DNS 1-2 Computational Details: Difference between revisions
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== Computation of statistical quantities == | == Computation of statistical quantities == | ||
Describe how the averages and correlations are obtained from the instantaneous results and how | <!--- Describe how the averages and correlations are obtained from the instantaneous results and how | ||
terms in the budget equations are computed, in particular if there are differences to the proposed | terms in the budget equations are computed, in particular if there are differences to the proposed | ||
approach in Introduction. | approach in Introduction.--> | ||
<br/> | <br/> | ||
---- | ---- | ||
{{ACContribs | {{ACContribs | ||
|authors=Lionel Agostini | |authors=Arun Soman Pillai, Lionel Agostini | ||
|organisation=Imperial College London | |organisation=Imperial College London | ||
}} | }} | ||
Revision as of 08:27, 6 October 2021
Computational Details
Computational approach
The computations are performed using PyFR version 1.12.0, a python based framework for solving advection-diffusion type problems using the Flux Reconstruction approach of Huynh. The details of the numerical approach are available here.
Spatial and temporal resolution, grids
The domain is discretised into hexahedral elements. For a polynomial order of 5, this yields 174 solution points in the transverse direction and about in total. The grid is uniform in the spanwise and streamwise directions, but clustered near the walls in transverse direction. An explicit RK45 scheme is used to advance the solution in time. The order of accuracy of the solution changes a function of time, as discussed in Witherden et al. (2014). The grid used for the simulations is available on the ERCOFTAC database.
Computation of statistical quantities
Contributed by: Arun Soman Pillai, Lionel Agostini — Imperial College London
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