16. Computational fluid dynamics (Contd.)
The chapter outlines key principles in Computational Fluid Dynamics, focusing on the Reynolds shear stress equation and its implications for turbulent flows. It introduces various turbulence models, particularly the k-epsilon and k-omega models, and discusses direct numerical simulation techniques. The relationship between kinetic energy dissipation and turbulent flow characteristics is emphasized, highlighting the complexities involved in simulating turbulent systems effectively.
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Sections
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What we have learnt
- Reynolds shear stress is pivotal for understanding mean flow in turbulent fluids.
- The k-epsilon model focuses on the effects of turbulent kinetic energy and is widely used for turbulence modeling.
- Direct numerical simulation solves Navier-Stokes equations without turbulence models but requires substantial computational resources.
Key Concepts
- -- Reynolds Shear Stress
- A stress term that accounts for the effects of turbulence in the flow, essential for applying Reynolds-averaged equations to fluid dynamics.
- -- Turbulent Kinetic Energy (k)
- A measure of energy contained in turbulent eddies, used in turbulence modeling to predict flow characteristics.
- -- Eddy Viscosity (nu_T)
- A model parameter that represents the turbulent effect on viscosity in fluid flow, critical for calculating Reynolds shear stress.
- -- Closure Problem
- The challenge in turbulence modeling of relating unknown turbulence stresses to known quantities, often resolved through various models such as k-epsilon.
- -- Direct Numerical Simulation (DNS)
- A computational method that simulates fluid flows by solving Navier-Stokes equations directly, requiring high resolution to capture turbulent effects.
- -- Kolmogorov Length Scale (eta)
- The length scale at which turbulence energy is dissipated, crucial for understanding the dynamics of energy transfer in turbulent flows.
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