Welcome to SG2212/SG3114
An in-depth course on numerical methods for computer simulation of fluid flows. Together with SG2224 "Applied Computational Fluid Dynamics" (5 ECTS), a comprehensive course on theory and practice of computational fluid dynamics. |
Information for the re-exam June (omtentaperiod 3):
We plan the combined oral/written exams on June 7. A list where you can sign up will be sent to all registered students.
Note that you need to register for the re-exam in the period 8–22 of May.
Literature |
Handouts |
Assignments |
Recorded lectures |
Teachers
- Lectures:
Philipp Schlatter (pschlatt@mech.kth.se) 790 7176, office hours: Fri 14-15
Ardeshir Hanifi (hanifi@kth.se) 790 8482, office hours: Fri 14-15 - Course Assistants
Fermin Mallor (mallor@kth.se), office hours: Mon 14-17
Adalberto Perez Martinez (adperez@kth.se), office hours: Mon 14-17
Course Information
- There is a pdf with all relevant information for the course: course_plan_2023.pdf Download course_plan_2023.pdf
- Philipp's lectures will be - due to medical reasons - on Zoom: https://kth-se.zoom.us/j/69100960780 Links to an external site.
Course Registration
- All students need to be registered for the course (both Master's and PhD students); the registration period is early January 2023. If the registration is not possible online, please send an email to student@mech.kth.se. More information can be found at the Studentexpedition TR8.
Exam
- The exam will be an oral exam. Further information will be published later. It is a closed-book exam, that means no books, notes or calculators can be used. The questions are similar to the ones gives in the study questions, see handouts page. Plussning will be allowed, and the form will be opened in due time.
Re-Exam
- Will be similar as the ordinary exam.
Project theses
- If you are interested in continuing to study CFD e.g. in form of a Project Thesis (SG2010, 15 ECTS) or a Master's thesis (SG213X, 30 ECTS) you can always contact any of the teachers. A list of possible thesis projects for topics geared towards turbulence and machine learning is given here (KTH login required).