Specific problems of turbulent flow including automobile and truck aerodynamics and canonical flows including pipes, jets and boundary layers that are measured and simulated to gain basic understanding of turbulence. A goal of the course is to impart the necessary background for students to be able to critically assess and most effectively employ the turbulent flow prediction codes (e.g. Fluent) that are a mainstay of how turbulence is analyzed in modern industries.
Restriction: Permission of ENGR-Mechanical Engineering department.
Jointly offered with ENME656. Credit only gran ted for: ENME483 or ENME656.
Semesters OfferedFall 2018, Spring 2020
Develop an ability to solve basic and fundamental problems such as
- Use models to compute turbulent flows.
- Simulate turbulent flow using vortices.
- Decide on appropriate strategies for solving practical turbulent flows.
- Expand your ability to apply mathematics and physics to engineering problems involving fluid mechanics. This is strongly emphasized through lectures and computational assignments.
- Expand your ability to utilize MATLAB in solving engineering problems.
- Acquire an ability to critically read research articles in the literature.
- Week 1: Introduction to turbulent flow. Flow past a truck.
- Week 2: Physics of Homogeneous and Isotropic Turbulence.
- Week 3: DNS. Modeling dissipation.
- Week 4: Physics of Homogeneous Shear Flow and turbulent transport.
- Week 5: 2D turbulence and vortex simulations.
- Week 6: Spectral analysis of turbulence.
- Week 7: Physics of Channel and Pipe Flows.
- Week 8: Transport modeling.
- Week 9: Mixing length models.
- Week 10: Two-Equation Models.
- Week 11: Spalart/Allmaras model. 3D vortex simulations.
- Week 12: Introduction to Large Eddy Simulation (LES)
- Week 13: Dynamic LES Models.
- Week 14: Detached Eddy Simulations (DES)
- Week 15: Computing the flow past a truck.
- an ability to apply knowledge of mathematics, science, and engineering
- an ability to design and conduct experiments, as well as to analyze and interpret data
- an ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
- an ability to function on multi-disciplinary teams
Additional Course Information
None required. Posting of articles on elms.umd.edu.
- Two 75 minute lectures each week
Last Updated By
Dr. Peter Bernard, June 2017