
Analysis, Modeling, and Simulation of Adaptive Optics Systems for Extremely Large TelescopesProject Status1. Modeling and Analysis  The problem of designing adaptive optics system for extremely large telescopes can be considered one of identifying and quantifying the sources of wavefront correction error. The result of such a process forms the basis of a system error budget. Over the past year there have been a series of workshops and meetings where we have listed the known error sources and discussed their possible solutions. The collaborative research activity has made progress in modeling the effects and how design parameters and atmospheric conditions affect them. Here is a list of error sources, with links to research activity related to them:
2. Simulations  In addition to the error budget modeling work there is a parallel effort to outright model pointdesign MCAO systems on 30 meter class telescopes. Brent Ellerbroek is using an existing code he developed for the Gemini South MCAO system and has generated some preliminary performance predictions. Effort continues on the Arroyo AO modeling and simulation code. Matthew Britton gave presentations describing the current status and validation tests on the atmospheric propagation components. Rapid turn around of ELTsized simulations requires that the codes be capable of running on massively parallel supercomputers. Current "serial" coding requires 10^6 seconds (11 days) of compute time to simulate one second of realtime. With a 1000 parallel processors, this could be potentially reduced to 10^3 (17 minutes) of compute time. Jose Milovich has ported the Keck AO simulator and also run some 30meter aperture test cases to show dramatic speedup on a massively parallel processing architecture. This code will be combined with Arroyo over the next few months. Aron Ahmadia has studied ways of further parallelizing MCAO simulations to take advantage of thousands of processors. 3. Fast Reconstructor Algorithms  Significant progress has been made on algorithms for wavefront reconstruction, i.e. how one determines the commands to the multiple DMs' actuators given the measurements from the multiple guidestar wavefront sensors. The challenge is to keep these realtime computations tenable. A "standard" leastsquares solution takes on the order of n^2 operations, which, for ELT sized AO systems goes beyond state of the art for present day computers. Algorithms have now been developed that have close to order n operations. These are the hierarchical decomposition methods, iterative sparse matrix methods, and FFT methods, along with hybrids (like the FFTpreconditionediterativehierarchical sparse method). 4. Closed Loop Control  Given knowledge of the statistics of the seeing and the measurement noise, it is possible to derive a controller that gives the minimum wavefront error, resulting in maximum AOcorrected Strehl. Such a statistical optimum is known as a KramerRao lower bound and represents the best possible mean performance. We can quantitatively compare the performance of any other (possibly simpler to implement) controller against this baseline. The control law that results from statistical considerations is often called a "regularized" controller. Brent Ellerbroek has developed ideas for rapid calculation of an approximate regularized closedloop controller.  
Last Modified: Apr 14, 2003 
You are here:
Research Projects > ELT Analysis  In this section:
About  Project Status  5Year Roadmap  Publications
Center for Adaptive Optics 
Search 
Sitemap 
The Center 
Adaptive Optics 
Research 
Education/HR
Members 
Calendar of Events 
Publications 
Software 
Employment 
Picture Gallery 
Links 
What's New