A hybrid smoothed dissipative particle dynamics (SDPD) spatial stochastic simulation algorithm (sSSA) for advection–diffusion–reaction problems

We have developed a new algorithm which merges discrete stochastic simulation, using the spatial stochastic simulation algorithm (sSSA), with the particle based fluid dynamics simulation framework of smoothed dissipative particle dynamics (SDPD). This hybrid algorithm enables discrete stochastic sim...

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Vydáno v:	Journal of computational physics Ročník 378; s. 1 - 17
Hlavní autoři:	Drawert, Brian, Jacob, Bruno, Li, Zhen, Yi, Tau-Mu, Petzold, Linda
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Inc 01.02.2019 Elsevier Science Ltd
Témata:	Algorithms Computational fluid dynamics Computational physics Computer simulation Diffusion Discrete stochastic simulation Finite element method Fluid dynamics Fluid flow Fluid mechanics Meshless methods Microfluidics Organic chemistry Particle based fluid dynamics Reaction-diffusion equations Reaction–diffusion master equation Smooth particle hydrodynamics Yeast Discrete stochastic simulation Reaction–diffusion master equation Particle based fluid dynamics Particle Based Fluid Dynamics Discrete Stochastic Simulation Reaction-Diffusion Master Equation
ISSN:	0021-9991, 1090-2716
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Abstract	We have developed a new algorithm which merges discrete stochastic simulation, using the spatial stochastic simulation algorithm (sSSA), with the particle based fluid dynamics simulation framework of smoothed dissipative particle dynamics (SDPD). This hybrid algorithm enables discrete stochastic simulation of spatially resolved chemically reacting systems on a mesh-free dynamic domain with a Lagrangian frame of reference. SDPD combines two popular mesoscopic techniques: smoothed particle hydrodynamics and dissipative particle dynamics (DPD), linking the macroscopic and mesoscopic hydrodynamics effects of these two methods. We have implemented discrete stochastic simulation using the reaction–diffusion master equations (RDME) formalism, and deterministic reaction–diffusion equations based on the SDPD method. We validate the new method by comparing our results to four canonical models, and demonstrate the versatility of our method by simulating a flow containing a chemical gradient past a yeast cell in a microfluidics chamber. •A hybrid spatial SSA and SDPD method is proposed.•It allows the simulation of stochastic advection–reaction–diffusion problems in a Lagrangian description.•Successful validation of the new method in several benchmark problems.•Method is tested on a problem where commercial CAE software and typical deterministic methods cannot capture all dynamics.
AbstractList	We have developed a new algorithm which merges discrete stochastic simulation, using the spatial stochastic simulation algorithm (sSSA), with the particle based fluid dynamics simulation framework of smoothed dissipative particle dynamics (SDPD). This hybrid algorithm enables discrete stochastic simulation of spatially resolved chemically reacting systems on a mesh-free dynamic domain with a Lagrangian frame of reference. SDPD combines two popular mesoscopic techniques: smoothed particle hydrodynamics and dissipative particle dynamics (DPD), linking the macroscopic and mesoscopic hydrodynamics effects of these two methods. We have implemented discrete stochastic simulation using the reaction–diffusion master equations (RDME) formalism, and deterministic reaction–diffusion equations based on the SDPD method. We validate the new method by comparing our results to four canonical models, and demonstrate the versatility of our method by simulating a flow containing a chemical gradient past a yeast cell in a microfluidics chamber. •A hybrid spatial SSA and SDPD method is proposed.•It allows the simulation of stochastic advection–reaction–diffusion problems in a Lagrangian description.•Successful validation of the new method in several benchmark problems.•Method is tested on a problem where commercial CAE software and typical deterministic methods cannot capture all dynamics. We have developed a new algorithm which merges discrete stochastic simulation, using the spatial stochastic simulation algorithm (sSSA), with the particle based fluid dynamics simulation framework of smoothed dissipative particle dynamics (SDPD). This hybrid algorithm enables discrete stochastic simulation of spatially resolved chemically reacting systems on a mesh-free dynamic domain with a Lagrangian frame of reference. SDPD combines two popular mesoscopic techniques: smoothed particle hydrodynamics and dissipative particle dynamics (DPD), linking the macroscopic and mesoscopic hydrodynamics effects of these two methods. We have implemented discrete stochastic simulation using the reaction-diffusion master equations (RDME) formalism, and deterministic reaction-diffusion equations based on the SDPD method. We validate the new method by comparing our results to four canonical models, and demonstrate the versatility of our method by simulating a flow containing a chemical gradient past a yeast cell in a microfluidics chamber.We have developed a new algorithm which merges discrete stochastic simulation, using the spatial stochastic simulation algorithm (sSSA), with the particle based fluid dynamics simulation framework of smoothed dissipative particle dynamics (SDPD). This hybrid algorithm enables discrete stochastic simulation of spatially resolved chemically reacting systems on a mesh-free dynamic domain with a Lagrangian frame of reference. SDPD combines two popular mesoscopic techniques: smoothed particle hydrodynamics and dissipative particle dynamics (DPD), linking the macroscopic and mesoscopic hydrodynamics effects of these two methods. We have implemented discrete stochastic simulation using the reaction-diffusion master equations (RDME) formalism, and deterministic reaction-diffusion equations based on the SDPD method. We validate the new method by comparing our results to four canonical models, and demonstrate the versatility of our method by simulating a flow containing a chemical gradient past a yeast cell in a microfluidics chamber. We have developed a new algorithm which merges discrete stochastic simulation, using the spatial stochastic simulation algorithm (sSSA), with the particle based fluid dynamics simulation framework of smoothed dissipative particle dynamics (SDPD). This hybrid algorithm enables discrete stochastic simulation of spatially resolved chemically reacting systems on a mesh-free dynamic domain with a Lagrangian frame of reference. SDPD combines two popular mesoscopic techniques: smoothed particle hydrodynamics and dissipative particle dynamics (DPD), linking the macroscopic and mesoscopic hydrodynamics effects of these two methods. We have implemented discrete stochastic simulation using the reaction-diffusion master equations (RDME) formalism, and deterministic reaction-diffusion equations based on the SDPD method. We validate the new method by comparing our results to four canonical models, and demonstrate the versatility of our method by simulating a flow containing a chemical gradient past a yeast cell in a microfluidics chamber.
Author	Petzold, Linda Yi, Tau-Mu Li, Zhen Drawert, Brian Jacob, Bruno
AuthorAffiliation	a Department of Computer Science, University of North Carolina at Asheville, Asheville, North Carolina, 28804, USA e Department of Computer Science, University of California-Santa Barbara, Santa Barbara, California, 93106, USA d Department of Molecular, Cellular, and Developmental Biology, University of California-Santa Barbara, Santa Barbara, California 93106, USA b Department of Mechanical Engineering, University of California-Santa Barbara, Santa Barbara, California, 93106, USA c Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912, USA
AuthorAffiliation_xml	– name: b Department of Mechanical Engineering, University of California-Santa Barbara, Santa Barbara, California, 93106, USA – name: c Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912, USA – name: a Department of Computer Science, University of North Carolina at Asheville, Asheville, North Carolina, 28804, USA – name: e Department of Computer Science, University of California-Santa Barbara, Santa Barbara, California, 93106, USA – name: d Department of Molecular, Cellular, and Developmental Biology, University of California-Santa Barbara, Santa Barbara, California 93106, USA
Author_xml	– sequence: 1 givenname: Brian orcidid: 0000-0002-0543-8189 surname: Drawert fullname: Drawert, Brian email: bdrawert@unca.edu organization: Department of Computer Science, University of North Carolina at Asheville, Asheville, NC, 28804, USA – sequence: 2 givenname: Bruno surname: Jacob fullname: Jacob, Bruno organization: Department of Mechanical Engineering, University of California-Santa Barbara, Santa Barbara, CA, 93106, USA – sequence: 3 givenname: Zhen orcidid: 0000-0002-0936-6928 surname: Li fullname: Li, Zhen organization: Division of Applied Mathematics, Brown University, Providence, RI 02912, USA – sequence: 4 givenname: Tau-Mu surname: Yi fullname: Yi, Tau-Mu organization: Department of Molecular, Cellular, and Developmental Biology, University of California-Santa Barbara, Santa Barbara, CA 93106, USA – sequence: 5 givenname: Linda surname: Petzold fullname: Petzold, Linda organization: Department of Mechanical Engineering, University of California-Santa Barbara, Santa Barbara, CA, 93106, USA
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CitedBy_id	crossref_primary_10_1016_j_dib_2018_11_103 crossref_primary_10_1016_j_enganabound_2023_03_038 crossref_primary_10_1016_j_chemphys_2023_112011 crossref_primary_10_1088_1742_6596_1391_1_012071 crossref_primary_10_1016_j_mtcomm_2020_101277 crossref_primary_10_33693_2313_223X_2019_6_2_48_52 crossref_primary_10_1016_j_cis_2022_102774
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Keywords	Discrete stochastic simulation Reaction–diffusion master equation Particle based fluid dynamics Particle Based Fluid Dynamics Discrete Stochastic Simulation Reaction-Diffusion Master Equation
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Snippet	We have developed a new algorithm which merges discrete stochastic simulation, using the spatial stochastic simulation algorithm (sSSA), with the particle...
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SubjectTerms	Algorithms Computational fluid dynamics Computational physics Computer simulation Diffusion Discrete stochastic simulation Finite element method Fluid dynamics Fluid flow Fluid mechanics Meshless methods Microfluidics Organic chemistry Particle based fluid dynamics Reaction-diffusion equations Reaction–diffusion master equation Smooth particle hydrodynamics Yeast
Title	A hybrid smoothed dissipative particle dynamics (SDPD) spatial stochastic simulation algorithm (sSSA) for advection–diffusion–reaction problems
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