First Presentation Post (For Comments)

Posted by Almambet Iskakov, Robert Pienta on September 14, 2015

First Presentation Post (For Comments)


Almambet Iskakov, Robert Pienta

12 00 AM ,Mon, Sep 14 2015

Background

  • Directional Solidification of Al, Ag, Cu Eutectic Alloy
  • Control structure during solidification process
  • Align phases/structures parallel to growth direction
  • Achieve desired material properties

2D-DS

3D-DS

Phase-Field Simulation: Solidification

  • Solidification Model:
    • Simulate growth of phases
    • Thermodynamic model
    • Concentration model
    • Three phase metal
    • Varied Parameters: Temperature Gradient and Gradient Velocity

Boundary-Conditions

Directional Solidification Visual

simulation-slices

lamellae

Phases evolve parallel to growth direction.

Binary Phase Diagram

Binary Phases

  • Eutectic mixture:
    • Liquid transforms into two different phases
    • Lowest melting/freezing temperature

ternary-diagram

  • In this Al, Ag, Cu alloy, the eutectic ratios by mole fraction at 773.6K:
    • 18% Ag (25% experimental)
    • 69% Al (62% experimental)
    • 13% Cu (14% experimental)

Simulated Microtstructure

colored-phases

  • A cross-section of solidified material close to solidification front
  • Most continuous phase: Al
  • Chained brick-like structure: Al2Cu and Ag2Al

Common Microstructure Patterns

ms-patterns

The Data

  • More than 20 simulations
    • Resolution from 200x200 to over 2000x2000
    • Simulated with varied solidification velocities
    • Varied volume-fractions of Al, Ag2Al, and Al2Cu
    • Plenty of measurements!
      • An 800x800x4256 sample has 2.72 billion data points
        • This simulation took 16 hours on 13700 cores!

Simulation in action

Al = Green, Ag2Al = Orange, and Al2Cu = Blue

Extracting Features

We have already started generating spatial statistics (correlations) for our data

Correlations

0 = Al, 1 = Ag2Al, and 3 = Al2Cu

Distance-varying Distributions

  • The simulated microstructures vary through the course of each simulation
  • We can sample the approximate steady-state, solidified structure at various times,
    • using the difference between successive spatial statistics
    • or just using one of the last time/height steps (assuming that the simulation was run until a steady state was reached)
  • We may later consider the changes in spatial statistics over time to help mine the Process-Structure linkages

Computational Plans

Next Steps

  • Use dimensionality reduction (DR) over our large space of spatial statistics:
    • Conventional PCA
    • Newer low-rank approximation DR techniques
    • (possibly) Attempt to use locality sensitive hashing
  • Model the relationship between our simulated solidification processes
    • linear model (regression)
      • interpretable
      • simple model, unlikely that Eutectoid Al has linear structural relationships
    • nonlinear model (kernel methods)
      • less interpretable
      • complex model, can model complex relationships

Questions & Comments

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