upxo.algorithms.alg224 module

upxo.algorithms.alg224.mc_iterations_3d_alg224(self)

DESIGNED TO ACHIEVE: Bi-modal grain size distribution

Each of the initial set of iterations is to contain the following

STEP 1: Do the regular iteration using any of the 200 series of algorithms

STEP 2: Identify grains and their neighbours

STEP 3: Calculate state partitioned grain area distribution

STEP 4: Identify small grains with areas less than P % of mean area for each state

STEP 5: Select the state with the largest mean area: S_large

STEP 6: Select the state with the smallest mean area: S_small

STEP 7: Prepare a merger list comprising of two columns. First column is to have the global grain IDs of certain grains belonging to S_small. The second column is to have a list of global grain IDs of neighbouring grains belonging to S_large. If for a grain of S_small, no neighbouring grains of S_large exit, then cancel the merger operation for the current S_small grain. Iterate through all the remaining grains.

STEP 8: Calculate the grain area distribution. Calculate the modality Calculate the shift in peaks.

STEP 9: If the peak shift is in the direction of target peak, then accept the present iteration using a iteration transition probability.