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Siddharth Manay Chandrika Kamath Center for Applied Scientific Computing 2 March 2005PowerPoint Presentation

Siddharth Manay Chandrika Kamath Center for Applied Scientific Computing 2 March 2005

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### Siddharth ManayChandrika KamathCenter for Applied Scientific Computing2 March 2005

Progress Report on Data Analysis Work at LLNL: Aug’04 - Feb’05

UCRL-PRES-209947-DRAFT This work was performed under the auspices of the U.S. Department of Energy by University of California Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48.

http://www.llnl.gov/casc/sapphire/

Our progress on earlier applications

- Feature selection for EHOs (data from DIII-D)
- IDL code + instructions transferred to [email protected]
- visit to GAT + talk
- interest in licensing Sapphire software
- sample scenario ready for the web

- Separation of signals in climate data
- a standalone C++ code available which uses our libraries for PCA/ICA
- to be used in illustrating creation of workflows
- sample scenario ready for the web

Work done by Erick Cantu-Paz, Imola K. Fodor, Abel Gezahegne, Nu Ai Tang

New application: tracking in NSTX data

- Joint work with PPPL (Klasky)
- Problem: track the plasma over time
- IDL code implementing a variant of block matching is too slow
- Prototyping other block-matching approaches

National Spherical

Torus Experiment

Leveraging LDRD funding (CK); work done by Erick Cantu-Paz, Cyrus Harrison

New application: classification of puncture (Poincaré) plots for NCSX

- Joint work with PPPL (Klasky, Pomphrey, Monticello)
- Classify each of the nodes: quasiperiodic, islands, separatrix
- Connections between the nodes
- Want accurate and robust classification, valid when few points in each node

National Compact

Stellarator Experiment

Quasiperiodic

Islands

Separatrix

Polar Coordinates Plots

- Transform the (x,y) data to Polar coordinates (r,).
- Advantages of polar coordinates:
- Radial exaggeration reveals some features that are hard to see otherwise.
- Automatically restricts analysis to radial band with data, ignoring inside and outside.
- Easy to handle rotational invariance.

Piecewise Polynomial Fitting: Dividing data into intervals. Plots

- Use the q-histograms to find intervals.
- Need to divide the q domain into intervals that are:
- Restricted to regions of q that have data.
- Small enough so that polynomial will fit the data.
- Large enough to span gaps where data is missing

Piecewise Polynomial Fitting: Computing polynomials Plots

- In each interval, compute the polynomial coefficients to fit 1 polynomial to the data.
- If the error is high, split the data into an upper and lower group. Fit 2 polynomials to the data, one to each group.

Blue: data.Red: polynomials. Black: interval boundaries.

Classification Plots

- The number of polynomials needed to fit the data and the number of gaps gives the information needed to classify the node:

2 Polynomials

2 Gaps

Islands

2 Polynomials

0 Gaps

Separatrix

Results Plots

3995 points, Separatrix

250 points, 3 Islands

Puncture 1, node 79

Zoom around =1.6

Zoom around =1.6

Future work Plots

- Set up web pages for climate and fusion scenarios
- NSTX data: continue building and testing block-matching algorithms
- NCSX data
- continue interactions with Neil, Don, Scott
- continue to refine and validate approach
- investigate ways of making it more robust
- investigate exploiting nearby nodes
- design and implement in C++ for insertion into PPPL analysis pipeline

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