Passive scalar interface in a spatially evolving mixing layer (A. Attili and D. Denker)

Quartz nozzle sampling (D. Felsmann)

Dissipation element analysis of a planar diffusion flame (D. Denker)

Turbulent/non-turbulent interface in a temporally evolving jet (D. Denker)

Dissipation elements crossing a flame front (D. Denker and B. Hentschel)

Particle laden flow (E. Varea)

Turbulent flame surface in non-premixed methane jet flame (D. Denker)

DNS of primary break up (M. Bode)

Diffusion flame in a slot Bunsen burner (S. Kruse)

Various quantities in spatially evolving jet diffusion flame (D. Denker)

OH layer in a turbulent wall bounded flame (K. Niemietz)

Measurement Techniques and Data Analysis

The course takes place in the winter semester.


Lecture Professor Heinz Pitsch

Thursday, 12:30 - 14:00, H09 (CARL) (12.10.2023 - 01.02.2024)

Exercise Christian Schwenzer

Thursday, 14:30 - 15:15, H09 (CARL) (12.10.2023 - 01.02.2024)


Friday, 09.02.2024, 08:30 - 10:00, H02 (CARL)

Lecture and Exercise

LV-Registration: RWTHonline

For Lecture and Exercise, a joint moodle page (VO) is used.

Lecture and Exercise material: RWTHmoodle


Introduction to the meaning of measurement data, measurement uncertainty and data analysis.

  • Acquisition, presentation and management of measurement data
  • Challenges in the acquisition of measurement data based on examples
  • Introduction of statistical parameters
  • Data management and data life cycle
  • Measurement uncertainties
  • Introduction of different measurement uncertainties
  • Determination of measurement uncertainties and error propagation (Part 1)
  • Determination of measurement uncertainties and error propagation (part 2)
  • Calibration and verification of the measurement technique
  • Data analysis
  • Filtering of data and data visualization
  • Hypothesis testing and functional relationships by means of regression
  • Data analysis with machine learning methods (introduction)
  • Data analysis with machine learning methods (examples)
  • Application of the learned methods in MATLAB

Accompanying the lecture, exercises are offered in which the students apply the acquired
methods and concepts using real data sets.


The lecture "Measurement Techniques and Data Analysis" has a scope of 3 ECTS.


Consultation Hour

Individual arrangement. Contact Christian Schwenzer.


Freitag, 09.02.2024, 08:30 - 10:00, H02 (CARL)