Fast E2E simulation tools and calibration strategies for EAGLE-MOAO on the E-ELT

Manal Chebbo AO4ELT3 30 May 2013 1

Manal Chebbo, Alastair Basden, Richard Myers, Nazim Bharmal, Tim Morris, Thierry Fusco, Jean-Francois Sauvage

Fast E2E simulation tools and calibration strategies for EAGLE-MOAO on the E-ELT


DASP VS E2E-SDurham Ao Simulation Platform VS E2E- Sparse (LAM / ONERA)

Conventional MVM dense Sparse methods utilized.

Atmosphere to Slopes slow but accurate model.

Typically needs to run for several hours to simulate 10 – 100 s of operational time, but with high fidelity…

Sparse methods only.

Atmosphere to Slopes fast but approximated model.

Speed comparable to analytical codes, but capabilities of E2E codes, for ex: Optical tolerance studies…

Basden et al 2007Chebbo et al 2011

AO4ELT2

Manal Chebbo AO4ELT3 30 May 2013

Synopsis

3

1. Sparse E2E Simulator2. DASP Simulator3. Conclusion and Perspective


Synopsis

4

1. Sparse E2E Simulator

2.DASP Sim

ulator

3. Conclusion and Perspective


Sparse E2E Simulator

AO in blocks

Configuration system

Turbulent generator

WFS model

Tomographic reconstructor

DM model


Sparse E2E Simulator Description of the different modules

Configuration System



Turbulence generator

Generate layers in the form of pixilated phase screen

Simulation and computation of white noise FT Inverse FT → real part d/r0

Wind speed, direction for the considered layer



Wave Front Sensor Sparse-Model

➜ Realistic sparse Geometry Model of Shack- Hartman WFS

Illuminated and partly illuminated subapertures are managed

400 subapertures / 308 are fully or partly illuminated in the pupil

• RCO/RCO_d FORMAT : (Representation Complete and Ordered)

Ds = {rco} Ds.r = 2* Ns2

Ds.c = N2

Ds.n = 4* nb_pix* Ns2

Ds.ix = ptr_new([0,make_array(D.r, /long)]) Ds.jx = ptr_new(lonarr(D.n + bandwidth)) Ds.xn = ptr_new(make_array(D.n+ bandwidth, /float))



Sparse Wave Front Reconstruction

Sparse noise covariance matrix Cn

Noise are not correlated noise is uniform →

Turbulence covariance matrix



Deformable Mirror sparse-Model

➜ DM with sparse influence functions

Ifs profile ~ double Gaussian IF = 0 beyond Nacinf

Fs → RCO → [ N2 , Nact-valid]

64*64 actuators , Nacinf= 4 Nacinf= 6

30% of mechanical coupling for minimizing the fitting error.


Sparse E2E Simulator Challenges of AO with Tomography

Tip, Tilt and Defocus Indetermination

➜ Goal: Generate realistic WFS measurement’s with filtered out TTF

We modify the slopes to remove any tip tilt or defocus

S

TTF



Tip, Tilt and Defocus Indetermination

We search the transformation matrix M to an orthogonal space which exludes TTF

2 methods present : ? Setting mean slopes to zero.

? M contain all the modes generated by the AO system.



Tip, Tilt and Defocus IndeterminationM

ethod-1

TT is reduced to 7% Defocus is not filtered

Subtraction of average slopes


Challenges of AO with Tomography Tip, Tilt and Defocus Indetermination

M contains the WFS answer to all the modes generated by the system

Method - 2

Karhunen–Loève

TTF are filtered out


Synopsis

15

2. DASP Simulator

3.Conclusion and Perspective

1.Sparse E2E Sim

ulator


DASP VS E2E-S

Conventional MVM denseSparse methods utilized.

Atmosphere to Slopes slow but accurate model.

No, Simple only TT

Sparse methods only.

Atmosphere to Slopes fast but approximated model

TTF filtered out.

Basden et al 2007 Chebbo et al 2011 AO4ELT2


DASP

● Durham AO Simulation Platform● Python and C● MPI, shared memory, and multi-threaded● Suitable for ELT-scale problems● Cross checked with other codes: Octopus (42

m), YAO (4.2 m), Fourier (ONERA) (42 m)...


DASP EAGLE E2E DASP-simulations

Simulation Parameter:

42 m, central obscuration = 6 m

R0 = 13.5 cm @ 500 nm.

L0= 30 m

SH With 84*84

LGS closed loop GLAO with M4 and the same with open loop correction on the MOAO DM

40 s of telescope time (10000 iterations)

No NGS , 6 LGS in a regular hexagon



H-band Ensquared energy in 75mas over the field of view. 6 LGS at 7.3arcmin diameter 6 LGS, 7.3 arcminute diameter

Strehl Map


Actuators required

● By making use of global M4 GLAO correction

– Can reduce requirements for MOAO DMs

– MOAO can be relaxed somewhat without dramatically affecting AO performance.

● To ones that can be bought today!



● Steep fall with rotation● 0.2 degrees

● ~1/8th subap at edges

21

Alignment tolerance

● Lateral misalignment reduces performance– Up to 1/8th of a sub-aperture has small

effect


Rotational tolerance


Conclusion:

Current Project is to produce a new validated rapid E2E simulation which combines the speed advantages of analytic codes with the ease performing engineering tolerance analysis, for example: Rotational and misalignment

Future Work:

Cross check the codes New user interface for E2E-S code Aim to produce a new form of systems engineering for ELT scale AO problems.


Thank You


Comparison with other codes

Fast E2E simulation tools and calibration strategies for EAGLE-MOAO on the E-ELT

Documents

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