Cargando…
How environment affects active particle swarms: a case study
We investigate the collective motion of self-propelled agents in an environment filled with obstacles that are tethered to fixed positions via springs. The active particles are able to modify the environment by moving the obstacles through repulsion forces. This creates feedback interactions between...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748504/ https://www.ncbi.nlm.nih.gov/pubmed/36533200 http://dx.doi.org/10.1098/rsos.220791 |
_version_ | 1784849839918415872 |
---|---|
author | Degond, Pierre Manhart, Angelika Merino-Aceituno, Sara Peurichard, Diane Sala, Lorenzo |
author_facet | Degond, Pierre Manhart, Angelika Merino-Aceituno, Sara Peurichard, Diane Sala, Lorenzo |
author_sort | Degond, Pierre |
collection | PubMed |
description | We investigate the collective motion of self-propelled agents in an environment filled with obstacles that are tethered to fixed positions via springs. The active particles are able to modify the environment by moving the obstacles through repulsion forces. This creates feedback interactions between the particles and the obstacles from which a breadth of patterns emerges (trails, band, clusters, honey-comb structures, etc.). We will focus on a discrete model first introduced in Aceves-Sanchez P et al. (2020, Bull. Math. Biol. 82, 125 (doi:10.1007/s11538-020-00805-z)), and derived into a continuum PDE model. As a first major novelty, we perform an in-depth investigation of pattern formation of the discrete and continuum models in two dimensions: we provide phase-diagrams and determine the key mechanisms for bifurcations to happen using linear stability analysis. As a result, we discover that the agent-agent repulsion, the agent-obstacle repulsion and the obstacle’s spring stiffness are the key forces in the appearance of patterns, while alignment forces between the particles play a secondary role. The second major novelty lies in the development of an innovative methodology to compare discrete and continuum models that we apply here to perform an in-depth analysis of the agreement between the discrete and continuum models. |
format | Online Article Text |
id | pubmed-9748504 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-97485042022-12-16 How environment affects active particle swarms: a case study Degond, Pierre Manhart, Angelika Merino-Aceituno, Sara Peurichard, Diane Sala, Lorenzo R Soc Open Sci Mathematics We investigate the collective motion of self-propelled agents in an environment filled with obstacles that are tethered to fixed positions via springs. The active particles are able to modify the environment by moving the obstacles through repulsion forces. This creates feedback interactions between the particles and the obstacles from which a breadth of patterns emerges (trails, band, clusters, honey-comb structures, etc.). We will focus on a discrete model first introduced in Aceves-Sanchez P et al. (2020, Bull. Math. Biol. 82, 125 (doi:10.1007/s11538-020-00805-z)), and derived into a continuum PDE model. As a first major novelty, we perform an in-depth investigation of pattern formation of the discrete and continuum models in two dimensions: we provide phase-diagrams and determine the key mechanisms for bifurcations to happen using linear stability analysis. As a result, we discover that the agent-agent repulsion, the agent-obstacle repulsion and the obstacle’s spring stiffness are the key forces in the appearance of patterns, while alignment forces between the particles play a secondary role. The second major novelty lies in the development of an innovative methodology to compare discrete and continuum models that we apply here to perform an in-depth analysis of the agreement between the discrete and continuum models. The Royal Society 2022-12-14 /pmc/articles/PMC9748504/ /pubmed/36533200 http://dx.doi.org/10.1098/rsos.220791 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Mathematics Degond, Pierre Manhart, Angelika Merino-Aceituno, Sara Peurichard, Diane Sala, Lorenzo How environment affects active particle swarms: a case study |
title | How environment affects active particle swarms: a case study |
title_full | How environment affects active particle swarms: a case study |
title_fullStr | How environment affects active particle swarms: a case study |
title_full_unstemmed | How environment affects active particle swarms: a case study |
title_short | How environment affects active particle swarms: a case study |
title_sort | how environment affects active particle swarms: a case study |
topic | Mathematics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9748504/ https://www.ncbi.nlm.nih.gov/pubmed/36533200 http://dx.doi.org/10.1098/rsos.220791 |
work_keys_str_mv | AT degondpierre howenvironmentaffectsactiveparticleswarmsacasestudy AT manhartangelika howenvironmentaffectsactiveparticleswarmsacasestudy AT merinoaceitunosara howenvironmentaffectsactiveparticleswarmsacasestudy AT peuricharddiane howenvironmentaffectsactiveparticleswarmsacasestudy AT salalorenzo howenvironmentaffectsactiveparticleswarmsacasestudy |