Pinned Grain Growth Progress Report IV

Posted by Fred Hohman, David Montes de Oca Zapiain on November 23, 2015

Outline

  • Meeting with domain experts outcome
  • Image segmentation revisited

  • Chord length distribution expanded

  • PCA input plots
  • PCA output plots
  • Preliminary regression results
  • What’s next?

Outcome from Meeting with Domain Experts

  • Meeting with a computational scientist in the area of multiscale modeling @ Sandia National Lab.

  • Extremely useful meeting after which we added 2 more classes to the current simulation pool we had before.

Images of New Classes Added

Rolling:

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Images of New Classes Added

Random Distribution with Random Shapes:

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Obtaining Grain Boundary and Precipitates Revisited

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Obtaining Grain Boundary and Precipitates Revisited

  • Image of old image segmentation algorithm

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Obtaining Grain Boundary and Precipitates Revisited

  • Image of new image segmentation algorithm

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Chord Length Distribution Extraction Expanded

  • First decided to not use the average Chord Length distribution.
  • We normalized the distribution such that the area under the curve is equal to 1.
  • At this point this chord length tells us the probability of finding a chord of length “X” within our MS, nevertheless it is not enough.
  • We needed to add something that accounts, “weighs” more the chords of bigger size.

Chord Length Distribution Extraction Expanded

  • Thus by multiplying the frequency of each chord by its size and normalizing it again, the bigger chords now have a “bigger weight” in the distribution.

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PCA Input

  • PACE job using 64GB of memory
  • Data matrix size (n_simulations,27000000) where n_simulations=220
  • 220 is the total number of simulations we were able to perform during semester

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PCA Input Screen Plot

  • More than 95% variance captured in first 5 PC values

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PCA Output

  • Chord length distributions (in general) cleaned up after new segmentation code
  • Data matrix size (n_simulations, 299) where n_simulations=220

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PCA Output Screen Plot

  • More than 95% variance captured in first 3 PC values

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Regression Test

  • We now have two .m files containing our PC values for both the inputs and outputs of our model
  • Used “hacked” Scikit-Learn/PyMKS module to perform linear regression on our PC values
  • Objective: predict chord length distribution given a new precipitate distribution
  • We can calculate the R-square value for a give combination of polynomial degree and number of PC values used
    • Can create plot showing all combinations of a given set of values in degree and n_components

(Thanks David Brough!)

Regression Test

  • Using all simulations (220)

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Regression Test Results

  • Order of Polynomial: 2
  • Number of Components: 3
  • R-squared Value: 0.708516065498

Next Steps

  1. Further analysis on selecting input and output of our model
  2. Improve process-structure linkage