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pydiffusion: A Python Library for Diffusion Simulation and Data Analysis Cover

pydiffusion: A Python Library for Diffusion Simulation and Data Analysis

Open Access
|Apr 2019

Figures & Tables

Figure 1

General architecture of the pydiffusion package.

Figure 2

Configuration nearby an interface on simulation grids. The dash line denotes the interface at location xIf. The height of each dot represents the concentration value (C) at each grid and the interface. Connecting those dots, yielding the solid curve, is the concentration profile during simulation. The arrows represent the mass flux J between adjacent grids.

Figure 3

(a) Comparison of the input interdiffusion coefficients (lines) for the Ni-Mo system to mphSim() with those obtained by applying the Sauer-Freise method to the simulated profile (crosses); (b) Comparison between simulated diffusion profile (line) and experimental profile (open circles) of a Ni/Mo diffusion couple after being annealed at 1100ºC for 800 h.

DOI: https://doi.org/10.5334/jors.255 | Journal eISSN: 2049-9647
Language: English
Submitted on: Dec 4, 2018
Accepted on: Apr 2, 2019
Published on: Apr 23, 2019
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Zhangqi Chen, Qiaofu Zhang, Ji-Cheng Zhao, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.