A Distributed Computational System for Large Scale Environmental Modeling

Carnegie Mellon University and Massachusetts Institute of Technology

Impact of the Project

Study of Alternative Fuels

The picture above shows output from a study currently underway to determine the effects of the wide-spread use of alternatives fuels in southern California. The system shows ozone concentrations over Los Angeles for two different scenarios. The first panel shows the predicted concentrations of ozone over the region if automobiles used compressed natural gas (CNG) fuel instead of standard gasoline. The second panel shows the same concentrations without any changes in the vehicle fleet. The third panel shows the difference between the first two panels. This type of visualization allows us to rapidly compare the effects different fuel technologies would have on air quality.

This goal of this study is to examine the effects of 6 different alternative fuels in each one of 12 different scenarios. This study will require a total of over 1000 CPU hours (45 CPU days) to perform all the computations. Our system allows us to perform these runs in parallel on a series of high-performance workstations so that all the calculations necessary to complete the study can be done in a week, rather than a month and a half. This drastic increase in turn-around time allows us much greater flexibility in choosing different scenarioes to examine, and allows us to have a much greater impact on policy formulation.

Other Recent Accomplishments

More information about the project can be obtained from Dr. Armistead Russell ( russell@pollution.me.cmu.edu ).

Comments on or problems with the project WWW pages should be directed to Erik Riedel ( riedel@cmu.edu ).

Project Overview

 The Grand Challenge - Environmental Modeling project is a joint project between the Department of Mechanical Engineering and the School of Computer Science at Carnegie Mellon University and the Department of Chemical Engineering at the Massachusetts Institute of Technology. Funding for this project is provided by the National Science Foundation under the Grand Challenge Program in High Performance Computing and Communications.

Objective

 In the past, the only way to determine the efficacy of solutions to various environmental problems was to implement a set of control strategies and measure the results. When looking at air quality, this approach had to make use of the atmosphere as the best and only laboratory for experimentation. The development of computational models of the physical and chemical processes that take place in the atmosphere allows experimentation on different control strategies without the extraordinary expense and difficulty of a "real-world" implementation.

Developing cost-effective solutions to pressing environmental problems such as air pollution requires mathematical models that can describe the physical and chemical processes occurring in the atmosphere. These models are very demanding of computational resources, hindering the development and use of the most scientifically advanced methods. Fortunately, development of parallel and distributed algorithms for such models' computational and data storage requirements is a viable goal. This effort is designed to provide significant advances in parallel/distributed software environments and computational algorithms for environmental modeling.

Approach

 Our project is attacking the problem on four main fronts, by developing:

Principal Investigators

Associated Projects

Technology Transfer

Team Members

Publications and Presentations
CMU/HPCC/Version 2.01/16-Jan-95/er1p