Valid for: 2026/27
Faculty: Faculty of Engineering LTH
Decided by: PLED BI/RH
Date of Decision: 2026-04-09
Depth of study relative to the degree requirements: Second cycle, in-depth level of the course cannot be classified
Elective for: BI4, BR4-bsa, RH4
Language of instruction: The course will be given in Swedish
The course is designed to provide knowledge of how the spread of fire and combustion gases is simulated using Computational Fluid Dynamics (CFD), vaious methods for modelling (LES, RANS), in e.g. fire safety design and fire investigations, as well as provide an understanding of the limitations of the numerical and physical models used, and an awareness of the most common sources of error. The course also aims to provide deeper knowledge, both theoretical and practical, about more advanced sub-models that are mainly used for research.
Knowledge and understanding
For a passing grade the student must
Competences and skills
For a passing grade the student must
Judgement and approach
For a passing grade the student must
Grading scale: TH - (U, 3, 4, 5) - (Fail, Three, Four, Five)
Assessment: Written individual examination and approved individual assignments. Presence at the seminars is mandatory.
The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.
Modules
Code: 0118. Name: Exam.
Credits: 3.5. Grading scale: TH - (U, 3, 4, 5).
Assessment: Exam on the theoretical part of the course.
The module includes: Be able to describe the physical models used for conservation of mass, material, energy, and momentum.
Be able to describe various numerical methods for solving the equation sets.
Be able to identify the limitations and most common sources of error of the model components used.
Be able to apply different sub-models to practical problems as well as being able to discuss the reults.
Code: 0218. Name: Assignments.
Credits: 4.0. Grading scale: UG - (U, G).
Assessment: Approved assignments and approved active participation in seminars.
The module includes: Assignments related to the lecturing material that is handed in individually and then discussed during seminars.
The student should have worked with the given assignment and performed simulations, generated results and present a discussion which relates to the presented theory, numerical errors, user errors, limitations of the model as well as common mistakes.
Admission requirements:
Course coordinator: Jonathan Wahlqvist,
jonathan.wahlqvist@brand.lth.se
Course administrator: Linnéa Ekman,
linnea.ekman@ebd.lth.se
Generative AI may be used in a similar way as other tools used for gathering information. Using the resulting text, figure, code or similar created by a GAI tool is treated as deceptive and is treated as cheating. The use of GAI tools shall be stated in written material for examination. Recommendation on the use of GAI tools is presented on the course area on Canvas.