Course detail
Energy System Modeling
FSI-9MES Acad. year: 2025/2026 Winter semester
The course is focused on computational models of energy systems and models of individual problems and phenomena occurring in these systems. The course does not focus on mastering specific simulation tools, but it is aimed at developing critical thinking and other abilities and skills needed to create and use computational models of real-life systems and problems.
Language of instruction
Czech
Supervisor
Department
Entry knowledge
Practical knowledge of mathematics, physics, thermodynamics, heat transfer and fluid mechanics. Computer skills.
Rules for evaluation and completion of the course
Students need to complete individual energy system modeling projects approved by the teacher. The students are encouraged to choose the topics of their projects with regard to the topics of their dissertations.
Attendance at the lectures is not obligatory. Completion of the project assignment is obligatory.
Aims
Development of students' abilities for independent creation and use of computational models of energy systems. The main emphasis is on the ability to appropriately formulate the problem and to propose (use) a computational model apt for achieving the desired results.
Students will learn critical and methodical approaches to creating and using models of energy systems.
The study programmes with the given course
Programme D-ENE-P: Power Engineering, Doctoral, recommended course
Type of course unit
Lecture
20 hours, optionally
Syllabus
Formulation of the modeled problems. Reasons for and appropriateness of using computational modeling. Model creation methodology. Adequacy (detail) of the model with respect to the desired results. Computational complexity of models. Mathematical complexity of models. Implementation methods. Integration of models with computational tools. Approaches to model formulation. Energy balance methods. Discretization and numerical methods. Direct and iterative solutions. Input and output data. Uncertainty of input data. Initial and boundary conditions. Processing and interpretation of modeling results. Parametric studies. Proposal and analysis of various scenarios. Model verification and validation. Discrepancies between simulation and experimental results. Utilization of computational models for optimization of energy systems.