Treffer: Jupyter Notebooks for the study of advanced topics in Fluid Mechanics.

Title:
Jupyter Notebooks for the study of advanced topics in Fluid Mechanics.
Authors:
Castilla, Robert1 (AUTHOR), Peña, Marta2 (AUTHOR) marta.penya@upc.edu
Source:
Computer Applications in Engineering Education. Jul2023, Vol. 31 Issue 4, p1001-1013. 13p.
Database:
Education Research Complete

Weitere Informationen

In recent years, Jupyter Notebooks have become a very useful free and open‐source tool in teaching, as they allow you to combine text, images, mathematical expressions, links and code into a single document. This gives students an interactive document with which they can experiment and learn with the help of high‐level mathematical calculus. In Fluid Mechanics, it is very common for students to deal with complex computations that take away attention from the Mechanic, especially in advanced topics such as Rheology, Turbulence, or Boundary Layer. The subject "Advanced Fluid Mechanics" is an elective one of the last year of the Bachelor's degree in Industrial and Aerospace Technology Engineering at the Terrassa School of Industrial, Aerospace and Audiovisual Engineering at the Universitat Politècnica de Catalunya. This subject has three ECTS credits and has been taught since the academic year 2020–2021 This subject complements the compulsory subject Fluid Mechanics and is developed in 6 weeks with 5 h of class each week. This work presents Fluid Mechanics modules with Jupyter Notebooks that complement the syllabus given in the compulsory subject. An elective subject is presented where subjects of Fluid Mechanics per week are studied independently, using different Python tools: symbolic calculation, modeling of experimental data, statistical analysis, numerical calculation, and so forth. The main goal is for the student to focus on mechanical concepts and actively learn to use the tools available, especially open source, to do the associated mathematical calculations. [ABSTRACT FROM AUTHOR]

Copyright of Computer Applications in Engineering Education is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)