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Lagrangian Particle, first particle integration step wrong dt and age? |
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February 13, 2020, 06:39 |
Lagrangian Particle, first particle integration step wrong dt and age?
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Join Date: Feb 2020
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Hei,
I am using reactingParcelFoam as LTS (local -time-stepping) for a steady state analysis. Using a precalculated flow result (reactingCloud1Properties -> active no, I do one steady particle injection (reactingCloud1Properties -> active yes; transient no. I use v1812 at the moment. 1. First thing I noticed is that deltaTMax from reactingCloud1Propetries is not used in algorithm. 2. In the particle integration loop in "Foam::KinematicParcel<ParcelType>::move" in KinematicParcel.C in the first step the particle velocity is not yet calculated so U_ is (0 0 0), calcVelocity was not yet called. The displacement is reported as: Tracking from (0.02 0.005 0.003) along (0 0 0) to (0.02 0.005 0.003) which makes sense when particle velocity is zero. But what is somehow strange is that the stepfraction is increased as a result of factor f has size of maxCo. Threafter the time-step dt is calculated to a non-zero value due to non-zero stepfraction. Time-step is added to age, so the particle has a starting age which is proportional to maxCo and trackTime (trackTime is the maxTrackTime from reactingCloud1Properties). First step of while loop: f = 1- p.stepFraction (latter is zero at start) so f = 1 then f = min(f,maxCo), with maxCo of 0.01 f is 0.01. The trackToFace (actually in particle::trackToStationaryTri) algorithm sets stepFraction to 0.01. So with this stepfraction, sfrac (is stepFraction at start=0) and trackTime of 10s dt is 0.1s and age increases to 0.1s without any movement. This is also visible in the age file and one can visualize that in paraview. The particle velocity is calculated afterwards with (I guess) the new particle position and flow conditions and is used in the next tracking step, which seams to be ok. That would mean in my opinion that the particle age is incorrect. Did anybody observe the same behaviour? |
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lagrangian method, particle cloud |
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