Virtual telemetry has the advantage that it is inexpensive to produce from real time simulations and readily transmittable using (sadly, highly modified) open source streaming software. Real telemetry is usually expensive to receive (if it is even available long term), tends to be messy, comes in no particular order, and can be incomplete or erroneous due to transmission problems or sensor malfunction. We will generate multiple streams continuously for extended periods (e.g., months or years): clean data, somewhat error prone data, and quite lossy or inaccurate data. By studying all of the streams at once we will be able to devise DDDAS components useful in predictive contaminant modeling.
This is joint with a cast of infinity from two NSF ITR grants: Janice Coen, Martin Cole, Yalchin Efendiev, Richard Ewing, Greg Jones, Chris Johnson, Robert Kremens, Raytcho Lazarov, Jan Mandel, Chad Shannon, Jenny Simpson, Anthony Vodacek, and Wei Zhao