Award Abstract #0540248
DDDAS-TMRP: Planet-in-a-Bottle: A Numerical Fluid-Laboratory System

NSF Org:	CNS Division of Computer and Network Systems
Initial Amendment Date:	September 15, 2005
Latest Amendment Date:	September 15, 2005
Award Number:	0540248
Award Instrument:	Standard Grant
Program Manager:	Krishna Kant CNS Division of Computer and Network Systems CSE Directorate for Computer & Information Science & Engineering
Start Date:	January 1, 2006
Expires:	June 30, 2009 (Estimated)
Awarded Amount to Date:	$600000
Investigator(s):	Charles Leiserson cel@csail.mit.edu (Principal Investigator) Bradley Kuszmaul (Co-Principal Investigator) John Marshall (Co-Principal Investigator) Christopher Hill (Co-Principal Investigator)
Sponsor:	Massachusetts Institute of Technology 77 MASSACHUSETTS AVE Cambridge, MA 02139 617/253-1000
NSF Program(s):	ITR-DYNAMIC DATA DRIV APP SYS, DYNAMIC DATA DRIVEN APPL SYSTS
Field Application(s):	0000912 Computer Science
Program Reference Code(s):	HPCC, 9218
Program Element Code(s):	7581, 7481

ABSTRACT

This project will create a "Numerical Fluid-Laboratory System" to enable enhancing the understanding of the Earth's weather and climate, which depend critically on accurate forecasting and state-estimation technology. This research effort aims to design and build a laboratory-scale DDDAS, called Planet-in-a-Bottle, as a practical and inexpensive step toward a planet-scale DDDAS. The Planet-in-a-Bottle DDDAS will emulate many of the large-scale challenges of meteorological and oceanographic state-estimation and forecasting but provide a controlled setting to allow systematic engineering strategies to be employed to devise more efficient and accurate techniques. The Planet-in-a-Bottle DDDAS will consist of two interacting parts: a fluid lab experiment and a numerical simulator. The system will employ data assimilation in which actual observations are fed into the simulator to keep the models on track with reality, and will employ sensitivity-driven observations and mesh refinement in which the simulator targets the real-time deployment of sensors to particular geographical regions and times for maximal effect, and refines the mesh to better predict the future course of the fluid experiment. In addition, the feedback loop between targeting of both the observational system and mesh refinement will be mediated, if desired, by human control.

The project will investigate adjoint methods to determine how and where to deploy observations, as well as how and where to refine the simulation. The laboratory will provide insights into nonlinear fluid dynamics by visualizing and analyzing the three-dimensional behavior of a natural fluid noninvasively. Fundamental questions regarding the predictability of chaotic systems and the impact of adapting models and observations dynamically on the predictive capabilities will be explored. To design an effective Planet-in-a-Bottle DDDAS requires a substantial investment in advanced computing technology, because a naive simulation on regular meshes take far longer than the real-time evolution of the fluid. To enhance performance, the research project will devise novel algorithms and software that exploit low-cost clusters of commodity processors. Specifically, the researchers will investigate memory layout strategies for irregular meshes based on the theory of decomposition trees, which will lead to algorithms that can effectively exploit both memory hierarchy and parallelism. To investigate how the programming of such complex algorithms can be simplified, they shall investigate and develop a distributed transactional memory library to integrate sharing and synchronization in a cluster programming environment. Developing these computer-science technologies will require substantial technical expertise in the areas of algorithms and computer systems.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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