Pinned Grain Growth Progress Report Presentation II

Fred Hohman, David Montes de Oca Zapiain

12 00 AM ,Mon, Oct 05 2015

Outline

  • Detailed Workflow and Data Pipeline
  • Conditioning and Segmentation in Matlab (Step 1)
  • 2 Point Statistics and PCA in Python (Steps 2, 3)
  • Preliminary Results

Matlab

Picture 1

Image Segmentation

  • Output from SPPARKS is a 27 million by one vector, reshape it to a 300X300X300 matrix.
  • Each number corresponds to a grainID. Max value corresponds to a precipitate.
  • We need to segment this data into our Microstructure Function.

Picture 2

Picture 3

Obtaining Grain Boundary and Precipitates

Picture 4

Obtaining .mat files using PACE

  • The issue is that the output from the filter are 3 images with 0 and 1.
  • Which is desirable but we need it to change to one image with 3 distinct phases for us to input it to PyMKS.
  • Made a for loop that will go through each .dumpfile and automatically segment it and save it as .mat file.

Python

Continuing where we left off in Matlab…

  • Input: .mat files that only contain 0, 1, and 2 as values in an 300x300x300 array
    • Greatly reduces memory:
    • file.dump (~500MB) -> file.mat (~3MB)

2 Point Statistics Conditioning

  • Initial size: (300, 300, 300) per file
  • Loop that pulls each 100 outputs per simulation
    • Concatenates to feed directly into PyMKS
    • (n_samples, x, y, z) = (100, 300, 300, 300) per simulation

2 Point Statistics Computation

  • 3 phase material -> 6 correlations
  • Results in array of size (100, 300, 300, 300, 6)
    • Initially only care about one correlation: grain boundary and pins [1,2]
  • After 2 point statistics computation: (100, 300, 300, 300, 1)

2 Point Statistics Plots

Shows 2 point statistics evolve over grain growth simulation

Note: Visualization issues:

  1. Low volume fraction
  2. Color bar not scaled correctly by default

Picture 4

Picture 4

Picture 4

PCA

  • Forming the Data Matrix
    • Consider each 3D microstructure as long array
    • Reshape (100, 300, 300, 300) to (100, 300^3) = (100, 27,000,000)
  • SciKit Learn Randomized PCA
    • Can specify number of PC values to output
    • Have tried: 2 and 3
      • Results in (100, 2) and (100, 3) matrix

PCA Plots

  • Final .mat files currently finishing on PACE
  • Code pipeline is working but needs polishing
  • Meaningless 2D and 3D example below

Picture 4

Picture 4

Next Steps

Bring analysis and visualization into 3D.

  1. 2 point statistics of 3D data (longer computation)
  2. Visualize 3D 2 point statistics results
  3. Compute 3D PCA and Plot to see a path per simulation
  4. Work with David Brough to use latest fitting code to create model

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