Course detail

Experimental Modeling and Signal Processing

FSI-LES Acad. year: 2025/2026 Summer semester

In the subject, students will learn about the design of the experiment, the evaluation of the measured data into basic characteristics. Furthermore, with data processing using Fourier transformation, spectrogram and usability of these dependencies for practice. Sensitivity analysis and the basics of uncertainty in experimental modeling will also be part of the curriculum.

Language of instruction

Czech

Number of ECTS credits

5

Supervisor

doc. Ing. Vladimír Habán, Ph.D.

Department

Energy Institute

Entry knowledge

Basics of work in Python (Matlab), basics of dynamics and statistics.

Rules for evaluation and completion of the course

The course will end with the defense of individual projects and the examination of basic knowledge of the subject matter covered.

Participation in exercises is mandatory and controlled.

A rare non-participation in the exercises will lead to the assignment of substitute assignments.

Aims

The aim of the course is to introduce students to the design of an experiment and the evaluation of measured data. By processing measured data using Fourier transform, spectrogram. Also with signal filtering, basics of measurement uncertainty evaluation and sensitivity analysis.

The study programmes with the given course

Programme N-SUE-P: Computational Simulations for Sustainable Energy, Master's, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Syllabus


  1. Introduction to the issue, design of the experiment, similarity numbers

  2. Measured signal, sampling, basics of processing

  3. Fourier transform

  4. Spectrogram

  5. Signal filtering

  6. Integration and derivation of the measured signal (with the application of filtering)

  7. Regression of measured data

  8. Sensitivity analysis

  9. Signal processing in accordance with ČSN 20816 into rms, p-p, etc.

  10. Differences and procedures when comparing CFD result with experimental modeling

  11. Basics of image processing, edge detection, optical flow.

  12. Examples of solved problems from practice.

Computer-assisted exercise

26 hours, compulsory

Syllabus


  1. Python signal processing, graphs

  2. Reading of the measured signal, text files, TDMS, type files.

  3. Output to texts, type file, graphs.

  4. Bulk file processing

  5. Fourier transform

  6. Spectrogram, melspectrogram

  7. Regression

  8. Sensitivity analysis of measured data

  9. Vibration measurement and evaluation in the form of rms, p-p for bearing and shaft vibrations.

  10. Balancing at prototypy.

  11. Assignment of projects and control of students' project work.