Course detail

Automation of Calculation, Simulation and Visualization

FSI-LAVAcad. year: 2024/2025

This course offers a structured approach to programming fundamentals and their applications in the context of energy engineering. The initial weeks focus on establishing a solid foundation, introducing students to basic programming concepts and data processing techniques. As the course progresses, we delve deeper into advanced programming features, such as debugging, logging, and profiling. The utilization of both standard and third-party libraries is explored. Additionally, the course underscores the significance of data analysis and presentation, emphasizing the use of Python libraries like Numpy, Pandas, and Plotly, enabling the creation of visually appealing and interactive graphs.

Furthermore, students will be introduced to specialized tools such as FeniCSx, Coolprop, and Xsteam, which are essential for addressing energy-related tasks. The course concludes with coverage of optimization techniques, parallel programming for processing large volumes of data, and a comprehensive review of assignments completed by students throughout the semester, ultimately leading to earning credit.

Language of instruction

Czech

Number of ECTS credits

2

Mode of study

Not applicable.

Guarantor

Ing. Libor Kudela, Ph.D.

Department

Energy Institute (EÚ)

Entry knowledge

A foundational understanding of mathematics and physics at the undergraduate level, coupled with analytical thinking skills.

Rules for evaluation and completion of the course

Regular and active participation in exercises, delivery of all assigned tasks is required for credit to be granted.

Aims

In this course, students will learn how to automate calculations and design processes for developing in-house software by utilizing the Python programming language, along with compatible libraries and open-source software. This approach minimizes the need for manual and intellectual labor, ultimately enhancing efficiency. Furthermore, students will also become acquainted with tools for visually presenting results and data through appealing diagrams, extending beyond engineering calculations.

Study aids

The course is complemented by a body of online resources, primarily in the form of instructional videos, providing comprehensive explanations of the theoretical and practical aspects of the topics covered. Students are strongly encouraged to make use of these resources while tackling their assignments during semestr.

Prerequisites and corequisites

Not applicable.

Basic literature

FÜHRER, C.; SOLEM, J.E. a VERDIER, O. Scientific Computing with Python - Second Edition: High-performance scientific computing with NumPy, SciPy, and pandas. Packt Publishing, 2021. (EN)
STEINKAMP, V. Python for Engineering and Scientific Computing. Rheinwerk Computing, 2024. (EN)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme N-ETI-P Master's

    specialization ENI , 1 year of study, summer semester, compulsory

  • Programme C-AKR-P Lifelong learning

    specialization CLS , 1 year of study, summer semester, elective

Type of course unit

 

Computer-assisted exercise

26 hod., optionally

Teacher / Lecturer

Ing. Libor Kudela, Ph.D.

Syllabus

Week 1 - Introduction to programming 1 - Data types, Basic operations, Generic operations,

Week 2 - Introduction to programming 2 - Flow control, Loops, Functions, arguments,

Week 3 - Objects, Inheritance, Polymorphism,

Week 4– Debugging, logging, profiling,

Week 5 - Python Standard Libraries, Third Party Modules, Imports,

Week 6 - Working with files, Text and binary files,

Week 7 - Arrays and Matrices, Numpy library,

Week 8 - Time series, Data analysis, Pandas,

Week 9 - Data presentation, Interactive graphs, Plots, Dashboard,

Week 10 - Selected Libraries for Energy Engineers, FeniCSx, Coolprop, Xsteam,

Week 11 - Optimization, SciPy, PyTorch,

Week 12 - Parallel programming for processing a large volume of data,

Week 13 - Review of assignments, Credit.