Baldan et al 2022a - Revision history

Scipediacontent at 11:41, 15 February 2022

2022-02-15T11:41:56Z

Scipediacontent: Scipediacontent moved page Draft Content 653816879 to Baldan et al 2022a

2022-02-15T11:19:55Z

Scipediacontent moved page Draft Content 653816879 to Baldan et al 2022a

Scipediacontent: Created page with "== Abstract == Particle tracking within an underlying flow field is routinely used to analyse both industrial processes and natural phenomena. In a computer code running on a..."

2022-02-15T11:19:26Z

Created page with "== Abstract == Particle tracking within an underlying flow field is routinely used to analyse both industrial processes and natural phenomena. In a computer code running on a..."

New page

== Abstract ==

Particle tracking within an underlying flow field is routinely used to analyse both industrial processes and natural phenomena. In a computer code running on a
distributed-memory architecture, the different behaviour of fluid-particle systems must be taken into account to properly balance element-particle subdivision among processes.
In unsteady simulations, the parallel efficiency is even more critical because it changes over time. Another challenging aspect of a scalable implementation is the initial particle
location due to the arbitrary shapes of each subdomain. In this work, an innovative parallel ray tracing particle location algorithm and a two-constrained domain subdivision
are presented. The former takes advantage of a global identifier for each particle, resulting in a significant reduction of the overall communication among processes. The latter is
designed to mitigate the load unbalance in the particles evolution while maintaining an equal element distribution. A preliminary particle simulation is performed to tag the cells
and compute a weight proportional to the probability to be crossed. The algorithm is implemented using MPI distribute memory environment. A cloud droplet impact test
case starting from an unsteady flow around a 3D cylinder has been simulated to evaluate the code performances. The tagging technique results in a computational time reduction
of up to 78% and a speed up factor improvement of 44% with respect to the common flow based domain subdivision. The overall scalability is equal to 1.55 doubling the number of
cores.

== Full document ==
<pdf>Media:Draft_Content_653816879722_paper.pdf</pdf>

@@ Line 3: / Line 3: @@
 Particle tracking within an underlying flow field is routinely used to analyse both industrial processes and natural phenomena. In a computer code running on a
 distributed-memory architecture, the different behaviour of fluid-particle systems must be taken into account to properly balance element-particle subdivision among processes.
-In unsteady simulations, the parallel efficiency is even more critical because it changes over time. Another challenging aspect of a scalable implementation is the initial particle
+In unsteady simulations, the parallel efficiency is even more critical because it changes over time. Another challenging aspect of a scalable implementation is the initial particle location due to the arbitrary shapes of each subdomain. In this work, an innovative parallel ray tracing particle location algorithm and a two-constrained domain subdivision are presented. The former takes advantage of a global identifier for each particle, resulting in a significant reduction of the overall communication among processes. The latter is designed to mitigate the load unbalance in the particles evolution while maintaining an equal element distribution. A preliminary particle simulation is performed to tag the cells and compute a weight proportional to the probability to be crossed. The algorithm is implemented using MPI distribute memory environment. A cloud droplet impact test case starting from an unsteady flow around a 3D cylinder has been simulated to evaluate the code performances. The tagging technique results in a computational time reduction of up to 78% and a speed up factor improvement of 44% with respect to the common flow based domain subdivision. The overall scalability is equal to 1.55 doubling the number of cores.
 location due to the arbitrary shapes of each subdomain. In this work, an innovative parallel ray tracing particle location algorithm and a two-constrained domain subdivision
 are presented. The former takes advantage of a global identifier for each particle, resulting in a significant reduction of the overall communication among processes. The latter is
 designed to mitigate the load unbalance in the particles evolution while maintaining an equal element distribution. A preliminary particle simulation is performed to tag the cells
 and compute a weight proportional to the probability to be crossed. The algorithm is implemented using MPI distribute memory environment. A cloud droplet impact test
 case starting from an unsteady flow around a 3D cylinder has been simulated to evaluate the code performances. The tagging technique results in a computational time reduction
 of up to 78% and a speed up factor improvement of 44% with respect to the common flow based domain subdivision. The overall scalability is equal to 1.55 doubling the number of
 cores.
-== Full document ==
+== Abstract==
 <pdf>Media:Draft_Content_653816879722_paper.pdf</pdf>

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