List of all ME courses

3 Credits
Spring term, odd years

ME 560, and
ME 565 or ME566
Sourabh Apte
308 Rogers Hall

Course Description

TThis course provides an overview of turbulence modeling techniques. Emphasis is placed on turbulence simulation techniques such as direct numerical simulation (DNS), large-eddy simulation (LES), and Reynolds-averaged Navier Stokes (RANS) models. This is not a programming intensive course, but rather based on the theory of turbulence modeling techniques. Students will gain understanding of turbulent flows and commonly used modeling techniques. Commonly used single and two-point statistics, energy spectra, and feature identification techniques will be summarized. Lectures will be designed to provide an overview of various topics and to introduce different concepts used in turbulence modeling. However, for deeper understanding of the presented concepts, this course requires extensive reading of assigned material including journal articles, handouts, book chapters, etc.


  • Turbulence modeling techniques
  • Direct numerical simulation (DNS)
  • Large-eddy simulation (LES)
  • Reynolds-averaged Navier Stokes (RANS) models
  • Single and two-point statistics,
  • Energy spectra
  • Feature identification techniques

Learning Outcomes

The student, upon successful completion of this course, will be able to:

  1. Derive governing equations for turbulent flow based on temporal as well as spatial filtering and apply the governing equations to analyze simple free shear as well as wall-bounded flows,
  2. Evaluate Reynolds averaged Navier Stokes equations and a suite of models used to close the Reynolds stresses using statistical correlations, realizability of the Reynolds stress tensor, and predictive capability,
  3. Perform spatial and temporal filtering of DNS data for apriori and a posteriori analysis employed in large-eddy simulation,
  4. Conduct independent reading of advanced research papers on assigned topics and analyze the findings based on the fundamental concepts of gradient diffusion hypothesis, scale-similarity, spatial and temporal scales, tensor manipulations, among others to individually complete a thorough research paper.