The Army has the challenge of monitoring a collection of targets while under line-of-sight ballistic threat. Uncertainties in both the environment and the behavior of threat sources make it difficult to develop optimal surveillance strategies for such missions.  The goal of this research is to develop a planning framework for target surveillance that accounts for these uncertainties.

Mykel Kochenderfer, Stanford University
Song-Jun Park, Army Research Laboratory
Dale Shires, Army Research Laboratory
Distributed Processing of Big Data for Military Applications

Distributed Computing (Fig 1) is a computing technique of dividing a problem into small sub problems, each of which is solved by one or more processors. For distributed computing, different independent processors are brought together into a cluster to solve a problem. Data are computed on distributed way across the cluster. The nodes communicate with each other through the network with message passing. All nodes in distributed computing have own memory.

Vojislav Stojkovic, Morgan State University