Networked agents for scientific computing

The thesis of this article is that agent based computing provides important advantages for scientific computing. We present this thesis in the context of a particular application, the simulation of gas turbine engines. This application is typical in that it involves an enormously complex device of great economic importance, one whose design is continually evolving to achieve higher value and to fit new uses. Ideally, a designer would change some aspect of the engine and then run a simulation to see how the performance, cost, durability, etc., change. Such a simple approach will be infeasible for the foreseeable future because the complete simulation of an engine design requires days, weeks or even years on petaflops class computing systems. Thus the design process and simulation software must be configurable so that a simulation can focus on particular aspects of the engine. For example, in designing a new turbine blade (they do not all have the same shape) one might (a) model the blade itself very accurately, (b) model the air flow field and structure near the blade with moderate accuracy, (c) model the air flow fields and structures further away roughly, and