S imulation is an ideal way to predict behavior in complex systems that we cannot otherwise test. For example, imagine trying to predict the behavior of a weapons system or a set of flood warning sensors by testing the real thing. Simulation offers definite advantages-safety and controlled circumstances, for instance-over the limited testing we could perform with an actual wartime battle or a real flood. The behavioral complexity that such large-scale modern systems can exhibit, however, demands computing power that exceeds current desktop technology. To meet such a challenge, we need high-resolution, large-scale representations of systems composed of both naturally occurring and manmade elements.In this article we describe a high-performance simulation environment we developed to model complex systems. We also discuss results we obtained by modeling and simulating a watershed. Environment overviewThe simulation environment we developed is called DEVS-C++. It is based on a modeling formalism called DEVS, which stands for Discrete-Event System Specification, as we will explain.We can characterize a high-performance, simulationbased design environment with two levels of intensive information processing:♦ At the decision-making level, the environment searches many alternative designs and associated models.♦ At the execution level, simulations generate and evaluate complex candidate model behaviors, possibly interacting with human participants in real time.Our simulation environment has four layers: simulation (lowest), modeling, optimization, and decision making (highest). Each builds on lower layers to implement its functionality.As Figure 1 shows, processes execute concurrently in a heterogeneous, distributed computing environment. 1 We use genetic algorithms in the optimization layer to search through a model space to find models that will effectively predict system behavior.The functions of a GA are distributed among software components called agents. Each GA agent has access to a simulator for executing experiments. Experiments consist of several trials, testing how well a particular structure works in a problem environment. We represent the environment as a simulation model, controlled and ♦ ♦ ♦ DEVS-C++, a high-performance environment for modeling large-scale systems at high resolution, uses a discrete-event formalism called DEVS to represent both continuous and discrete processes. A prototype suggests that the DEVS formalism can combine with genetic algorithms running in parallel to serve as the basis of a very general, very fast class of simulation environments.♦ .

Recent developments in the use of GIs for spatial dynamic modeling has resulted in improved fire growth simulations. This paper examines previous growth models and some of their weaknesses. We then define what would be required to handle the growth of surface fire within a raster based GIS program. The paper discusses the requirements for the algorithm needed to model fire growth and how this algorithm is implemented using the PROMAP GIS modeling extensions. Examples are given of fire growth simulation using predefined conditions. The need for further testing of actual fires is discussed.

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Four composite material systems useful for roofing in the tropics were developed which utilize major percentages of bagasse (sugar cane residue) filler, and minor amounts of phenolic or other resin binders. The various materials were actually manufactured in small quantities in three developing countries and trial exposure roofs installed.