MASK3610 Anvendt fluidmekanikk Emneplan

No formal requirements over and above the admission requirements.

Passed the course Mathematics 1000 (MEK1000), Mathematics 2000 (MEK2000), and Physics and Chemistry (MEK1100) / Physics (MEK1400) and Mechanics (MAPE1300).

On successful completion of the course, students should have the following learning outcomes defined in terms of knowledge, skills, and general competence.

Knowledge

The student has:

• knowledge of supervised, unsupervised, reinforcement learning
• good understanding of the principles of state-of-the-art deep neural networks such as convolutional neural networks, sequential models (RNN, LSTM), Transformers, GenerativeAI (Autoencoder, GAN, Diffusion models), and reinforcement learning.
• a good understanding of both theoretical and practical know-how required to use machine learning and deep learning methods effectively.

Skills

The student can:

• build, train, test, and deploy machine learning and deep learning models
• analyze machine learning methods in regard to their performance and effectiveness
• use existing deep learning networks, improve and/or customize them to apply to new problems

General competence

The student:

• has both theoretical and practical understanding of machine learning and deep learning methods
• can discuss relevance, strength, and limitations of machine learning and deep learning in solving real-world problems
• can work on effectively relevant research projects

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student:

• understands the basic concepts of fluid mechanics (such as boundary layer, continuum hypothesis, viscosity, conservational laws) and recognizes practical fluid dynamics problems.
• knows the difference between system approach and control volume approach and is able to explain the difference between Eulerian and Lagrangian description of fluid flow.
• knows how to use the fundamental governing equations of fluid dynamics - (continuity, momentum and energy equations) and is aware of its area of application.
• is capable of using dimension analysis to design prototypes of a reduced scale.
• understands the basic theoretical background of CFD and is able to perform simple CFD analysis.
• understanding of the various physical phenomena associated with the internal flow and external flow over common geometries.

Skills

The student is capable of:

• making simple calculations of velocity, pressure, temperature and shear stress for incompressible flows.
• formulating reasonable mathematical models to solve technical problems.
• calculating lift and drag forces acting on rigid bodies moving relative to a fluid.
• analyzing flow in pipes and pipeline networks and calculating energy and pressure loss.
• performing basic CFD analysis and comprehend the results of CFD.

General competence

The student must:

• be capable of using mathematical modelling and solutions to problems in fluid mechanics.
• be able to use CFD tool to solve relevant problems related to fluid flow

Exam in two parts:

• A group project: implementation and report (about 7000 words). A group of 2-3 students will be formed during the course. Each group member receives an individual grade based on their contribution to the project.
• Individual oral exam (about 30 minutes).

Each of them carries 50% weight in the final grade. The oral examination cannot be appealed.

Both exams must be passed in order to pass the course.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

All aids are permitted for the project report, provided the rules for plagiarism and source referencing are complied with.

No aids are permitted for the oral exam.

Grade scale A-F.

Two internal examiners. External examiner is used periodically.

Associate Professor Raju Shrestha

• Bachelor level knowledge in linear algebra, vector calculus, and basic statistics, and probability is important for understanding some of the concepts in this course.
• Knowledge and skills in programming, particularly Python, and machine learning frameworks such as scikit-learn, TensorFlow, and Keras.
• Knowledge and skills in cloud containerization technologies such as Docker.

This course covers principles of machine learning and deep learning methods and best practices in solving problems effectively. Most of the problems are related and applicable in various areas such as computer vision, surveillance, assistive technology, medical imaging, etc. Therefore, the course intends to provide case studies and examples of ML and DL in solving various problems. Students can explore the tremendous potential of modern AI, ML, and DL methods and techniques in solving problems in different application domains through project work.