Efficient-Search-of-Neighboring-Particles

Efficient Search of Neighboring Particles

Note on non-puntual particles

The criteria L/M > interactionRadius changes to L/M > interactionRadius + 2 * maximumParticleRadius.

Note on optimum M for growing density

Given L=20, interactionRadius=1 and particleRadius=0.25 and the previous criteria, matrix size M is bounded as M < 40/3 = 13,33.

In this sense, tests were made for different values of M and N.

For a greater density, a greater M is better. M = 13 is the optimum M for these tests.

Compilation

mvn package

Execution

Generation of static and dynamic files

python StaticAndDynamicGenerator.py

Running simulation

java -jar tp1-1.0-SNAPSHOT.jar

Options:

• -h, --help: Prints usage infp.
• -M, --matrix <size>: Size of the squared matrix.
• -r, --radius <double>: Interaction radius.
• --pbc: Enable periodic boundary conditions.
• --bf: Enable brute force algorithm.
• --staticFile <path>: Path to static file.
• --dynamicFile <path>: Path to dynamic file.

The simulation's results (execution time and list of neighbours for each particle) are printed to stdout. If you wish, you can redirect the output to a txt file:

java -jar target/tp1-1.0-SNAPSHOT.jar --pbc=true --dynamicFile=random/Dynamic-5.txt --staticFile=random/Static-5.txt --radius=3.0 > output.txt

Running Animation

The file animation.m provides of a function that animates the simulation's results:

• static_file: Path to static file.
• dynamic_file: Path to dynamic file.
• output_file: Path to output file.
• M: MxM matrix size.
• particle_id: a certain particle id to view with its neighbors.

animation('./random/Static-5.txt','./random/Dynamic-5.txt','./output.txt', 20, 43);