Stud.IP Uni Oldenburg
Universität Oldenburg
06.12.2021 09:22:44
phy611 - Theoretical Methods (Vollständige Modulbeschreibung)
Originalfassung Englisch PDF Download
Modulbezeichnung Theoretical Methods
Modulkürzel phy611
Kreditpunkte 6.0 KP
Workload 180 h
attendance: 56 hrs, self study: 124 hrs
Einrichtungsverzeichnis Institut für Physik
Verwendbarkeit des Moduls
  • Master Engineering Physics (Master) > Pflichtmodule
Zuständige Personen
Anemüller, Jörn (Prüfungsberechtigt)
Doclo, Simon (Prüfungsberechtigt)
Hartmann, Alexander (Prüfungsberechtigt)
Kühn, Martin (Prüfungsberechtigt)
Kunz-Drolshagen, Jutta (Prüfungsberechtigt)
Neu, Walter (Prüfungsberechtigt)
Peinke, Joachim (Prüfungsberechtigt)
Poppe, Björn (Prüfungsberechtigt)
Schmidt, Thorsten (Prüfungsberechtigt)
Stoevesandt, Bernhard (Prüfungsberechtigt)
Strybny, Jann (Prüfungsberechtigt)
Computational Fluid Dynamics (CFD I & II) - Deeper understanding of the fundamental equations of fluid dynamics. - Overview of numerical methods for the solution of the fundamental equations of fluid dynamics. - Confrontation with complex problems in fluiddynamics. - To become acquainted with different, widely used CFD models that are used to study complex problems in fluid dynamics. - Ability to apply these CFD models to certain defined problems and to critically evaluate the results of numerical models. Computerorientierte Physik Extension and complement of qualification in theoretical physics through the acquisition of solid and deep knowledge of advanced concepts and methods in theoretical physics. Depending on the selected course the students acquire knowledge in the fields of basis numerical methods of theoretical physics, algorithms and data structures in scientific computing, code debugging. They obtain skills for a confident application of modern methods of theoretical physics such as diagram generation, Molecular Dynamics and Monte Carlo simulations and quantitative analysis of advanced problems of theoretical physics and in further development of the physical intuition. They enhance their competences to effectively deal with sophisticated problems of theoretical physics, to independently develop approaches to current issues of theoretical physics, and to comprehend common concepts and methods of theoretical physics and the natural sciences, in general. Modelling and Simulation The students attending successful the course acquire an advanced understanding of the conceptual design of models in the field of engineering sciences. Special emphasis is on identifying the significant physical processes and the choice of the most efficient modelling type. The interaction of numerical simulations with field measurements and laboratory measurements including the theory of similarity will be discussed. To meet the needs of renewable energy, laser technology, environmental sciences and marine sciences the practical focus is on the modelling and simulation of fluid dynamics in small scales and close to structures.
Computational Fluid Dynamics (CFD I & II) - CFD I: The Navier-Stokes equations, filtering / averaging of Navier- Stokes equations, introduction to numerical methods, finite- differences, finite-volume methods, linear equation systems, NS-solvers, RANS, URANS, LES, DNS, turbulent flows, incompressible flows, compressible flows, efficiency and accuracy. - CFD II: 
Introduction to different CFD models, such as OpenFOAM and PALM. Application of these CFD models to defined problems from rotor aerodynamics and the atmospheric boundary layer. Computerorientierte Physik - Debugging - Data structures - Algorithms - Random number generation - Data analysis - Percolation - Monte Carlo simulation - Finite size scaling - Quantum Monte Carlo - Molecular dynamics simulations - Event-driven simulations - Graphs and algorithms - Genetic algorithms - optimization problems Modelling and Simulation - Understanding of advanced fluid dynamics including three-dimensional, transient and compressible processes - Identifying the significant physical processes, defining the dimensionality and relevant scales in time and space - Theory of similarity, range of dimensionless numbers - Potential Theory - Numerical Algorithms and possibilities of independent coding of simplest mathematical models - Limitations of numerical models, risk of empirical approaches included in numerical models - Introduction of a complete chain of Open-Source-CFD-Tools, considering preprocessing, processing and postprocessing tools - Need and availability of appropriate measurement techniques for the steering, calibration and verification of models - Contactless high-resolving measuring techniques in the fluid dynamics - Limits of accuracy of different modelling and simulation concepts
- Computational Fluid Dynamics (CFD I & II) J.H. Ferziger, M. Peric, Computational Methods for Fluid Dynamics, Springer, 2002. C. Hirsch, Numerical Computation of Internal and External Flows: Introduction to the Fundamentals of CFD, Vol 1: Fundamentals of Computational Fluid Dynamics, 2nd edition, Butterworth-Heinemann, Amsterdam. P. Sagaut, Large Eddy Simulation for Incompressible Flows, Springer, Berlin, 1998. J. Fröhlich, Large Eddy Simulationen turbulenter Strömungen, Teubner, Wiesbaden, 2006. (in German) - Computerorientierte Physik T. H. Cormen, S. Clifford, C.E. Leiserson, und R.L. Rivest: Introduction to Algorithms. MIT Press, 2001. K. Hartmann: Practical guide to computer simulation. World-Scientific, 2009. J. M. Thijssen: Computational Physics. Cambridge University Press, 2007. M. Newman, G. T. Barkema: Monte Carlo Methods in Statistical Physics. Oxford University Press, 1999. - Modelling and Simulation Versteeg, K.H. & Malalasekera, W.: An Introduction to Computational Fluid Dynamics. Prentice Hall, 2nd rev. Ed., 2007.
Unterrichtsprachen Deutsch, Englisch
Dauer in Semestern 1 Semester
Angebotsrhythmus Modul halbjährlich
Aufnahmekapazität Modul unbegrenzt
Modullevel / module level ---
Modulart / typ of module je nach Studiengang Pflicht oder Wahlpflicht
Lehr-/Lernform / Teaching/Learning method Lecture: 3hrs/week; Excercises: 1hrs/week
Vorkenntnisse / Previous knowledge
Lehrveranstaltungsform Kommentar SWS Angebotsrhythmus Workload Präsenz
2 SoSe oder WiSe 28
2 SoSe oder WiSe 28
Präsenzzeit Modul insgesamt 56 h
Prüfung Prüfungszeiten Prüfungsform
According selected course