This might not sound like much, but it matters greatly to plane manufacturers. (2006), a 2% decrease in κ gives 1% decrease in predicted aircraft drag. For example, for the Karman ‘constant’, κ, (in l = κ d, where l is the mixing length and d nearest wall distance) the published range is 0.38–0.45) and we do not even know if it is a constant! As noted by Spalart et al. The formidable task facing turbulence modelling is reflected by Richard Feynman (Physics Nobel Laureate), who wrote, “Turbulence is the last great unsolved problem in classical physics.” Indeed we have the situation where we do not even know basic physical constants. Then a range of established modelling approaches is outlined, starting with low order and then moving to techniques where CFD becomes predictive with no modelling. Initially the nature of turbulence is outlined and thus the modelling challenges defined. Hence, this chapter has strong importance in relation to the safe and reliable use of CFD.

The modelling of turbulence has strongly defined CFD as a postdictive rather than predictive process. These are all aspects that are vital in aerodynamic design.

Turbulence generally plays a key role in drag generation, heat transfer, particle dispersion and scalar mixing along with sound generation.

Fd turbulence