Virtual Computing Environment for Future Combat Systems

Participating Teams and Team Members Participating Teams –Enabling Technologies in Support of NCC –Computational Structural Mechanics and material-by-design –Chem-bio Team Members –Robert Baffeur –Baoquan Chen –Ravi Janardan –Shashi Shekhar –Kumar Tamma –Jon Weissman

Motivating Example Weather, Terrain, Base map Demographics, Transportation Plume Modeling ( Images from ) Examples Chem-Bio portfolio project (Dr. Alibadi) Scenario – managing a (say chem-bio) attack Components of the system Gathering initial conditions Weather data from NWS or JSU Terrain maps (State of federal Govt.) Building geometry (City Govt.) Plume simulation using supercomputers Visualizing results – map, 3D graphics Response planning Current effort is very large because of Lack of large-scale reusable components Lack of integrations services Autonomous components Distribution – location of data, computers Heterogeneity – e.g. data formats Service model – interfaces are low-level

Problem Definition Other Examples Projectile-Target interaction portfolio Precision targeting of missiles Other – Logistics of troop/equipment movement Deadlines, congstion, price, … Issue: Mega-programming [Wiederhold 98] –Software size is growing –Shift in Programming tasks –Larger systems Reuse of well-defined components Need more integration effort Current tools are inadequate –Few reusable components –Little support for component integration Integration Coding SmallLarge Medium

Technical Goals Develop a VCE –To support rapid development of –for multi-disciplinary large-scale software development –To increase the lethality and survivability of Army FCS What is a VCE? –Tools to integrate distributed heterogeneous autonomous components Data format interchange (ICE, J. Clarke) Resource identification, scheduling (J. Weissman) –A collection of reusable components Large-scale autonomous With well-defined services –Example components Visualization(B. Chen) Routing(S. Shekhar) Prototyping(R. Janardan) Virtual HPC design(K. Tamma)

VCE - System Architecture Fluid Dynamic Simulation Plume simulation of toxic agents Physical Prototyping Create 3D physical prototype of terrain Database Graphic objects: e.g. 3D Model of building GIS layer: e.g. sections, exits, capacity Visualization Engine Animation of plume evolution and people movements in the building GUI User interface to initialize seat occupancy and toxic location User VDG&DICEVDG&DICE HPGIS Route Planning for evacuation with capacity constraints and toxic constraints

Services by Components HPGIS: Develop algorithms for computing evacuation route plans Visualization Engine : Develop graphic visualization tools to display evolution of plume and moving of people in the building Fluid Dynamic Simulation : Predict and simulate the evolution of plume in the building Physical Prototyping Efficient algorithms and software to create physical prototypes of terrain Virtual Data Grid : Provide an interface to underlying Grid infrastructure (Globus) to support remote execution of components, provide a VDG interface to ICE and Globus ICE/DICE : Test ICE/DICE system that has interface for exchanging data with other applications for real-time visualization.

Main Activities Tools for integration of components: Installed ICE system and Globus on Army Center machines Virtual data grid design and evaluation –to automate the scheduling and execution of components Reusable Components: 3D visualization of Atlanta plume evolution data from Prof. Alibadi GIS – Geo-registration of Atlanta buildings, Addition of roads, vegetation Dynamic Routing algorithms for Atlanta roadmap in presence of plumes Prototyping – algorithms to prototype terrain

Status ICE system installation QingSong Lu attended ICE workshop in June We have an installation of ICE Acquisition of 3D Models Acquired St. Paul downtown model, Atlanta downtown model with plume simulation Tested St. Paul model with graphics rendering programs Acquisition of Visualization Environments Reviewed visualization equipment at ARL, Iowa State U, MechDyne, SGI, etc. Working on getting a configuration to start with and grow with Development of Visualization Software Tested St. Paul model with graphics rendering software Working with Prof. Candler to visualize fluid-flows over urban terrain HPGIS : Route Planning Algorithms Surveyed literature on routing algorithms and evacuation planning Formulated the problem and developed preliminary approaches

Rapid Protoyping: Project Goal Develop scalable HPC techniques that allow the effective deployment of RPP in Battlefield Visualization. RPP can also be used in Signature Modeling to create detailed physical scale models of complex weapons systems (e.g., tanks, UAVs) that can then be tested for their signature mitigation capabilities (e.g., radar evasion). Create, on demand, physical scale models of enemy terrains and assets to help mission planners and field commanders develop and evaluate different combat strategies. Digital data generated from satellite images, airborne laser scanners, etc. Sandbox Prototype (graphics from E. Swann at NRL)

Work done in Year 1 Investigated efficient methods for identifying structural properties of battlefield topographies. Digital Elevation Model (graphic from USGS) Overlay Map of Afghanistan from Ikonos Satellite (graphic from Advantage: Existence of a natural build direction requiring no/little support structures—hence fast builds. Topographies tend to be terrains (height fields) or near-terrains.

Terrain Recognition Algorithm Designed very efficient algorithm to decide if a cross-section of a topography is a 2D terrain in some direction. Computational effort scales just linearly with size of cross-section. Result based on geometric properties that depend only on edge orientations, not on global connectivity of polygon. Yields a fast, ‘first-cut’ test for determining if given topography is a 3D terrain. 2D terrain (for vertical dir.) Not a 2D terrain (for any dir.)

Terrain Decomposition Algorithm Designed efficient algorithm to decompose a 2D near-terrain into two terrains. Computational effort scales roughly from linear to quadratic. Searches for decomposition via a rotational sweep on convex hull. Allows for fast building of the two pieces in parallel. Near-terrain Decomposition into two terrains

There is a need to design/develop and implement a general purpose flexible multibody dynamics code which possesses the following capabilities for general applications of Army interest: Efficiently integrate Index-3 stiff DAE, preserving the order of accuracy in primary/algebraic variables by using either: –Index-3 formulation. –Stabilization –Constraint preservation –Prevent order reduction Be able to represent large deformations/strain accurately as in the case of inertial coordinate formulation and finite deformation dynamics. Invariant conserving algorithms for long term rigid/flexible multibody dynamics. Optimal/Intelligent dissipation in order to damp out high frequency oscillations and smart integrators. Be able to model contact physics at joints. HPC simulations and parallel environments Innovative HPC design and Analysis Approaches for Flexible Multi-body Dynamics – K Tamma

Multibody systems Multibody system is defined to be a collection of bodies, which are kinematically constrained due to different types of joints. Each body may undergo large translations & rotations. Rigid Body Fundamental Elements of Multibody System Body 1 Body 2 Body 3 Body n Joint DamperActuator Force Element Multibody System InertiaElasticityViscosityForces Elastic BodySpringDamperReactionDriver Finite Elements Rigid Body Dynamics Interface Some Common Joints

Progress 1 Existing New Accuracy – Velocity Simulation of a spin top motion using energy conserving scheme 2 2 Andrews seven body squeezer mechanism – New developments prevented order reduction

Next Few Talks on VCE Portfolio HPGIS: Next talk by S. Shekhar Visualization : Talk by B. Chen Virtual Data Grid : Talk by J. Weissman