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The toolkit provides the fundamental physics models and particle tracking algorithms that track each particle in space and time.
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Hydrogeologic flow models and particle-tracking were used to simulate the mixture of water of different ages produced by the selected PSWs (detailed by Crandall et al. [2009] and Lindgren et al. [2011]).
Even though the formation of bacterial colonies is a complex process that requires a variety of mechanisms (including flagella, pili, secretion of surfactants, siderophores, and enzymes), both continuum models and particle-based hybrid models can reproduce the fractal-like branched patterns characteristic of mature bacterial colonies.
SM was developed by hybridising desirability model and particle swarm optimization (PSO).
The former needs a particle turbulence model and particle stress model, whereas the latter needs a fluid instantaneous velocity model and interparticle collision model.
Particle growth is incorporated in the model, and particle size distributions are easily obtained.
A stochastic modeling and particle filtering-based algorithm has been proposed by using a state-space model to solve nonlinear and non-Gaussian signals [12].
In areas where not enough real observed data are available, a hydrogeological model and particle tracking method can be used for the monitoring network optimization.
Computational fluid dynamics (CFD) with standard k-ε turbulence model and particle trajectory model is used to simulate the internal flow when fracturing fluid flows across the ball seat.
Moreover, a new hybrid approach based on analytical modeling and Particle Swarm Optimization (PSO) is proposed to achieve improved photoelectric behavior at zero bias that can ensure favorable self-powered MSM-based UV-PD.
Moreover, a new hybrid approach based on analytical modeling and Particle Swarm Optimization (PSO) is proposed to determinate the optimal band-gap profile of the amended CZTS absorber layer to achieve further efficiency enhancement.
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