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How to do postprocessing or on-the-fly analysis of max. WSS for each facet within one period?

Posted: Mon Nov 04, 2019 1:18 am
by hfwang
Dear developers,

I want to simulate pulsatile blood flow in an artery. Most importantly, I want to find maximum wall shear stress (WSS), together with its corresponding time step, for each facet (each finite element) of the entire artery mesh within one heart beat (say period T=0.8 second). Then, all local max. WSS and their corresponding times should be displayed over the arterial wall via ParaView.

Naively, I can save the simulation per time step and then write a C++/Python script to find the maximum WSS and its corresponding instant for each facet. However, it will be both memory-consuming and time-consuming. Could you give me some suggestions in this regard? How could this be realized in an easier way?

Best regards,
Haifeng

Re: How to do postprocessing or on-the-fly analysis of max. WSS for each facet within one period?

Posted: Mon Nov 04, 2019 8:25 pm
by davep
Hi Haifeng,

Reading in the restart files and finding the max wss is not really that time consuming I think, compared to the time it takes to generate the results.

But if you really wanted to do this on-the-fly then I suppose you could modify the solver code to calculate this as it was writing restart results files.

Cheers,
Dave

Re: How to do postprocessing or on-the-fly analysis of max. WSS for each facet within one period?

Posted: Tue Nov 05, 2019 12:37 am
by hfwang
Hi Dave,

Many thanks for your suggestions!

As you kindly suggested, I will try to add some extra functions into the 'write_restart_files.c' to find max WSS(t, f_i) for each finite segment (facet) f_i of the vessel wall during one heart beat.

Reading in the restart files and finding the max WSS is certainly a good way. To do this, however, in may case, I need to store results per time step in order to compare and determine at which time step the max WSS appears; this also needs to be carried out for each finite segment of the vessel wall, and thus both time consuming and memory consuming.

Best,
Haifeng