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Data structure for on-lattice cluster-cluster aggregation model performance optimization
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DOI：10.1016/j.cpc.2013.11.015.png)
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Abstract
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A compounded data structure is developed to optimize the simulation of colloidal aggregation using the on-lattice Cluster-Cluster Aggregation (CCA) model. Brownian motion, collision detection and aggregation as the basic operations in the CCA simulation are illustrated and evaluated based on the compounded data structure, respectively. The critical improvement of our algorithm is in distinguishing any selected clusters consisting particles and ascertaining their neighboring positions efficiently in simulation, which was traditionally performed by the exhaustive search in the whole system. Analytical results show that the new algorithm achieves linear computational complexity in each of the main operations, which is very appealing in performance optimization in using on-lattice CCA simulations. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Data structure
On-lattice simulation
Metropolis sampling
Cluster cluster aggregation
Computational complexity
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