• University of Massachusetts Lowell, Fall 2024
• Dept. of Chemical Engineering (Nuclear Program)
• Prof. Valmor F. de Almeida ([email protected])

The goal of this course is to offer students an opportunity to exercise concepts learned in previous courses in the form of a project. Groups of two (or three) students will select a topic for design and analysis of nuclear systems. Past projects are kept here as an example for possibilities of future projects.

Although the project topics are flexible, the past ones have close ties to balance of plant and to the content taught in a related course Engy-4350.

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 is clicked on. This will not allow for rendering mathematical formulae. Alternatively you can render the notebooks on NBViewer by clicking on the render|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 download button above on the right upper side of the page and download a ZIP archive to your local machine. Unzip the archive. Then use your own Jupyter Notebook server to navigate to the directory created by the unzip operation and upload the notebook files. In this case the files will not be updated and you will need to return to the repository for getting new files or updated versions of previously downloaded files.

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