Transformative Advances in DDDAS with Application to Space Weather Monitoring.

摘要

This project focused on DDDAS-motivated developments in support of space weather monitoring and prediction. The project involved four interrelated tasks relating to physics-driven adaptive modeling, adaptive data assimilation with input reconstruction, event-based sensor reconfiguration, and optimization of scheduling. For data assimilation, the emphasis has been on model refinement. The problem of estimating the eddy diffusion coefficient using total electron content measurements has led to new techniques for determining the essential modeling details needed by the retrospective cost model refinement technique. For spacecraft design, multidisciplinary optimization design techniques were applied to the design of small satellites accounting for multiple vehicle subsystems. For download scheduling, optimization techniques were used to account for multiple spacecraft and ground stations.