An Intelligent Agent Architecture for Concurrent CFD Feature Extraction

CFD simulations are advancing to correctly simulate highly complex uid ow problems that can require weeks on expensive computing clusters. These simulations can generate terabytes of data and pose a severe challenge to a researcher analyzing the data. Presented here is a solution to drastically reduce researcher post-processing time by extracting uid ow features concurrent with a running CFD simulation using intelligent software agents. The software agents are designed to work inside the CAF E concept and operate e ciently on high performance computing clusters. Three types of agents are given and their belief tuples de ned. A simulation of a bluntn is run showing convergence of the horseshoe n line to its nal spatial location at 540 iterations, or 60% of solution convergence. The agent architecture correctly selects between two vortex feature extraction algorithms and correctly identi es the expected probabilities of core lines throughout solution convergence.