Postgraduate Course: Numerical Methods for Chemical Engineers (MSc) (PGEE11169)
Course Outline
| School | School of Engineering |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | The problems a chemical engineering faces today range from molecular simulations to computational fluid dynamics and reaction and process modelling. These areas span a wide range of time and length scales and sophisticated numerical methods are required to reach a solution. This course introduces computational and mathematical methods for the solution of multi-scale chemical engineering problems. |
| Course description |
Week - Lecture topic (Tutorial/Assignment)
1 Linear equation systems
Linear equation system solvers
2 Nondimensionalisation
Nonlinear algebraic equations
3 Nonlinear systems solvers (Nonlinear systems)
Parameter estimation
4 ODE introduction
Explicit ODE solvers
5 Stiff systems and DAEs (ODE)
Implicit methods 1
6 Optimisation introduction
Linear programming
7 Nonlinear optimisation 1 (Optimisation)
Nonlinear optimisation 2
8 Model vs Data: fitting
Uncertainty in data and models
9 Implicit methods 2
PDE introduction
10 Method of lines with FDM 1 (PDE)
Method of lines with FDM 2
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2022/23, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Seminar/Tutorial Hours 20,
Feedback/Feedforward Hours 3,
Formative Assessment Hours 5,
Revision Session Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
146 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written Exam: 0%
Coursework: 100%
Practical Exam: 0% |
| Feedback |
During the Q+A sessions after the lectures and during the workshops; feedback on the submitted coursework using details feedback and marking proforma. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand the principles of scientific computing with Python, including stability and computational effort;
- Understand the most important numerical methods and their working principles, scope and limitations;
- Choose, apply and implement the best numerical method to solve multi-scale chemical engineering problems;
- Check and interpret the solutions and use them to design and improve chemical engineering processes.
|
Reading List
Core reading list:
- Steven C. Chapra, Numerical methods for engineers, McGraw-Hill Education
- Svein Linge, Hans Petter Langtangen, Programming for Computations - Python, A Gentle Introduction to Numerical Simulations with Python 3.6, Springer International Publishing |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Numerical methods,computational methods,applied mathematics,chemical engineering |
Contacts
| Course organiser | Dr Daniel Friedrich
Tel: (0131 6)50 5662
Email: D.Friedrich@ed.ac.uk |
Course secretary | Mrs Shona Barnet
Tel: (0131 6)51 7715
Email: Shona.Barnet@ed.ac.uk |
|
|
University Homepage