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WiSe 2020/21 - WiSe 2023/24

English

Mathematical Methods of Turbulence Control II

3

Nayeri, Christian

unbenotet

Portfolioprüfung

Zugehörigkeit


Fakultät V

Institut für Strömungsmechanik und Technische Akustik

35311200 FG Experimentelle Strömungsmechanik

Physikalische Ingenieurwissenschaft

Kontakt


No information

No information

christian.nayeri@tu-berlin.de

No information

Learning Outcomes

In this course students learn modern mathematical methods of turbulence control. These methods include model-free control, model-based control, dynamic reduced-order models and flow estimators. Students attending this course will be able to control turbulent flows in experiment and/or simulations, to model and to analyze these flows.

Content

The course teaches following contents, e.g. - Regression problems in fluid mechanics for control, modeling, estimation and parametric dependencies - Regression solvers in fluid mechanics focusing on methods of machine learning - Model-free control, e.g. machine learning control - Model-based control with dynamic reduced-order models - Model identification and surrogate models (digital twins) - Data analysis and state estimators from measurement signals

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Mathematische Methoden der Turbulenzregelung IIIVSoSeEnglish1

Workload and Credit Points

Mathematische Methoden der Turbulenzregelung II (IV):

Workload descriptionMultiplierHoursTotal
Attendance15.01.0h15.0h
Pre/post processing15.04.0h60.0h
75.0h(~3 LP)
The Workload of the module sums up to 75.0 Hours. Therefore the module contains 3 Credits.

Description of Teaching and Learning Methods

The contents will be taught within a lecture in compact format and encourage self guided application of the methods under supervision. Execises and projects help to deepen the acquired knowledge.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

required: -fundamental fluid mechanics desired: -fundamental control theory and machine learning

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

ungraded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

German/English

Test elements

NamePointsCategorieDuration/Extent
Hausaufgabe/Projekt30flexibleNo information
Rücksprache70oralNo information

Grading scale

At least 50 points in total needed to pass.

Test description (Module completion)

No information

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 20.

Registration Procedures

Please register at secretary HF1 (fd-TB-office@win.tu-berlin.de).

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  available
Additional information:
Will be provided during lecture

 

Literature

Recommended literature
No recommended literature given

Assigned Degree Programs

This module is not used in any degree program.

Miscellaneous

The lecture will be given by Prof. Bernd Noack (Bernd.Noack@limsi.fr). For further details contact him.