Dear all, This is not an answer, nor a solution. Just wanted to share an observation from running a parallel simulation, which should converge to a stationary solution I observe a kind of random variation in the kinetic energy - which matches the "low amplitude, random" description of Fernando. I thought it comes from the outflow condition I am using, or the high order modes, and I ignored it. Best, Stan 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 On 12.01.2018 09:46, F Mellibovsky wrote:
Dear all,
When running parallel (72 core) aggregate quantitities such as body forces (particularly pressure forces) seem to include a certain degree -low amplitude- of randomness in their evolution. The high frequency random oscillations seem to be of the order of the sampling period. This suggests some kind of issue with the gathering of data from all parallel processes for aggregate quantity computation.
Is there some precaution that must be taken upon compilation or in defining the cluster parallel environtment to avoid this issue?
I attach one such forces time-series to illustrate this. Viscous forces seem to be just fine, while pressure (and total) forces oscillate at high frequency.
Cheers
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