Important notice
The course guide is provisional.
The PDF version of the course guide may take a few days to become available in the DDD.
Computational Fluid Dynamics and Process Optimisation
Code: 43325Credits: 9
| Degree programme | Type | Course |
|---|---|---|
| Biological and Environmental Engineering | OB | 1 |
Contact lecturer
- Name :
- Oscar Guerrero Sodric
- Email :
- oscar.guerrero@uab.cat
Teaching staff
- Oscar Guerrero Sodric
Teaching staff (external to UAB)
- Martí Cortada García
Group languages
You can consult this information at the end of the document.
Prerequisites
Mass and energy balances.
Transport phenomena.
Chemical and biological kinetics.
Differential calculation. Ordinary differential equations. Differential equations with partial derivatives.
Numerical methods.
Programming languages. Matlab.
Basic skills in technical drawing using software AutoCAD-type
Objectives
The main objective is twofold, on the one hand the application with criteria of tools for modelling, simulation and optimisation of chemical, biotechnological and environmental processes and, on the other hand, to work on the bases of Computational Fluid Dynamics.
The specific objectives of the course are:
- Formulate mathematical models for different processes from non-stationary state balances and other additional equations.
- Numerically solve mathematical models with simulation programs and analyze the results.
- Use methods for univariate and multivariate mathematical optimization.
- Adjust mathematical models. Analyze the sensitivity of model parameters.
- Apply the basic notions of experimental design.
- Develop calculation programs, based on the fundamental principles of Transport Phenomena and the appropriate Numerical Methods.
- To solve problems of Transport Phenomena in such a way that the student can understand how they are structured and which are the principles of operation of the commercial CFD packages, mainly ANSYS
Learning outcomes
- CA09 (Integrate knowledge of kinetics, thermodynamics, transport phenomena and numerical methods to analyse, design, model and optimise different types of reactors and their operating strategies.) Integrate knowledge of kinetics, thermodynamics, transport phenomena and numerical methods to analyse, design, model and optimise different types of reactors and their operating strategies.
- CA10 (Formulate and solve univariate and multivariate mathematical and technical-economic optimisation problems.) Formulate and solve univariate and multivariate mathematical and technical-economic optimisation problems.
- KA06 (Recognise the structure and use of commercial Computational Fluid Dynamics (CFD) packages.) Recognise the structure and use of commercial Computational Fluid Dynamics (CFD) packages.
- KA07 (Define the basic notions of experimental design.) Define the basic notions of experimental design.
- SA06 (Deduce the learning skills required to continue one's training in a self-managed or autonomous manner.) Deduce the learning skills required to continue one's training in a self-managed or autonomous manner.
- SA07 (Build mathematical models of processes in a steady or non-steady state using suitable numerical methods to solve these models.) Build mathematical models of processes in a steady or non-steady state using suitable numerical methods to solve these models.
- SA08 (Deduce the equations of change of Transport Phenomena to solve engineering problems in order to establish the model for a system.) Deduce the equations of change of Transport Phenomena to solve engineering problems in order to establish the model for a system.
- SA09 (Use the most adequate IT instruments to complement knowledge in the field of biological engineering and environmental engineering.) Use the most adequate IT instruments to complement knowledge in the field of biological engineering and environmental engineering.
Contents
The subject is structured in two modules:
Modeling and optimization of processes
- Modelling of chemical, biological and environmental processes
- Simulation of processes with ordinary differential equations
- Simulation of processes with differential equations with contour conditions
- Simulation of processes with differential equations with partial derivatives
- Univariate, multivariate and constrained optimization methods
- Model fit: Parameter Determination and Sensitivity Analysis
- Design of experiments
Computational Fluid Dynamics
- Introduction
- Geometry and mesh
- The integrator
- The Visualizer
- Case Studies
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Study, search for information and resolution of the proposed cases. | 89 | 3.56 | |
| Approach to the resolution of proposed cases | 14 | 0.56 | |
| Theoretical and theoretical-practical classes | 56 | 2.24 |
The course will be developed in theory classes and theoretical-practical classes. In addition, during the course different proposed cases will have to be solved and presented, which will be carried out mainly outside the class schedule. It is necessary to bring your own laptop.
For this course, the use of Artificial Intelligence (AI) technologies is allowed exclusively in the Matlab learning process, but may not be used for the writing of the modeling work or during the exams. The student will have to clearly identify which parts have been generated with this technology, specify the tools used and include a critical reflection on how these have influenced the process and the final result of the activity. Non-transparency of the use of AI in this evaluable activity will be considered academic dishonesty and may result in a partial or total penalty in the grade of the activity, or higher penalties in serious cases.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| MOP. Problem delivery. | 7.5 | 10 | 0.4 | CA09, CA10, KA07, SA07, SA08, SA09 |
| MOP. Exam | 30 | 3 | 0.12 | CA09, CA10, SA07, SA08, SA09 |
| CFD. Case study resolution | 15 | 30 | 1.2 | KA06, SA06, SA08, SA09 |
| MOP. Work/s of modelling and simulation of real systems | 12.5 | 20 | 0.8 | CA09, CA10, SA06, SA07, SA08, SA09 |
| CFD. Exam | 35 | 3 | 0.12 | KA06, SA08, SA09 |
Evaluation
(a) Scheduled evaluation process and activities
The course is divided into two independent modules: 1) Process Modeling and Optimization (MOP) and 2) Computational Fluid Dynamics (CFD).
Below are the evaluation activities of each module of the subject with their percentage of weight on the final grade of the module:
Process Modeling and Optimization
- Activity 1 (15%, individual). Problem solving.
- Activity 2 (60%, indivdual). Partial exam with different problems.
- Activity 3 (25%, in group). Modelling work on real scientific papers.
Computational Fluid Dynamics
- Activity 1 (10%, individual). Assistance.
- Activity 2 (20%, in group). Course work CFD.
- Activity 3 (70%, individual). Exam with Theory and Problems.
The final grade is the average grade of the two modules. The grade for each module must be greater than or equal to 4.5/10 in order to make the average. You need at least a 3.0/10 in each of the exams to pass the module.
The non-presence in class when evaluation tests are carried out is a zero of the activity, without possibility of recovery. In the CFD exam, the minimum score for theory and problems is 3.0/10.
b) Programming of evaluation activities
The schedule of assessment activities will be communicated on the first day of the course.
The virtual platform used for communication with students will be the UAB Moodle Virtual Campus.
c) Recovery process
Student can apply for make-up of each module as long as they have presented himself to a set of activities that represent at least two thirds of the total grade of the module. Of these, those students who have a grade of more than 3.0 on average for all the activities in the module may be presented for make-up.
The make-up process of each module will consist of an exam with all the contents of the module. The maximum grade that can be obtained using this procedure will be6.0 in each module recovered.
d) Procedure for revision of qualifications
For each assessment activity, a place, date and time of review will be indicated where the student can review the activitywith the professor. In this context, complaints can be made about thegrade of the activity, which will be evaluated by the professor responsible for the subject. If the student does not submit to this review, this activity will not be reviewed at a later date.
e) Qualifications
In case one of the modules does not reach 4.5/10, the maximum final grade of the course will be 4/10 and the suspended module will have to be repeated the following year.
Honour plates. It is the decision of the faculty responsible for the subject to award an honorary matriculation grade. UAB regulations state that MH can only be awarded to students who have obtained a final grade equal to or higher than 9.00. Up to 5% of the total number of students enrolled may be awarded.
A student will be considered non-assessable (NE) if he/she has not presented to a set of activities whose weight equals a minimum of two thirds of the total grade of the subject.
f) Student Irregularities, Copying and Plagiarism
Without prejudice to other disciplinary measures that may be deemed appropriate, irregularities committed by the student that may lead to a variation in the grade of an evaluation act shall be graded with a zero. Therefore, copying, plagiarism, cheating, letting copy, etc. in any of the evaluation activities will involve suspending it with a zero. Evaluation activities graded in this way and by this procedure will not be recoverable.
g) Evaluation of Repeating Students
The only change in subject evaluation for repeaters is the possibility of maintaining grades from a module passed the previous course. This option must be communicated by email to the teacher responsible, no later than 15 days after the start of classes.
h) Single Assessment
This course does not offer a single assessment.
Bibliography
- J.D. Anderson. Computational Fluid Dynamics. The basics with Applications. McGraw-Hill, Inc., 1995.
- H.K. Versteeg, W. Malalasekera. An Introduction to Computational Fluid Dynamics. The Finite Volume Method. Prentice Hall, 2nd ed., 2007.
- S.V. Patankar, \"Numerical Heat transfer and Fluid Flow\". Hemisphere Pub., 1980.
- J. Tu, G.H. Yeoh, C. Liu. Computational Fluid Dynamics. A practical Approach. Elsevier, 2nd ed., 2013
- R. Byron Bird, Warren E. Stewart, Edwin N. Lightfoot. Transport Phenomena, 2002.
- Blazek J. Computational Fluid Dynamics: Principles and applications, 2005.
- Ferziger J., Peric M. Computational Method for Fluid dynamics, 2020
- B.W. Bequette. Process Dynamics. Modeling Analysis and Simulation. Prentice-Hall. International Series in the Physical and Chemical Engineering Sciences, 1998.
- W.L. Luyben. Process Modeling, Simulation and Control for Chemical Engineers, 2nd ed. McGraw-Hill, New York, 1990.
- MATLAB. The MathWorks MATLAB® http://es.mathworks.com/
- Yeong Koo Yeo;Yeong Koo Yeo. Chemical Engineering Computation with MATLAB® 2020
- Al-Malah K MATLAB Numerical Methods with Chemical Engineering Applications, 2013
Software
A minimum level of MATLAB is required to undertake the course. The CFD part will be taught in FLUENT.
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE
| Type of teaching | Group | Language | Semester | Shift |
|---|---|---|---|---|
| (TEm) Theory (master) | 1 | Catalan | annual | afternoon |