By Amory Wakefield and Steve Miller, MathWorks
Monte Carlo analysis is a standard method of simulating variability that occurs in real physical parameters. In aerospace applications, Monte Carlo techniques can be used to ensure high-quality and robust designs. Even with a shared commercial-off-the-shelf (COTS) environment, fully testing or optimizing a design can take thousands of simulation iterations and days to complete. Depending on the complexity of the system and fidelity of the model, each iteration could take hours to run. Simulation time can become a critical bottleneck in the development process. Being able to run multiple, independent scenarios in parallel can lessen this time significantly. In this paper, we discuss techniques for modeling, optimizing, and testing plant models to build better system models in MATLAB® and Simulink®. We also present new methods for speeding up Monte Carlo techniques by using high-performance computing clusters.
This paper was presented at the AIAA Modeling and Simulation Technologies Conference.