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Engy-5310 Computational Continuum Transport Phenomena

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The goal of this course is to present an unified treatment of continuum transport phenomena using computational finite element methods. Therefore serving as a foundation for simulation of coupled fluid flow (multiphase), thermo-mechanics, heat transfer, radiation, reactive mass transfer and electromagnetics. The computational framework used is open source and based on MOOSE: Multiphysics Object-Oriented Simulation Environment. Time-dependent, three-dimensional models will be the end-target of this course, therefore necessary topics in parallel computing will be covered. Basic knowledge of transport phenomena is required in addition to computer programming skills of one or more object oriented languages (e.g. C++ or/and Python).

This three-credit lecture course over fifteen weeks, consists of Jupyter notebooks used for lectures (see notebooks/).

Feedback and collaboration to improve this course are welcome through GitHub pull requests and issues or direct email.

This course uses Jupyter Notebooks in Python programming language. The content can be accessed in the following ways:

  • Static HTML version of the notebooks will be displayed on the current browser if a notebook file listed in the code repository, notebook/ is clicked on. This will not allow for rendering mathematical formulae. Alternatively you can render the notebooks on NBViewer by clicking on the NBViewer badge above.
  • Click on the launch/binder badge above to launch a Jupyter Notebook server for the course notebooks. There will be a delay for the Binder cloud server to build a Python (Anaconda) programming environment for you. However once it is done, it will start a Jupyter Notebook server on your web browser with all notebooks listed. Upon clicking on individual notebook files, you will access the live course notebooks.
  • Use the green Code button above on the right upper side of the page and either download the repository using GitHub Desktop or download a ZIP archive to your local machine. Unzip the archive. Then use your own Jupyter Notebook server (see Syllabus or Introduction notebooks on how to install Anaconda) to navigate to the directory created by the unzip or GitHub Desktop operation and upload the notebook files. In the case of a ZIP download, the files will not be updated on your local machine and you will need to return to the repository for getting new files or updated versions of previously downloaded files as the course progresses. If you use GitHub Desktop, the repository will be in sync.

Students will profit from either taking or self-studying a companion course that explains many of the computational aspects of using Jupyter notebooks, Python language programming, and methods in computational engineering.

Thanks in advance for inputs to improve this course.
Regards,
Prof. Valmor F. de Almeida

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